WO2009010069A1

WO2009010069A1 - Method of determining a class of a load connected to an amplifier output

Info

Publication number: WO2009010069A1
Application number: PCT/DK2008/050177
Authority: WO
Inventors: Esben Skovenborg; Klas Åke DALBJÖRN; Kent Tange
Original assignee: The Tc Group A/S
Priority date: 2007-07-16
Filing date: 2008-07-11
Publication date: 2009-01-22
Also published as: WO2009010056A1

Abstract

The invention relates to a method of determining a class of a load by - providing at least one classifier representing at least one class, - measuring and extracting at least one feature of a measured load while a signal is provided to said load, - determining the class of said measured load statistically on the basis of said at least one feature and said at least one classifier. The invention further relates to a load class determining amplifier comprising an amplifier, a data processing means and an amplifier output connected to a load, said amplifier comprising means for measuring and extracting at least one feature of said load while a signal is provided to said load, and said data processing means comprising means for determining the class of said load statistically on the basis of said at least on feature and at least one classifier. The invention further relates to a method of verifying if a load connected to an amplifier output corresponds to a predefined load, said method comprising the steps of - measuring and extracting at least one feature of a measured load while a signal is provided to said load, - determining statistically on the basis of said at least one feature and at least one classifier representing at leas t one class if said measured load belongs to the class of said predefined load.

Description

METHOD OF DETERMINING A CLASS OF A LOAD CONNECTED TO AN AMPLIFIER OUTPUT.

Field of the invention

The invention relates to a method of determining a class of a load, e.g. a kind of loudspeaker coupled to an audio amplifier output.

Background of the invention

A certain degree of determining a load coupled to an amplifier is well-known. The known methods, however, require cumbersome or interrupting test procedures, and/or only answer general questions such as: Is a load connected? Does it short- circuit? What general type (small/big) of load is connected? These are important and relevant questions, but not necessarily important enough to justify shutting everything down in order to carry out an interrupting test procedure, and not necessarily relevant enough to really enable a significant power and audio optimization of the audio system.

An example of a method answering only general questions and therefore not sufficiently relevant for many purposes, is found in US 2006/0104453 Lee where loudspeakers in a multi-channel system are analyzed with respect to impedance to determine if they are duct- type, and if they are small or large type loudspeakers. A problem of such rule-based and "hard logic" analysis is that the resulting determination of the load is inherently relative primitive and simple as the analyzed load is a non-linear and complex element, basically.

An example of a method involving an interrupting test procedure and therefore unsuitable for many purposes is found in GB 2 390 908 A Visteon Global Technologies, where an automotive audio system is tested to ensure that the loudspeakers have been connected properly. The test involves exciting the loudspeakers with a pre-determined test-signal, and then suddenly break the connection between the amplifier and the loudspeakers. Until the excited loudspeaker cones come to rest, currents are generated in the loudspeaker magnets and these signals, the so-called back-EMF signals, are measured. By comparing the measured time-domain back-EMF signals with corresponding signals from known loudspeakers by means of simple pattern matching techniques, it is determined if all loudspeakers are correctly connected. Apart from the fact that the test-procedure interrupts normal use of the audio system, the load matching suffers from the same disadvantages as mentioned above, i.e. rule-based and "hard logic" analysis.

Problems identified in the prior art are among other things an inflexible and often insufficiently detailed determination of a connected load, and use of interrupting test procedures.

Summary of the invention

The present invention relates to a method of determining a class of a load by

- providing at least one classifier representing at least one class, - measuring and extracting at least one feature EFC of a measured load LS; 2, 6, while a signal is provided to said measured load,

- determining the class CL of said measured load statistically on the basis of said at least one feature and said at least one classifier.

Broadly, the invention relates to a class as a collection of loads that have something in common, e.g. model, band and/or driver of the connected load. The class may thus broadly be established and applied for the purpose of establishing a sort of reference to which a measured load may relate. The class(es) may thus be very broad or non- detailed if the purpose of determining the load is only to discriminate between an LF- driver, and MF-driver and a HF-driver. Likewise, the class(es) may be very narrow or detailed if the purpose is to discriminate between different specific drivers or cabinets of same loudspeaker types. In an embodiment of the invention, different degrees of broad and narrow classes are established among which the class of the measured load can be determined.

One of the significant advantages of the invention is that the determination is established statistically instead of based on a set of rules, thereby availing much more gradual determination when compared to a rule-based determination based on when the measured load falls into one interval of reference measures or not. The different approaches may also according to most references be referred to as pattern recognition, or statistical pattern recognition, and pattern matching. In this framework, patterns classified by pattern recognition are usually groups of measurements or observations, defining points in an appropriate multi-dimensional space, whereas patterns spotted by pattern matching are rigid patterns. Hence, in statistical pattern recognition, statistical tools are used for e.g. modeling the classes, feature extraction, classification, etc. In pattern matching, rules and hard logic are used for comparing two rigid patterns to determine if they are equal.

According to an embodiment of the invention, determination on the basis of a classifier and measured features is preferably performed on the basis of the classifier having the measured features as input.

It should be noted that a statistical method may comprise rule-based decisions or processing, as long as just a part of the processing is based on statistics. In other words, methods that are not pure rule-based, are statistical methods and lead to a statistical determination. Furthermore, it should be noted that a classifier according to the present invention may be a simple measure such as, e.g. a statistical distance measure, or it may comprise complex processing, including pre-processing for preparing statistical data or the measured and extracted features, e.g. a statistical normalization, and post-processing for analyzing and interpreting the results, e.g. a decision layer. Hence, a statistical determination according to the present invention may comprise rule-based processing, e.g. in a decision layer, or as part of the core classifier method itself.

The statistical approach of the present invention further avails modeling of classes applicable for the determination which may otherwise be difficult to establish robustly by a set of rules. In order to establish whether a measured load belongs to or is "close/closest" to a certain class it must be possible to relate the measured load to a representation of one or more classes in the classifier. This representation may be in the forms of a statistical model of the class, e.g. a representation based on relevant parameters which may facilitate an automated determination of the class. The model may be established in numerous different ways within the scope of the invention.

Another advantage of the present invention is that the classes that are represented in the classifier need not be explicitly defined. Due to the statistical nature of the modeling in the classifier, the classes may be implicitly defined during a "training" or "model parameter estimation" phase. This training may be performed only once, based on a pre-selected set of classes. Or, the training may be carried out at any time, when either a new class is to be added to the system, or when an existing class is to be extended or its model be further substantiated.

The classifier may thus be regarded as a representation of one or more classes represented in a multi-dimensional feature space.

A classifier may for example comprise a statistical function based on a set of measured features of reference loads belonging to the same class or classes (non- parametric model) or a classifier may for example comprise a statistical model consisting of parameters optimized or estimated based on measured features of reference loads belonging to the same class or classes (parametric model).

It should be noted that a model may be applied for representation of several classes, e.g. when applying a neural network model as classifier.

A determination may be established in several different ways within the scope of the invention. One way of establishing a determination may thus e.g. be a verification where the input to the process is a request regarding whether the measured load is belonging to a certain class. The response to the request may be established in several different ways but it may simply be a yes or a no to whether the load has been determined to belong to the requested class. A response may also involve that a probability measure is established and returned to the user.

Another way of establishing a determination may e.g. be to establish a number of probability measures related to different modeled classes in order to avail the user to know the most likely load class.

Several other ways of establishing the determination may be applied within the scope of the invention as long as the determination is based on a statistical approach.

It should also be noted that the invention facilitates very complex and user friendly determinations of class(es), in that the amount of information established with the determination and the amount of information or options presented to the user may range from very simple to very complex, according to the needs and interests of the user, and the complexity and extensiveness of the classifiers, possible classes, possible loudspeakers used, the loudspeaker setups tested, etc. Thus, as one of several possible examples within the scope of the invention, a determination may e.g. be established as a verification request where a load is measured and where the result of the determination is a "reply" establishing that a narrow class could not be determined for the specific loudspeaker, but that the measured loudspeaker was determined to fall within a "super-class" of subwoofers. Several other examples of such determinations may be applied within the scope of the invention.

This aspect also involves that the determination of a class of the measured load can lead to several different types of results in different embodiments of the invention.

The determination result may in an embodiment merely be a "soft" determination, e.g. a probability for the measured load belonging to one or more classes, i.e. without any decision layer for considering or interpreting the probability determined. This must then be done by the user or other processing means. In a different embodiment, the determination includes a decision layer, e.g. establishing a result comprising the class to which the measured load most likely belongs, or a verification result as mentioned above. According to the present invention a feature of a measured load may be any feature derivable from measurements, typically electrical measurements, or the measurements themselves. Features include, but are not limited to, voltage, current, corresponding values of voltage and current, impedance, characteristics derived from impedance as function of frequency, e.g. mean value, variance, maximum slope, minimum slope or an expression for 'ripple', etc., possibly divided into several frequency bands, DC resistance, resonance frequency, power consumption, transfer function, temperature, physical properties, impedance change according to applied power, temperature change according to applied power, cone excursion caused by applied power or voltage, difference of response to small signal and large signal stimuli, impulse response to a specific test signal, integrated, averaged or differentiated functions of any of the above features during a time window or in a frequency band, specific electrical or physical reaction to a specific test signal, vibration caused by a specific test signal, etc.

The measured and extracted features may be specifically related to a loudspeaker or other load for which determination of a class is desired, or they may relate to the loudspeaker or other load in combination with an additional load, e.g. a loudspeaker cable. According to an embodiment of the present invention, a class determination may be performed for a part of a combined load, e.g. a loudspeaker being part of a cable and loudspeaker combined load, as the statistical approach of the present invention facilitates determination of class even for loads being influenced by additional loads such as significantly long loudspeaker cables. In an alternative embodiment of the present invention, determination of a class of loudspeaker cable or a class of a combination of a loudspeaker cable and a loudspeaker is facilitated.

Hence, a load according to the present invention may be any load from which features may be measured and extracted as described above, and comprise a combination of loads, e.g. a cable and loudspeaker, or two or more loudspeakers coupled in parallel or series. It is noted that a load according to the present invention relates to more than loudspeakers per se, and may relate to any kind of loudspeakers, headphones, monitor loudspeakers, in-the-ear-monitors, hearing aids, transducers, megaphones, specialized loudspeakers, loudspeaker arrays, loudspeaker cables, channel separation filters, attenuators, subsequent amplifier stages, wireless audio transmitters, converters, powered microphones, probes or sensors or any other kind of loads typically or possibly coupled to amplifier outputs of any kind or any other means of establishing a signal, typically an audio signal.

In a very preferred embodiment of the invention, the measurements are performed with any present utility signal, preferably an audio signal, e.g. live or recorded music, speech, or other program material, etc., instead of requiring specific, predetermined test signals, frequency or amplitude sweeps, etc. In other words, this present embodiment enables the use of a complex, multi-frequency signal for the classification/verification. This very advantageous embodiment thereby enables classification or verification of loads during live use of the amplifier and loads. The resulting benefits are e.g. the possibility of verifying/classifying during e.g. a concert to ensure that nothing is changed or to update reference measurements, the possibility of late verification, e.g. after the concert is started if the time was short during set up, the possibility of using real music for testing and verification which is typically much more pleasant to listen to than e.g. pure tone sweeps, the possibility of performing testing and verification while the performers are practicing, warming up or tuning their instruments, etc. If there is no utility signal present, e.g. during a break or while setting up, almost any recorded music or program material can be used, e.g. from the sound engineer's favorite compact disc or streamed from a mobile phone, depending on the input facilities of the amplifiers. The material used should preferably comprise a broad frequency range for a complete verification and accurate reference measurements, but due to the statistical classification exercised it is actually not entirely necessary for achieving acceptable results in most situations. In short, this embodiment of the present invention is advantageous over known methods in that it does not need predetermined or carefully chosen test signals or annoying sweeps, and it does not require interruption of any ongoing performance or any loads. Moreover, it is possible to measure all loads simultaneously with the present utility signal by providing measurement means on all amplifier channels. Thereby, it is also avoided performing test sweeps with each loudspeaker sequentially.

In a very preferred embodiment of the present invention, the measurements are performed instantaneously or immediately as opposed to a measuring scheme where measuring is repeated with different signal characteristics until a threshold is exceeded or a predefined result is achieved. In other words, the measuring is performed without requiring control over or interruption of the audio signal. The present invention thus facilitates simple and easy, from a user's perspective, classification and verification of loads at any time, including during live performance or direct transmission, with no or only minimal influence on the signal provided to the loudspeakers. In other words: a strictly observing classification method, i.e. non- interrupting and non-controlling, which works at any time as long as almost any signal is present.

According to the present invention, the step of measuring and extracting features is performed while a signal is provided. In other words, the measuring may take place continuously or discretely, once, regularly or all the time, for a shorter or longer period, as long as the signal is provided to the load or at some time or several times during a period in which the signal is provided to the load, but not necessarily the entire period. This is very advantageous over the prior art mentioned above, in that it enables an observing, non-interrupting and non-intrusive classification, i.e. a classification that can e.g. be performed without affecting or interrupting the live audio or program material reproduced by the loudspeakers to be classified.

In a preferred embodiment of the invention, the classes are based on models describing a number of reference loads, instead of individual reference loads themselves. Hence, the classes are more robust and descriptive than if the measured features were simply compared with corresponding features derived from specific reference loads. According to the present invention, statistical methods are used for the classification or verification.

As an example, in an embodiment of the present invention the determination is performed by means of classifiers based on a statistical distance measure D, and the features measured and extracted from the load are mean value and variance in several different, overlapping feature extraction frequency bands Bl, B2, ..., B7 derived from a load impedance function established by a method and/or an amplifier as described in PCT patent application No. PC17DK2007/050099 filed on 16 July 2007, hereby incorporated by reference as regards a method and amplifier for establishing an impedance function of a connected load.

When said method is an observing method, an advantageous embodiment of the present invention is obtained.

By observing method is according to the present invention meant a method that can be carried out with no or almost no impact on the system to which it is applied. In particular, it means within the scope of the present invention, that the load can be classified without interfering with the on-going performance of the audio reproduction system comprising the load, e.g. that a loudspeaker being part of a concert setup can be classified at any time during the concert without noticeable changes in the audio reproduction experienced by the audience or the performer(s). This is highly beneficial compared to previous methods that typically needed to interrupt the show (or, hence, be performed prior to or following the show), or needed a pre-determined test signal to be reproduced by the load in order to classify it.

When said measuring employs a substantially non-intrusive measuring method as regards affecting an interaction between said signal and said measured load, an advantageous embodiment of the present invention is obtained. By non-intrusive measuring method according to the present invention it is meant that a measuring method can be carried out with no or almost no influence on the signal that is measured. In particular, it means within the scope of the present invention, that the measurements from which features are extracted are made on the signal sent to the loudspeaker without changing that signal significantly. This further means that the audio reproduction system can carry on its duties, e.g. reproduction of live or recorded music, even though measurements are performed and classifications are taking place in the background. This is highly beneficial compared to previous methods that typically need to interrupt or control the signal in order to measure characteristics of the load.

When said method is non- interrupting as regards a reproduction of an audio signal by said measured load, an advantageous embodiment of the present invention is obtained.

By non-interrupting method classification method is according to the present invention meant a method that does not interrupt the ongoing and desired working of the loudspeaker that is classified. This is highly beneficial compared to previous methods that typically need to interrupt the show (or, hence, be performed prior to or following the show), in order to classify the load.

When said measuring comprises multi-bit sampling, an advantageous embodiment of the present invention is obtained.

As opposed to known systems where measurements are e.g. made by applying a voltage sweep to an unknown impedance and determining at which voltage a predetermined current is exceeded, multi-bit sampling facilitates measuring e.g. a current instantly regardless of the voltage that created it, and hence without a need to control the voltage. In other words, multi-bit sampling facilitates instantaneous determination of a current and/or voltage without requiring control over or interruption of the signal that is measured, and hence it facilitates non-intrusive and truly observing classification of a load. When said measuring comprises measuring on the basis of a complex audio signal, an advantageous embodiment of the present invention is obtained.

According to a preferred embodiment of the present invention, a complex audio signal, i.e. a multi-frequency signal, e.g. music or speech, is used. This is opposed to and beneficial over known techniques that require pre-determined test signals with controlled frequency content, e.g. pure tones, frequency sweeps, white or pink noise, etc. By enabling the use of complex audio signals for classification it is also enabled to use the classification method with any available or forced audio input, e.g. music from a compact disc or MP3 -player, live vocal or musical instrument audio or a mix thereof during e.g. a concert, etc.

When at least one of said at least one feature (EFC) comprises a feature derived from an impedance function of said measured load, an advantageous embodiment of the present invention is obtained.

Impedance functions associated with loads, e.g. loudspeakers, are typically quite unique according to the type of load, the model and even the individual instances of that model. Features derived from an impedance function may therefore advantageously be used to classify loads accurately.

When said measuring comprises measuring at least one of a voltage and a current of said signal and at least one of said at least one feature (EFC) is derived from an impedance of said load, determined on the basis of a measured or estimated voltage and a measured or estimated current of said signal, an advantageous embodiment of the present invention is obtained.

In a preferred embodiment of the present invention, features are extracted on the basis of an impedance function of the load, established by measuring or estimating corresponding values of voltage and current of the signal provided to the load. In a preferred embodiment, the voltage is estimated from the audio signal processed by an amplifier driving the load, and the current through the loudspeaker is measured, e.g. by means of a small reference resistor and a multi-bit A/D-converter. The impedance as function of frequency can then be determined by a Fourier transform of the voltage and current signals and determining the frequency dependent impedance. Several methods for determining a possibly complex, frequency dependent impedance from voltage and current in either time-domain or frequency-domain are known and within the scope of the present invention.

When a result of said measuring is a frequency dependent result, an advantageous embodiment of the present invention is obtained.

As most applicable loads, e.g. loudspeakers, etc., have frequency dependent characteristics, e.g. frequency dependent impedances, it increases the load classification accuracy and/or usability significantly when taking the frequency dimension into consideration.

When said measured load LS; 2, 6 is connected to an amplifier 1, an advantageous embodiment of the present invention is obtained.

An amplifier may typically comprise one or several amplifier outputs to which a load, typically a loudspeaker may be coupled. The invention moreover allows individual determination performed in relation to each amplifier output and the associated load and it may also facilitate analysis of one or more groups of the amplifier outputs.

The establishment of such a determination will be described more in detail below as well as a description of suitable features which may serve basis for a statistical determination, i.e. based on probabilities. When allowing determination based on probability measures more complex and detailed determination may be established than by a straightforward, purely rule-based match/ no-match analysis as the loads typically are fundamentally non-linear and therefore unsuitable for straightforward linear deduction, e.g. a determination where different parameters are considered sequentially. Such non-linear behavior may e.g. rely on thermal conditions, aging, stress, production tolerance, etc. In other words, determination according to the present invention facilitates recognition even of loads which may look very much the same when performing primitive rule -based recognition, e.g. peak detection in impedance characteristics.

It is noted that a variant of determination of loads also within the scope of the present invention is verification that loads correspond to predefined or expected loads. Where determination of a class of a load in general comprises a set of possible classes containing several possible loads and the special class unknown, the variant of verification that a load corresponds to a certain expected load can be seen as a determination of a class of a load where the set of possible classes contains only a single class and the special class unknown, which in that case means not verified. An alternative way of deriving a simple verification from a general determination result is to compare the determined class with the expected class. This is a logical task leading to a binary result as verifications inherently do. Any degree of class determination and any result derivable from it, with any degree of information detail, and any variants such as verification, are within the scope of the present invention.

When the method is applied within an amplifier 1 and wherein said measured load LS; 2, 6 is connected electrically to said amplifier 1, an advantageous embodiment of the present invention is obtained.

The load may preferably be connected to the amplifier by conventional cabling.

When the amplifier executes the method by means of data processing means 3 according to instructions stored in memory means, an advantageous embodiment of the present invention is obtained.

The method is preferably computer implemented, i.e. it is preferably implemented by data processing means implemented in a personal computer, a laptop, a digital signal processor DSP, a microcontroller or microprocessor, a field programmable gate array FPGA, an application specific integrated circuits ASIC, etc.

When the amplifier comprises a data port by means of which models may be transferred to or from the amplifier, an advantageous embodiment of the present invention is obtained.

An applicable data port may e.g. comprise a network port, e.g. an Ethernet network port, USB port, Firewire port, Infrared port, Blutooth port, etc.

When said classifier or data related with said classifier is stored in a data storage 5, an advantageous embodiment of the present invention is obtained.

When said amplifier 1 comprises said data storage 5, an advantageous embodiment of the present invention is obtained.

When said data storage 5 is comprised by a central data storage to which said amplifier 1 has irregular or continuous access, an advantageous embodiment of the present invention is obtained.

According to an embodiment of the invention, the amplifiers may be coupled in a data network, typically with a central controller or monitoring means. In such a network, the data storage containing the classifiers and other relevant data may as well be comprised by a central network unit, e.g. as part of the central controller or monitoring means, in order to ensure that all amplifiers have access to the same information. In a further embodiment, the amplifiers may have access to an extended network, e.g. the Internet, e.g. for participation in a community for classifier distribution or for remote access to classifiers. The network connection, e.g. in the case of access to the Internet, need not be continuously established but may be established on demand or according to the needs. When said method comprises a decision layer, an advantageous embodiment of the present invention is obtained.

In a preferred embodiment of the present invention, a decision layer is provided to facilitate the user in interpreting and considering the results of the determination. The decision layer may, e.g., consider established probabilities and decide the most probable option, or it may, e.g., convey to the user that all loads are correct except from a few specified loads which are problematic to determine. As noted above, the decision layer can be rule-based or statistical, and it can form part of a classifier or be a separate method step carried out after the statistical processing, and possibly only on request from the user.

When said at least one classifier comprises a parametric classifier, an advantageous embodiment of the present invention is obtained.

When said at least one classifier comprises a non-parametric classifier, an advantageous embodiment of the present invention is obtained.

When said at least one class is established by providing a set of reference features RFC of a plurality of reference loudspeaker units RLS, an advantageous embodiment of the present invention is obtained.

When at least one of said reference features RFC is provided as a function of frequency, an advantageous embodiment of the present invention is obtained.

When at least one of said features EFC is determined as a function of frequency, an advantageous embodiment of the present invention is obtained.

According to a preferred embodiment, the features are considered with regard to frequency dependency, as the extra dimension of frequency in most cases widens the data set significantly and causes otherwise similar looking data sets to reveal significant differences. When said determination of a class of said load LS; 2, 6 is based on a probability of match between said measured and extracted features EFC of said measured load and one or several reference features RFC, an advantageous embodiment of the present invention is obtained.

When said determination of a class of said load LS; 2, 6 involves a statistical determination, an advantageous embodiment of the present invention is obtained.

When said determination of a class of said load LS; 2,6 involves a non-linear signal processing, an advantageous embodiment of the present invention is obtained.

By non-linear signal processing is facilitated more complex analysis and non-linear determination than facilitated by only linear processing means.

When the determination is performed automatically, an advantageous embodiment of the present invention is obtained.

When the determination is performed semi-automatically, an advantageous embodiment of the present invention is obtained.

When the determination is performed during a calibration phase, an advantageous embodiment of the present invention is obtained.

A calibration phase is regarded as the initial phase when a load has been coupled to an amplifier and where the initial tests are performed.

When the determination is performed during a verification phase, an advantageous embodiment of the present invention is obtained.

According to a preferred embodiment the method of determination is performed during a verification phase where it is verified if the connected load for each channel in the system is of the make and model that the user has predefined in his system configuration, i.e. that the class of each connected load corresponds to the predefined class. This is a convenient way to detect that everything has been connected as intended in a large system (often containing several hundred amplifier channels).

When said determination of a class of said load LS; 2,6 involves indication of one or more of the make, model, band, driver and/or number of parallel coupled loudspeakers of the measured load, an advantageous embodiment of the present invention is obtained.

The resulting determination of class may be more or less detailed depending on the intended use of the result and also depending on how many different loads which are actually applicable.

An example of a make and model indication is, e.g., JBL Vertec VT 4889. An example of a detailed make, model and band indication for a channel is, e.g., the MF band of a JBL Vertec VT4889, as this 3-way loudspeaker has separate inputs for each band. Such detailed information can be used for looking up further characteristics in the model's data sheet, preferably digitalized data sheets for automatic use by the amplifier.

When said determination of a class of said load LS; 2,6 involves indication of the type of the measured load, an advantageous embodiment of the present invention is obtained.

An example of a type indication is, e.g., that the connected load belongs to the class of one or several woofers in bass reflex type cabinet(s). Such information can be used for looking up generic characteristics for that type of loudspeaker, e.g. for further use by the amplifier.

When said determination of a class of said load LS; 2,6 involves indication of possible classes to a user and where the possible classes are associated with indication of calculated probabilities, an advantageous embodiment of the present invention is obtained.

By listing of calculated probabilities a user, e.g. a sound engineer, may manually complete the determination on the basis of knowledge of what loads the user expected to be connected to the amplifier. Optional or probable determinations may be displayed to a user and the user may then use his knowledge and expectations of the system to decide the established class or load.

When said determination of a class of said load LS; 2,6 involves indication of the load being a predefined load, or the probability of the load being a predefined load, an advantageous embodiment of the present invention is obtained.

According to this embodiment, the user, e.g. a sound engineer, makes the intended system configuration, i.e. which loudspeaker is intended on which channel, available to the system, and the determination method involves a decision layer verifying this configuration for each channel, or establishing probabilities of correct connections for each channel.

When said features EFC are extracted in at least two separate or overlapping feature extraction bands Bl, B2, ..., B7, an advantageous embodiment of the present invention is obtained.

According to an advantageous embodiment of the invention feature extraction may be performed in different bands with respect to frequency in order to establish a number of features sufficient to allow distinguishing between loads which are behaving relatively equal.

When said features EFC are extracted in at least three overlapping feature extraction bands Bl, B2, ..., B7, an advantageous embodiment of the present invention is obtained. According to an advantageous embodiment of the invention feature extraction may be performed in different overlapping bands with respect to frequency in order to establish a number of features sufficient to allow distinguishing between loads which are performing relatively equal.

When said features EFC are extracted in at least seven overlapping feature extraction bands Bl, B2, ..., B7, an advantageous embodiment of the present invention is obtained.

According to a preferred embodiment of the invention, the features are extracted in relatively many bands with respect to frequency in order to improve the robustness of the class determination.

When the determination of a class of said measured load is applied for deriving of information associated with said class CL, an advantageous embodiment of the present invention is obtained.

According to a preferred embodiment of the invention, determination of the coupled load may be applied for the purpose of establishing information which is associated with the determined class and application of this information in connection with the normal use of the amplifier when coupled to the load. Thus, such information may facilitate a live monitoring of the temperature of the voice coil, determination of aging, etc.

Moreover such normal use may e.g. include automatic or at least semi-automatic determination of short-circuits, defect cables, incorrectly connected loads, etc.

When said at least one feature EFC comprises at least one electrical measurement or is derived from at least one electrical measurement, an advantageous embodiment of the present invention is obtained. When said at least one feature EFC comprises impedance of the load as function of frequency, variance and/or mean value of the impedance of the load, resonance frequency of the load, DC resistance of the load, etc., an advantageous embodiment of the present invention is obtained.

It should be noted that e.g. mean impedance in two different feature extraction bands Bl, B2, ..., B7 - overlapping or not - may be regarded and applied separately as two different features. Hence, e.g. 14 individual features are determined by determining 2 features in 7 feature extraction bands.

When said measured and extracted features are compensated for a cable component before said determination of class is performed on the basis of said features, an advantageous embodiment of the present invention is obtained.

According to a preferred embodiment of the present invention, information about the impedance or other significant features of a loudspeaker cable or other cable is established in order to be able to neglect or subtract this information during the determination of class of the load or other use of the features measured and extracted from the load.

When at least two features EFC are established and analyzed in different feature extraction bands Bl, B2, ..., B7, overlapping or non-overlapping, an advantageous embodiment of the present invention is obtained.

The present invention further relates to a load class determining amplifier, comprising an amplifier 1, a data processing means 3 and an amplifier output AO; 4 connected to a load LS; 2, 6, said amplifier 1 comprising means for measuring and extracting at least one feature EFC of said load while a signal is provided to said load, and said data processing means 3 comprising means for determining the class CL of said load statistically on the basis of said at least one feature and at least one classifier. According to the present invention, an amplifier is provided which can determine the class of a connected load statistically while a signal is provided to the load. This is advantageous compared to known devices where the signal provided to the load has to be disconnected or muted before measurements are performed, and to known devices where the type of load is determined by rule-based logic.

According to the present invention, the measuring and extracting features is performed while a signal is provided to the load. In other words, the measuring may take place continuously or discretely, once, regularly or all the time, for a shorter or longer period, as long as the signal is provided to the load or at some time or several times during a period in which the signal is provided to the load, but not necessarily the entire period. This is very advantageous over the prior art mentioned above, in that it enables an observing, non-interrupting and non-intrusive classification, i.e. a classification that can e.g. be performed without affecting or interrupting the live audio or program material reproduced by the loudspeakers to be classified.

When said amplifier comprises an amplifier input AI provided with a complex audio signal, and said means for measuring comprises means for measuring characteristics of a signal provided to said load, said signal being derived from said complex audio signal, an advantageous embodiment of the present invention is obtained.

When said means for measuring comprises a multi-bit analog-to-digital converter, an advantageous embodiment of the present invention is obtained.

When said means for measuring employs a non-interrupting measurement method as regards the reproduction of an audio signal by said load, an advantageous embodiment of the present invention is obtained.

When said means for measuring employs multi-bit sampling, an advantageous embodiment of the present invention is obtained. When at least one of said at least one feature EFC comprises a feature derived from an impedance function of said load, an advantageous embodiment of the present invention is obtained.

When said load class determining amplifier comprises means for carrying out a method of determining a class of a load according to any of the above, an advantageous embodiment of the present invention is obtained.

The present invention further relates to a method of verifying if a load LS; 2, 6 connected to an amplifier output AO; 4 corresponds to a predefined load, said method comprising the steps of

- measuring and extracting at least one feature EFC of a measured load LS; 2, 6 while a signal is provided to said measured load,

- determining statistically on the basis of said at least one feature EFC and at least one classifier representing at least one class if said measured load belongs to the class of said predefined load.

According to the present invention, a verification method is provided where the input to the process is a request regarding whether the measured load is belonging to a certain class. The response to the request may be established in several different ways but it may simply be a yes or a no to whether the load has been determined to belong to the requested class. A response may also involve that a probability measure is established and returned to the user.

Thereby is also provided a very advantageous way of conveniently verifying that e.g. a large concert loudspeaker setup is cabled correctly according to the plan and all loudspeakers working properly.

According to the present invention a feature of a load may be any feature derivable from measurements, typically electrical measurements, or the measurements themselves. Features include, but are not limited to, voltage, current, corresponding values of voltage and current, impedance, characteristics derived from impedance as function of frequency, e.g. mean value, variance, maximum slope, minimum slope or an expression for 'ripple', etc., possibly divided into several frequency bands, DC resistance, resonance frequency, power consumption, transfer function, temperature, physical properties, impedance change according to applied power, temperature change according to applied power, cone excursion caused by applied power or voltage, difference of response to small signal and large signal stimuli, impulse response to a specific test signal, integrated, averaged or differentiated functions of any of the above features during a time window or in a frequency band, specific electrical or physical reaction to a specific test signal, vibration caused by a specific test signal, etc.

The measured and extracted features may be specifically related to a loudspeaker or other load for which verification is desired, or they may relate to the loudspeaker or other load in combination with an additional load, e.g. a loudspeaker cable. According to an embodiment of the present invention, a class verification may be performed for a part of a combined load, e.g. a loudspeaker being part of a cable and loudspeaker combined load, as the statistical approach of the present invention facilitates verification of class even for loads being influenced by additional loads such as significantly long loudspeaker cables. In an alternative embodiment of the present invention, verification of a class of loudspeaker cable or a class of a combination of a loudspeaker cable and a loudspeaker is facilitated.

In a very preferred embodiment of the invention, the measurements are performed with any present utility signal, preferably an audio signal, e.g. live or recorded music, speech, or other program material, etc., instead of requiring specific, predetermined test signals, frequency or amplitude sweeps, etc. In other words, this present embodiment enables using a complex, multi-frequency signal for the classification/verification. This very advantageous embodiment thereby enables classification or verification of loads during live use of the amplifier and loads. The resulting benefits are e.g. the possibility of verifying/classifying during e.g. a concert to ensure that nothing is changed or to update reference measurements, the possibility of late verification, e.g. after the concert is started if the time was short during set up, the possibility of using real music for testing and verification which is typically much more pleasant to listen to than e.g. pure tone sweeps, the possibility of performing testing and verification while the performers are practicing, warming up or tuning their instruments, etc. If there is no utility signal present, e.g. during a break or while setting up, almost any recorded music or program material can be used, e.g. from the sound engineer's favorite compact disc or streamed from a mobile phone, depending on the input facilities of the amplifiers. The material used should preferably comprise a broad frequency range for a complete verification and accurate reference measurements, but due to the statistical classification exercised it is actually not entirely necessary for achieving acceptable results in most situations. In short, this embodiment of the present invention is advantageous over known methods in that it does not need predetermined or carefully chosen test signals or annoying sweeps, and it does not require interruption of any ongoing performance or any loads. Moreover, it is possible to measure all loads simultaneously with the present utility signal by providing measurement means on all amplifier channels. Thereby is also avoided performing test sweeps with each loudspeaker sequentially.

In a very preferred embodiment of the present invention, the measurements are performed instantaneously or immediately as opposed to a measuring scheme where measuring is repeated with different signal characteristics until a threshold is exceeded or a predefined result is achieved. In other words, the measuring is performed without requiring control over or interruption of the audio signal. The present invention thus facilitates simple and easy, from a user's perspective, classification and verification of loads at any time, including during live performance or direct transmission, with no or only extremely small influence on the signal provided to the loudspeakers. In other words: a strictly observing classification method, i.e. non-interrupting and non-controlling, which work at any time as long as almost any signal is present.

According to the present invention, the step of measuring and extracting features is performed while a signal is provided. In other words, the measuring may take place continuously or discretely, once, regularly or all the time, for a shorter or longer period, as long as the signal is provided to the load or at some time or several times during a period in which the signal is provided to the load, but not necessarily the entire period. This is very advantageous over the prior art mentioned above, in that it enables an observing, non-interrupting and non-intrusive verification, i.e. a verification that can e.g. be performed without affecting or interrupting the live audio or program material reproduced by the loudspeakers to be verified.

As an example, in an embodiment of the present invention the verification is performed by means of classifiers based on a statistical distance measure D, and the features measured and extracted from the load are mean value and variance in several different, overlapping feature extraction frequency bands Bl, B2, ..., B7 derived from a load impedance function established by a method and/or an amplifier as described in PCT patent application No. PCT/DK2007/050099 filed on 16 July 2007, hereby incorporated by reference as regards a method and amplifier for establishing an impedance function of a connected load.

When said method is an observing method, an advantageous embodiment of the present invention is obtained. By observing method is according to the present invention meant a method that can be carried out with no or almost no impact on the system to which it is applied. In particular, it means within the scope of the present invention, that the load can be classified without interfering with the on-going performance of the audio reproduction system comprising the load, e.g. that a loudspeaker being part of a concert setup can be verified at any time during the concert without noticeable changes in the audio reproduction experienced by the audience or the performer(s). This is highly beneficial compared to previous methods that typically needed to interrupt the show (or, hence, be performed prior to or following the show), or needed a pre-determined test signal to be reproduced by the load in order to verify it.

When said measuring employs a substantially non-intrusive measuring method as regards affecting an interaction between said signal and said measured load, an advantageous embodiment of the present invention is obtained.

By non-intrusive measuring method is according to the present invention meant a measuring method that can be carried out with no or almost no influence on the signal that is measured. In particular, it means within the scope of the present invention, that the measurements from which features are extracted are made on the signal sent to the loudspeaker without changing that signal significantly. This further means that the audio reproduction system can carry on its duties, e.g. reproduction of live or recorded music, even though measurements are performed and verification taking place in the background. This is highly beneficial compared to previous methods that typically needed to interrupt or control the signal in order to measure characteristics of the load.

By non-interrupting verification method is according to the present invention meant a method that does not interrupt the ongoing and desired working of the loudspeaker that is verified. This is highly beneficial compared to previous methods that typically needed to interrupt the show (or, hence, be performed prior to or following the show), in order to verify the load.

As opposed to known systems where measurements are e.g. made by applying a voltage sweep to an unknown impedance and determining at which voltage a pre- determined current is exceeded, multi-bit sampling facilitates measuring e.g. a current instantly regardless of the voltage that created it, and hence without the need to control the voltage. In other words, multi-bit sampling facilitates instantaneous determination of a current and/or voltage without requiring control over or interruption of the signal that is measured, and hence it facilitates non-intrusive and truly observing verification of a load.

When said measuring comprises measuring on the basis of a complex audio signal, an advantageous embodiment of the present invention is obtained.

According to a preferred embodiment of the present invention, a complex audio signal, i.e. a multi-frequency signal, e.g. music or speech, is used. This is opposed to and beneficial over known techniques that require pre-determined test signals with controlled frequency content, e.g. pure tones, frequency sweeps, white or pink noise, etc. By enabling the use of complex audio signals for verification is also enabled using the verification method with any available or compulsory audio input, e.g. music from a compact disc or MP3-player, live vocal or musical instrument audio or a mix thereof during e.g. a concert, etc.

When at least one of said at least one feature EFC comprises a feature derived from an impedance function of said measured load, an advantageous embodiment of the present invention is obtained. Impedance functions associated with loads, e.g. loudspeakers, are typically quite unique according to the type of load, the model and even the individual instances of that model. Features derived from an impedance function may therefore advantageously be used to verify loads among a large field of often quite similar- looking reference loads.

When said measuring comprises measuring at least one of a voltage and a current of said signal, and at least one of said at least one feature (EFC) is derived from an impedance of said load determined on the basis of a measured or estimated voltage and a measured or estimated current of said signal, an advantageous embodiment of the present invention is obtained.

In a preferred embodiment of the present invention, features are extracted on the basis of an impedance function of the load, established by measuring or estimating corresponding values of voltage and current of the signal provided to the load. In a preferred embodiment, the voltage is estimated from the audio signal processed by an amplifier driving the load, and the current through the loudspeaker is measured, e.g. by means of a small reference resistor and a multi-bit A/D-converter. The impedance as function of frequency can then be determined by Fourier transforming the voltage and current signals and determining the frequency dependent impedance. Several methods for determining a possibly complex, frequency dependent impedance from voltage and current in either time-domain or frequency-domain are known and within the scope of the present invention.

As most applicable loads, e.g. loudspeakers, etc., have frequency dependent characteristics, e.g. frequency dependent impedances, it increases the load verification possibility and/or usability significantly when taking the frequency dimension into consideration. When said measured load LS; 2, 6 is connected to an amplifier 1, an advantageous embodiment of the present invention is obtained.

An amplifier may typically comprise one or several amplifier outputs to which a load, typically a loudspeaker may be coupled. The invention moreover allows individual verification performed in relation to each amplifier output and the associated load and it may also facilitate analysis of one or more groups of the amplifier outputs.

A variant of determination of loads within the scope of the present invention is verification that loads correspond to predefined or expected loads. Where determination of a class of a load in general comprises a set of possible classes containing several possible loads and the special class unknown, the variant of verification that a load corresponds to a certain expected load can be seen as a determination of a class of a load where the set of possible classes contains only a single class and the special class unknown, which in that case means not verified. An alternative way of deriving a simple verification from a general determination result is to compare the determined class with the expected class. This is a logical task leading to a binary result as verifications inherently do. Any degree of class determination and any result derivable from it, with any degree of information detail, and any variants such as verification, are within the scope of the present invention.

The load may preferably be connected to the amplifier by conventional cabling.

When said determining statistically on the basis of said at least one feature EFC and at least one classifier representing at least one class if said measured load belongs to the class of said predefined load, comprises a method of determining a class of a load as described above, an advantageous embodiment of the present invention is obtained.

The present invention further relates to a load verification amplifier comprising an amplifier 1, a data processing means 3 and an amplifier output AO; 4 connected to a load LS; 2, 6, said amplifier 1 comprising means for measuring and extracting at least one feature EFC of said load while a signal is provided to said load, and said data processing means 3 comprising means for determining statistically on the basis of said at least one feature EFC and at least one classifier representing at least one class if said load belongs to the class of said predefined load.

According to the present invention, an amplifier is provided which can verify if a connected load belongs to the class of a predefined load statistically while a signal is provided to the load. This is advantageous compared to known devices where the signal provided to the load has to be disconnected or muted before measurements are performed, and to known devices where the type of load is determined by rule-based logic.

According to the present invention, the measuring and extracting features is performed while a signal is provided to the load. In other words, the measuring may take place continuously or discretely, once, regularly or all the time, for a shorter or longer period, as long as the signal is provided to the load or at some time or several times during a period in which the signal is provided to the load, but not necessarily the entire period. This is very advantageous over the prior art mentioned above, in that it enables an observing, non-interrupting and non-intrusive verification, i.e. a verification that can e.g. be performed without affecting or interrupting the live audio or program material reproduced by the loudspeakers to be classified.

When said amplifier comprises an amplifier input AI provided with a complex audio signal, and said means for measuring comprises means for measuring characteristics of a signal provided to said load, said signal being derived from said complex audio signal, an advantageous embodiment of the present invention is obtained. When said means for measuring comprises a multi-bit analog-to-digital converter, an advantageous embodiment of the present invention is obtained.

When said means for measuring employs multi-bit sampling, an advantageous embodiment of the present invention is obtained.

When at least one of said at least one feature EFC comprises a feature derived from an impedance function of said load, an advantageous embodiment of the present invention is obtained.

When said load verification amplifier comprises means for carrying out a method of verifying if a load corresponds to a predefined load according to any of the above, an advantageous embodiment of the present invention is obtained.

The present invention further relates to a system comprising an amplifier 1 according to any of the above, and at least one load 2, 6.

The present invention further relates to a use of a method according to any of the above.

The present invention further relates to a method of determining a class of a load by

- providing at least one classifier representing at least one class,

- measuring and extracting at least one feature EFC of a measured load LS; 2, 6,

- determining the class CL of said measured load statistically on the basis of said at least one feature and said at least one classifier. The present invention further relates to a method of distributing classifiers representing load classes or data related to said classifiers, the method comprising providing a central data storage CD comprising a data port 130 and enabling at least two amplifiers 1 or users of amplifiers 1 to exchange said classifiers or said data with said central data storage CD.

The present invention further relates to a community for distribution of classifiers representing load classes or data related to said classifiers, said community comprising at least two amplifiers 1 or users of amplifiers 1 and a central data storage CD, said amplifiers 1 and central data storage CD comprising data ports 130 for facilitating exchange of said classifiers or said data.

The determination of a class of a load according to the present invention relies on the availability of classifiers or data from which classifiers can be established, i.e. data from preferably several reference loads belonging to the same class. As mentioned above, one way to establish a classifier is from measurements, e.g. impedance function measurements, for a number of loudspeakers belonging to the same class. Such measurements and classifier establishment can evidently be performed by the loudspeaker manufacturers. It may, however, be difficult to get the loudspeaker manufacturers to establish classifiers for their loudspeakers, including the loudspeakers already on the market and possibly discontinued, and it may therefore be insufficient to rely on the loudspeaker manufacturers to establish a huge classifier database for use in the amplifiers. Moreover, it may be beneficial to establish classes or classifiers representing super-classes, alternative loads, e.g. loudspeaker cables, loudspeakers damaged or worn in distinctive ways, both as sub-classes of type- classes, e.g. class of tweeters with a bulged cone, and as sub-classes of narrow model-classes, e.g. that specific tweeter model with a bulged cone, etc. Hence, even though the loudspeaker manufacturers may establish classifiers for their loudspeakers, it may be advantageous to facilitate a classifier establishment and distribution not limited to the loudspeaker manufacturers. Moreover, a means for distributing new classifiers should be established in order for users to be able to keep their amplifier load databases up to date when acquiring new loudspeakers. According to the present invention, an advantageous embodiment is to download classifiers to the amplifiers 1 from other amplifiers 1 or from the central data storage CD, or from a classifier provider CP. Thereby is facilitated updating the local loudspeaker profiles represented by classifiers, e.g. as new loudspeakers are acquired.

According to the present invention, an advantageous embodiment is to facilitate the amplifiers 1 to upload measured impedance functions or other data they have determined. As the classifiers get more robust and certain with larger data sets, and as the amplifiers are able to measure an impedance function or other data necessary to classify a load, in fact all the amplifiers are able to cooperate in establishing data sets for the classifiers. In other words, each time a load is determined by an amplifier, and possibly verified by a user, the measured data can be uploaded to, e.g., the central data storage or a classifier provider. The data may be accompanied by related, measured or manually input data, e.g. regarding temperature, connection, etc. Also new classifiers or data related to loads not yet represented by a classifier may be uploaded. In that case the user should preferably add data about the desired or suggested class, or any other relevant data such as, e.g., degree of wearing, any damages, type of use, etc. In the case that classifiers are provided to other community users on the basis of few measurements e.g. from other users, the classifiers may be associated with a robustness score or other indicator representing to what degree the output of the classifier can be trusted.

In a community as described above, the classifiers can be free to download, or download can be subject to a charge. In a preferred embodiment the charge and availability can be decided by the central data storage or provider. In a preferred embodiment a user contributing by uploading data or a classifier receives a compensation in the form of e.g. money, virtual money for use in the community, or the right to download a number of classifiers for free. In a preferred embodiment the classifiers can be discussed, scored, suggested changed, etc., by the community users. In a preferred embodiment the community may further facilitate distribution of amplifier related data other than load classifiers, e.g. amplifier settings, etc.

An advantage of the present invention is that the measurements are performed locally, i.e. not by a central instance, thereby relieving the loudspeaker manufacturers, the amplifier manufacturers or dedicated companies from performing them. Moreover, and even more important and beneficial, the measurements are made in real life situations, by real life amplifiers with real life speakers with natural wear and characteristics. Thereby, the data sets and classifiers established will possibly better handle classification in the actual live situations for the users downloading classifiers established this way.

The establishment of classifiers on the basis of (locally performed) measurements should preferably be performed centrally, e.g. by a central data storage, a classifier provider, the amplifier manufacturer or a dedicated classifier company in order to ensure quality of the established classifiers, an in order to avoid errors and maintain a structured and user friendly classifier hierarchy. In alternative embodiments, a user may be able to establish a classifier by means of several measurements on different loads of same class. In an alternative embodiment, such a locally established classifier may advantageously be used locally by the user establishing it, and may, if shared with the community, be marked as 'homemade'.

If the establishment of classifiers relies on extraction of features, e.g. from measured impedance functions, e.g. for use in a statistical distance classifier, the features may be extracted in the amplifiers before upload of data to the central data storage, or they may be extracted by the central data storage when establishing the classifier. The drawings

The invention will now be described with reference to the drawings of which

fig. IA and IB illustrate an amplifier according to an embodiment of the invention, fig. 2 illustrates a principle of determination according to a preferred embodiment of the invention, fig. 3 illustrates an impedance characteristic for a 3-way loudspeaker, fig. 4 illustrates an amplifier according to an embodiment of the invention, fig. 5 illustrates an impedance characteristic for 1 to 4 loudspeakers coupled in parallel to one amplifier output, fig. 6 illustrates a block diagram of the different steps in a process according to an embodiment of the invention of classifying or verifying a loudspeaker coupled to an amplifier output, fig. 7 illustrates a principle of overlapping feature extraction bands according to an embodiment of the present invention, fig. 8 illustrates an example of a data set for a statistical distance classifier, fig. 9 and 10 illustrate specific examples using a method according to embodiments of the invention, fig. HA and HB illustrate flow diagrams of automatic classification methods according to embodiments of the present invention, fig. 12A and 12B illustrate flow diagrams of automatic verification methods according to the present invention, fig. 13A and 13B illustrate embodiments of classifier distribution communities according to an embodiment of the present invention, fig. 14 illustrates an embodiment of a measuring means, fig. 15A - 15D illustrate different embodiments of signal processing in an amplifier, fig. 16A - 16D illustrate different embodiments of amplifiers, fig. 17A and 17B illustrate different embodiments of monitoring means, fig. 18 illustrates an embodiment of an amplifier with measuring means, and fig. 19 illustrates an embodiment of a measuring means.. Detailed description

Fig. IA illustrates an amplifier facilitating determination of a class of a coupled load according to an embodiment of the invention.

The amplifier is connected to a load 2 via an amplifier output 4 by means of a cable. The cable may comprise one or several conductors, typically two conductors.

Further loads 6 may be coupled to the amplifier both by means of separate dedicated amplifier outputs as illustrated in fig. IA or e.g. by parallel coupling to one amplifier output, as illustrated in fig. IB.

The amplifier moreover comprises a signal processor 3 by means of which a load coupled to the amplifier output 4 may be analyzed. The signal processor 3 comprises or is associated to a data storage 5. In particular the signal processor should facilitate feature extraction applicable for automatic or semi-automatic determination of a class of the load.

The amplifier 1 may be a stand-alone amplifier or it may be distributed in two or further units. The amplifier may be any kind of amplifier, including analog amplifiers of any kind, e.g. class B, class AB, class G, class H, etc., switching amplifiers of any kind, e.g. class D, etc., or a hybrid amplifier like the so-called tracked class D amplifier, described in more detail in U.S. patent No. 5,200,711, hereby incorporated by reference.

The amplifier 1 further comprises an amplifier input AI through which the signal to be amplified and provided to the loudspeakers LS is provided. The signal input to the amplifier may be any kind of audio signal or other signal suitable for use with the amplifier and connected load(s). Preferably the signal is a complex audio signal, i.e. a multi-frequency audio signal, e.g. comprising music or speech. The signal may be provided to the amplifier input by any suitable means, e.g. a mixer, a pre-amplifier, a microphone, a musical instrument, a playback device, e.g. a compact disc player, etc. In a preferred embodiment of the invention, the amplifier is used for live performance, e.g. a concert, and the audio signal therefore originally generated on stage and pre-processed by mixers, filters, etc., before provided to the amplifier of the present invention. It is noted that even though only a single amplifier input AI is illustrated in fig. IA and IB, the amplifier according to the present invention may comprise more amplifier inputs. For example, when the audio signal is split into different frequency bands, e.g. woofer, middle and tweeter bands, prior to reaching the amplifier, and the amplifier is a 3-way amplifier, it comprises 3 amplifier inputs. On the other hand, if the channel separation into e.g. 3 bands is performed within the amplifier, it will probably comprise only one broad-band amplifier input. Evidently any combination of amplifier inputs and amplifier outputs are within the scope of the present invention.

The functioning of the above disclosed embodiment of the invention will be further described below.

Fig. 2 illustrates a principle of determination of a class of a load according to a preferred embodiment of the invention.

As just one of several examples within the scope of the invention, the determination will be explained with reference to the exemplary embodiments of fig. IA and IB. Other hardware setups may be applied within the scope of the invention.

The determination involves a reference data base DB which e.g. may be stored in the data storage 5 of fig. IA and IB. The data base comprises a number of classifiers or data related to classifiers or representing classes represented by classifiers. Such data may, e.g., comprise reference features RFC. Each reference feature corresponds and describes relevant features of loads which may be coupled to the amplifier output.

In other words, features EFC of the load connected to the amplifier output 4 in figure IA and IB are measured and extracted, preferably as a function of frequency, during a calibration or setup phase or during use, e.g. a live music or speech performance or playback of recorded audio. The measured features EFC of the coupled load are then input to the classifiers related to the data in the database DB, e.g. a set of reference features RFC for the purpose of determining a class to which the coupled load belongs.

As a specific example, the set of reference features RFC may comprise features of a number of different loads which have been measured and analyzed previously and the resulting features are then stored in relation to the amplifier system. The actual measured coupled load may then be subjected to one or more of the classifiers of the data base and therefore serve as a basis for an automatic or semi-automatic determination of the class of the coupled load.

The establishment of such a determination will be described more in detail below as well as a description of suitable features which may serve basis for a statistical class determination, e.g. based on probabilities. When allowing determination based on statistical or probability measures more complex and detailed determination may be established than by a straightforward, purely rule-based match/ no-match analysis as the loads typically are fundamentally non-linear and therefore unsuitable for straightforward linear deduction, e.g. a determination where different parameters are considered sequentially. Such non-linear behavior may e.g. rely on thermal conditions, aging, stress, production tolerance, etc. In other words, determination of class according to the present invention facilitates recognition even of loads which may look very much the same when performing primitive rule-based recognition, e.g. peak detection in impedance characteristics.

Thus specific examples of runtime monitoring of a coupled load may be established by using the output voltage and current of an amplifier generated by the music signal through the same output, e.g. estimation of the voice coil and magnet temperature when music is playing and when there is silence, detection of changes in loudspeaker setup, for example one loudspeaker is disconnected, or detection of open/short circuit at output. As stated above, an amplifier according to the present invention preferably comprises an amplifier input for receiving an audio signal, preferably a complex audio signal. However, as the utility input for the amplifier may sometimes be silent, e.g. during a break in a concert or if simply no signal is applied, the amplifier may comprise means for generating a test signal to provide to the load(s) in order to perform measurements from which features for classification can be extracted. Such test- signal may preferably comprise broad-band music or other pleasant audio program material, but may alternatively comprise pure tones, noise, etc.

In a preferred embodiment of the invention, the measuring and extraction of features EFC comprise estimating and/or measuring voltage and current of the signal provided to the loudspeaker in order to determine an impedance function of the loudspeaker, preferably as a function of frequency, and even more preferably, a complex impedance function. Several methods for determining the impedance of a loudspeaker exist, but among these methods the ones preferred are non- interrupting and substantially non-intrusive, as regards the performance of the loudspeaker reproducing the audio signal, as an interrupting or intrusive measuring method would render the classification method impossible to use during e.g. a live performance and impossible to use with complex audio signals. Examples of non- interrupting and substantially non-intrusive measuring methods are described in more detail below.

In a preferred embodiment of the present invention, the features that are extracted for use in the statistical classification process comprise e.g. mean value and variance of impedance at different frequencies.

Fig 3 illustrates examples of impedances at the vertical axis as function of frequencies at the horizontal axis of 3 different driver units aimed at handling different frequency bands, e.g. as comprised by a 3 way loudspeaker. The curve 31 with the high peak at about 65 Hz illustrates the impedance characteristic of an LF driver for reproducing audio at low frequencies with respect to the audio band, the curve 32 having two small peaks below 200 Hz illustrates the impedance characteristic for an MF driver for reproducing audio at medium frequencies with respect to the audio band, and the curve 33 being relatively flat below 200 Hz illustrates the impedance characteristic for an HF driver for reproducing audio at high frequencies with respect to the audio band. Evidently, it may be possible to some degree to classify a driver according to its type, e.g. LF, MF or HF, on the basis of the location of peaks and the number of peaks, as indicated above by a rule-based analysis. It is, however, very difficult and insecure, if not impossible, by means of pure rule-based methods to distinguish a specific driver model from tens or hundreds of drivers of the same type. As mentioned above, the present invention facilitates using statistical methods based on probability or statistical models for classifying loads, which enables more detailed determination of class, e.g. regarding subtypes or driver model, or in advanced embodiments possibly even identification of unique loudspeakers from among other loudspeakers of same type and model.

In general, the determination of a class of a load need not result in a certain determined specific load class. The result may very well need further consideration or interpretation, e.g. by a user in a semi-automatic approach. Such result can e.g. be a list of probable classes with the corresponding probabilities or uncertainties mentioned. However, the method may in preferred embodiments comprise a decision layer for performing at least a part of the considerations or interpretation otherwise required from the user. In the following, the terms classification and verification are used for different kinds of results established by such a decision layer, as indicated here. The term classification is used with embodiments where the amplifier or computer connected to the amplifier is adapted to classify the actual measured load as belonging to a certain load class, e.g. type, model, driver, etc. on the basis of classifiers representing known load classes from a database containing a range of load classes or classifiers representing classes. The term verification is used with embodiments where the amplifier or computer connected to the amplifier is adapted to verify if the actual measured load with a certain, typically predefined probability belongs to a predefined load class to determine that the actually coupled load corresponds to a predefined or expected load. A preferred embodiment of a load classification amplifier is an amplifier which is able to automatically classify all connected loads and submit a resulting, actual system configuration plan to the user, e.g. by means of a display, a printer or electronic communication. This automatic classification may advantageously be carried out when the setting up of amplifiers and loudspeakers is completed, for example in order to enable the user to easily spot any incorrect connections, e.g. LF drivers connected to subwoofer outputs. The classification may be initiated in any suitable way, e.g. by the user pressing a button or automatically each time a loudspeaker is connected or disconnected to show an up-to-date connection status, or by a central network controller submitting a classification request to all connected amplifiers, etc.

A preferred embodiment of a load verification amplifier is an amplifier which is able to receive information about expected load connections, e.g. by a user uploading a complete or partial system configuration plan to the amplifier or a computer connected to the amplifier. Using the verification method the amplifier use this information to select classifiers on the basis of which the actual measured features from the amplifier outputs are classified and submits a verification result to the user, e.g. comprising which connections correspond to the expected, and which do not.

Evidently, an advanced embodiment of an amplifier or system according to the present invention may be enabled to carry out both classification and verification according to the task at hand, and probably even determination without the decision layer, e.g. for use in other processing applications, or for full control by the user. As the difference between decision layer classification and verification methods are more related to the way the user interacts with the system, than the measurements and calculations carried out, a preferred embodiment comprises measuring and processing means with a decision layer adapted for both classification and verification, and the user interface and high level algorithms are exchangeable or selectable by simple software or hardware updates, or merely options at a main menu. It is important to mention that there is not only one method to set up the system. For small systems one amplifier may be enough to control the entire system and handle all user interaction, where in larger systems multiple amplifiers and computers can be connected to each other in a data network. Each amplifier or computer can then store a database or part of a database or a central server can store the database or at least part of the database and the user may interact with all amplifiers by means of a central user interface.

Fig. 4 illustrates a verification algorithm according to a preferred embodiment of the invention. In a user interface the user starts by selecting one or more loads 41. The user interface may be connected to a data storage 42 e.g. a database, thereby allowing the user to choose a specific loudspeaker or specific loudspeakers in the database.

Alternatively, if the system does not know the expected load, the user may input the reference features RFC or classifier necessary for the method to be able to verify if the connected load belongs to an expected class not present in the database. In any case, the act of inputting may comprise any suitable method e.g. having the user browsing through available loudspeakers or loads on a small display on the amplifier, or having the user designing the full system configuration plan on a computer, e.g. a laptop, and connecting this to a network of amplifiers that thereby automatically receive relevant data according to the system configuration plan.

When the amplifier knows the expected load for one or more output channels, i.e. actually the expected classes, a step of measurement 43 is performed by measuring characteristics of the loudspeakers coupled to the amplifier. The measurement 43 can e.g. be carried out by playing a number of frequency sweeps to each or at least one of the amplifier outputs and simultaneously record corresponding estimations of voltage and current signals at the amplifier output.

The result of the measurement step 43 is used for performing impedance calculation 44 of one or more of the loads at the measured channels. When the voltage and current values are estimated or measured it is possible to do a complex impedance calculation e.g. by a multirate FFT algorithm to obtain a frequency dependent impedance characteristic for each of the tested amplifier outputs.

On the basis of the calculated impedance characteristic, different kinds of load analysis 45 can be carried out. Apart from the verification itself, described in more detail below, the load analysis also preferably comprises creating a reference 46 for use during the live performance situation for which the amplifier-loudspeaker setup is intended. By storing the initial measured impedance characteristic of the connected loads as a reference characteristics, a great advantage for the subsequent live monitoring of loads coupled to the amplifiers output is obtained. A reference impedance characteristic measured at the actual loudspeaker makes it possible to monitor if the impedance characteristic of a load is changing e.g. because of high temperature in the load or because of wear, or if a load is beginning to deteriorate or is subjected to physical damage.

Another advantageous, possible use of the calculated impedance characteristics is the estimation of the number of loudspeakers coupled in parallel 47 to a certain amplifier output, e.g. as illustrated in fig. IB. This estimation can e.g. be made by using the imaginary part of the impedance function, the real part of the impedance function or absolute value of the impedance. Examples of impedance characteristics of different numbers of equal loudspeakers coupled in parallel are illustrated in the diagram in fig. 5, comprising frequency at the horizontal axis and impedance at the vertical axis. The first curve 51 illustrates the impedance characteristic of one loudspeaker, the second curve 52 from above illustrates the impedance characteristics of two equal loudspeakers coupled in parallel to one amplifier output, and the third and fourth curves 53 and 54 from above illustrates the impedance characteristics of respectively three and four, equal loudspeakers coupled in parallel to one amplifier output. As evident from the curves the general shape of the impedance characteristic is preserved, but differently offset and scaled for different numbers of loads coupled in parallel. This quality enables the determination of the class of loads even when more loads are coupled in parallel, and it enables the estimation of the actual number of loudspeakers when first their classes have been determined and further information, e.g. the impedance characteristic for a single loudspeaker of that type, may thus be known.

By using the imaginary part of the impedance function the estimate of the parallel connections is independent of temperature. This technique may preferably be applied when dealing with subwoofers, LF and some MF drivers.

As some drivers have only a small imaginary part in the impedance, automatic determination must rely on the real or absolute value of the impedance, thereby resulting in the determination being temperature dependent. This is the case with some MF and most HF drivers.

Turning back to fig. 4, the calculated impedance characteristics are further verified in the verify load step 48. An embodiment of the verification algorithm itself is described in more detail below with regard to fig. 6 - 8. The result of the verification, and possibly also estimation of number of loads coupled in parallel, is concluded by an output to the user in an output step 49. The output may be a simple confirmation of which loads correspond to the expected loads selected by the user in the first step 41, or it may be more advanced and for example indicate suggestions for the loads which could not be verified, in the line of "The load could be the correct one, but seems to be damaged", "Warning: the load seems to be of an incorrect type and damage to the load or amplifier may occur" or "The load is not the expected one, but seems to be of a corresponding type, and can probably be used with corresponding results".

Fig. 6 illustrates in details a preferred way to use a calculated impedance characteristic of an actual load for verifying that it is the correct load with respect to a predefined load, e.g. represented by a class on the basis of its known impedance characteristic or other features or statistical models.

The calculated impedance characteristic is first normalized in step 61 to establish an impedance characteristic that depends less on e.g. cable length and cable impedance, the temperature in the loudspeaker and the number of loudspeakers connected in parallel. It should be noted that the normalization with respect to e.g. variance is considered a statistical operation, and a method comprising normalization is thus considered a statistical method. Furthermore, it should be noted that normalization may be implemented as a separate pre-processing step as illustrated in fig. 6, or it may be implemented in the classifiers as part of the statistical model. In any case, a determination method comprising normalization with respect to e.g. variance is according to the present invention considered a statistical determination.

In a preferred embodiment the user may further have been requested or facilitated to input information about the cable, e.g. regarding length, cross section and resistivity, as long cables may influence the combined cable and loudspeaker impedance significantly. A loudspeaker cable of 40 meters may thus easily apply a resistance of 1 Ω (Ohm). With such data, the processing means may more accurately neglect the cable impedance from the determination. In an alternative embodiment, the temperature in the load is estimated on the basis of the calculated impedance function after the load class has been determined, and the user is asked if the temperature is probable. If not, the user is asked to input the more probable temperature from which the expected load impedance function can be calculated. The difference between the measured impedance function and the expected impedance function is then considered the cable component of the impedance, and neglected or subtracted in subsequent impedance calculations and consideration, e.g. live monitoring of temperature. In yet an alternative embodiment, the amplifier may be adapted to allow determination of the class of the cable, i.e. by performing the method of determination of the class of a load, wherein the load is the cable, e.g. a cable short circuited at the loudspeaker end, or applied with a special short circuiting plug or plug with a predetermined impedance response. To this purpose, the database should comprise classifiers representing cable classes as well as loudspeaker classes. In a simple embodiment, merely the resistance of the cable is used for compensation, and a simple impedance measurement is sufficient in order to establish cable component information. The normalized impedance characteristic is subject to feature extraction 62 in order to establish a discrete data material preserving the characteristics of the calculated impedance function, and on which probability calculations or other statistical acts performed by classifiers can be made. In an advanced embodiment using very complex classifiers, the feature extraction and normalization as mentioned above, may be implemented as part of the classifiers instead of carried out as separate preprocessing steps. In any case, the feature extraction may according to the present invention be considered part of the statistical method. The feature extraction is preferably performed in several bands with respect to frequency of the impedance function, which is therefore preferably split into several, e.g. seven, ten, etc., different overlapping feature extraction frequency bands (Bl, B2, ..., B7), e.g. according to the weighing distribution illustrated in fig 7. The distribution in fig. 7 illustrates with frequency at the horizontal axis and a weighing factor on the vertical axis that each feature extraction band (Bl, B2, ..., B7) overlaps the one adjacent band to each side, but with decreasing weight. Thereby any distinct frequency in the audio band is in total weighed the same, either by being present and highly weighed in only one feature extraction band, or by being present and less weighed in two feature extraction bands. It is noted that any distribution, over-lapping or not, differently weighed or not, is within the scope of the present invention. Compared to a non-overlapping distribution, the overlapping distribution illustrated in figure 7 enables, however, much better detection and comparison of curve characteristics, e.g. peaks, present at the border between two feature extraction bands and avoids a characteristic, e.g. a peak, not being recognized as significant for the comparison because the actual measurement has put it in a different feature extraction band than in the reference curve in the database.

The feature extraction 62 in a preferred embodiment comprises extracting features EFC from the calculated impedance function of the actual load. Such features may in a preferred embodiment comprise e.g. mean value and variance for each of the feature extraction bands (Bl, B2, ..., B7). That is, in each feature extraction band is determined a mean impedance and the variance of the measure impedance function. In the example with 7 feature extraction bands are thereby calculated 7 mean impedance values and 7 variances. As these may in principle be considered individually and independently by the classifier, they are considered as distinct features, and the example thus leads to 14 distinct features EFC which can be input to the classifier.

One method of establishing classifiers representing loudspeaker classes is to extract reference features RFC of the reference loudspeakers beforehand in the same way as the features are extracted from the unknown loudspeaker during the determination. In a preferred embodiment, the reference features RFC are calculated or obtained beforehand, and stored in the database or formalized into statistical models in classifiers for easy lookup and subjection to the features extracted from the actual load. In an alternative embodiment, the features are extracted from both the actual load impedance function and stored reference load impedance functions at runtime. This may be beneficial if changes in the way features are extracted or classifiers established may occur in subsequent software updates or added improvements, but on the other hand the processing gets much heavier if the features are to be extracted for several reference loads at runtime. In an embodiment, the user may expect a load that is not present in the database. In such cases, the user may input a suitable classifier, e.g. by providing a reference impedance curve and let the feature extraction algorithm extract reference features and store them in the database as a classifier, or the load may have been delivered with a set of data comprising pre- calculated reference features or other data sufficient to establish a suitable classifier.

When both calculated actual features EFC and classifiers, e.g. comprising reference features RFC are at hand, a statistical method 63 can be performed in order to establish the probability or logical response of the actual load belonging to the class of the expected load. One of several applicable statistical methods within the scope of the invention is determination on the basis of calculation of a statistical distance measure indicating the similarity of an unknown sample set to a known one. One such suitable statistical distance D can be defined as

D(x) = J(x-μγ∑-^ι(x-μ) , which is determined on the basis of mean value μ = (μ_ι ,μ₂, μ₃ ,..., μ_p ), covariance matrix Σ = E[(X - E[x])(X - E[x])^τ ] , and multidimensional feature vector x - [X₁ , x₂ , x₃ , ... , x_p ) .

In the expression of the statistical distance above, μ and Σ constitute a (simple) statistical model, representing a class, and the data vector x consist of the features extracted on the basis of a measured load.

The statistical distance defined above is a scalar (number) that indicates how far from a modeled data set a given data vector is, i.e., how different is the data vector from the class defined by the data set. Smaller distances (i.e., smaller values for D) indicate that the data vector is likely to belong to the modeled class, and large distances indicate that the data vector is unlikely to belong to the class.

Fig. 8 illustrates a part of a data set, i.e. reference features RFC, usable by a classifier defined as described above, i.e. a statistical distance classifier, and a part of the data vector, i.e. features EFC, obtained by extracting features from the unknown load. The horizontal axis counts features, and fig. 8 shows 6 (1, 2, ..., 5, n) features of an example data set and data vector with respect to a vertical axis of a suitable scale. Each feature may, according to the above mentioned preferred embodiment represent either mean impedance or variance in a certain frequency band, and in the case of the above-mentioned preferred embodiment, there would thus be 14 features along the horizontal axis. The data set, i.e. the reference features, are preferably established by measuring, in this example, impedance functions of several loudspeakers known to belong to the same class, e.g. several subwoofers if the classifier is for coarse graduation only, or e.g. LF drivers of several 3-way loudspeakers of the same model if the classifier is for narrow, model-wise graduation. From the several measured impedance functions are extracted, in the present example, mean impedance and variance in 7 feature extraction bands, leading to 14 distinct features from each reference loudspeaker. From this population is derived a mean reference feature for each feature, and a standard deviation reference feature for each feature. Thus, a data set is established that reflects the mean and standard deviation of each of the 14 reference features among the population of same-class reference loudspeakers. In fig. 8 is shown as an example measured reference features 81 (the circles) of a single reference loudspeaker. To represent the entire reference loudspeaker population are shown mean reference features 82 (horizontal line in middle of box) and standard deviation reference features 83 (difference between top and bottom of box). These are the reference features used by the statistical distance classifier when considering an input from an unknown load. If for example feature No. 1 in fig. 8 corresponds to mean impedance in a first feature extraction band, the reference features are mean value 82 of the mean impedances, and standard deviation 83 of the mean impedances, both of the first feature extraction band among the reference loudspeakers. If for example feature No. 2 in fig. 8 corresponds to variance in a first feature extraction band, the reference features are mean value of the variances, and standard deviation of the variances, both of the first feature extraction band among the reference loudspeakers. Features No. 3 and 4 may then, e.g., correspond to mean and standard deviation of mean impedance and mean and standard deviation of variances, respectively, in a second feature extraction band among the reference loudspeakers. In fig. 8 is further shown examples of features 84 (crosses) extracted from an unknown load. The statistical distance measure is a scalar that reflects how likely the load from which the features 84 are extracted belongs to the class that is described by the mean 82 and standard deviation 83 values. In the example in fig. 8 the unknown load represented by crosses 84 does probably not belong to the class described by the data set 82, 83, as the difference for most features, except No. 4, is great compared to the loudspeaker represented by circles 81 that per definition belongs to the class. It is noted, however, that fig. 8 only shows 6 features out of the, for example, 14 features.

Last step in the preferred verification method illustrated in fig. 6 is a decision layer 64 wherein the result of the statistical method, e.g. the calculated statistical distance

D is considered for determining if the actual load belongs to the class of the expected load, or at least the probability for the actual load belonging to the class of the expected load. In a preferred embodiment utilizing the statistical distance measure method described above, a threshold distance of, e.g., 7 or 10 is predefined as the critical distance where a load is said not to belong to the expected class if the distance is greater. In an alternative embodiment, or in a classification method where no expected load is presented by the user, the reference load class with the least distance to the actual load features may be indicated as the corresponding load class, or a further distance threshold criterion may be applied to take care of unknown or damaged loads.

Several other statistical and probability processing methods may be applied within the scope of the invention as long as the method enables determination of a tested load on the basis of several features extracted from the tested load coupled to the amplifier.

For example, the following methods may be applied:

• Bayesian classifier, e.g. based on a Gaussian Mixture Model of the classes

• Neural network classifier, based on a Multi-Layer Perceptron model

• Support Vector Machine classifier

Although quite different, these classifiers have some common properties, relevant for the present invention. These classifiers are all -

• Statistical, in the sense that the definition of the class or classes is done implicitly, based on a set of samples (measured load features) from each class, and in the sense that no 'rules' are used to define the classes • Robust, in the sense that the models can "generalize" from the training data, and are robust against "noise" or variations in the actual load-measurements

• Suitable for modeling multiple classes and for modeling just a single class (like the statistical distance measure does)

These classifiers and related statistical models are widely published and are readily described in textbooks on statistical pattern recognition, such as the following, hereby incorporated by reference with regard to description of classifiers, models and statistical methods suitable for use in the present invention:

• R. Duda, P. Hart, D. Stork. "Pattern Classification", (2nd ed.), Wiley, 2001.

• T. Hastie, R. Tibshurani, J.H. Friedman, "The Elements of Statistical Learning: Data Mining, Inference, and Prediction" , Spinger, 2001.

• C. Bishop. "Pattern Recognition and Machine Learning" . Springer, 2006.

• N. Cristianini, J. Shawe-Taylor, "An Introduction to Support Vector Machines", Cambridge Univ. Press, 2000.

• K. Fukunaga, "Introduction to Statistical Pattern Recognition", Academic Press, 1990.

The determination of class, with or without the decision layer, may be automatic or semi-automatic in the sense that the established probabilities may be applied directly for final determination of the type, model, make, etc. which has been coupled to the amplifier.

Such automatic determination of class may e.g. be possible if the number of load classes among which determination can be made, i.e. the classifiers represented in the reference data base of the amplifier system, are relatively low and if the load classes represented in the data base are relative easily distinguished from one another. A problem related to such a setup may of course under some circumstances result in that the determination results in: not known - not classified. It should, however, be noted that the number of features extracted from the coupled load may increase the possibility that loudspeakers looking much the same when analyzing according to conventional methods may actually be recognized. An example of this situation is given in fig. 9.

A semi automatic approach may also be that a user is presented with a number of probable matches, e.g. when using the statistical distance measure method any loads with distances less than, e.g. 20, and where the matches moreover optionally but advantageously are also associated with a probability measure, by means of which a user may deduce the probable connected load. A typical experienced user knowing the amplifiers and loudspeakers that are available to him and the differences thereof, combined with a semi-automatic system which list a few probable loudspeakers for each channel if any doubt exists, may prove very advantageous as the method of the present invention provides the user with an overview and limited range of possibilities, from which the experienced user can typically easily deduct the correct answers, and still with significant advantages compared with having the user walking from loudspeaker to loudspeaker while having a colleague at the mixer table directing audio to each channel in turn to check the connections.

In fig. 9 and 10 specific examples are given using the above mentioned method for the purpose of determining a coupled load, i.e. for classification purposes.

In the specific embodiment of fig. 9, a Vertec4889 LF load is coupled to an amplifier, e.g. the amplifier of fig. IA or IB. The determination of the class of the coupled load is in this example performed by establishment of the statistical distances D shown on the vertical axis, which are calculated on the basis of 10 classifiers on the horizontal axis in the reference database. The actually coupled Vertec4889LF belongs to reference class number 7 out of 10 classes in the database illustrated in figure 9.

The bar graph shows that class number 7 clearly has the smallest distance to the measured load. According to one embodiment of the invention, the distance may typically be between 1 and 8 for a correct load, i.e. the threshold for dismissing a load as not belonging to the class of the reference is 8. It is, moreover, noted that load classes number 7 and 10 in the database actually have very familiar looking impedance curves, but that the classifier has no problem separating them in this case. Thus, a conventional rule-based approach would have resulted in that the coupled load could not be matched to any of the reference loudspeakers characteristics, or at least not be able to distinguish between loads 7 and 10, whereas the method of the present invention of applying determination of class by statistical means results in a relatively distinct recognition. In fig. 10, a further load is tested. In this example a load unknown to the reference database is measured, and the ten statistical distances D are calculated. The specific loudspeaker used is an Adamson Spektrix MF. All the ten distances are larger than 30 as no such loudspeaker had been represented by classes in any of the classifiers in the system.

Fig. HA illustrates a flow diagram of the principle of automatic classification according to one embodiment of the invention. The amplifier or central controller performs an automatic classification 111, i.e. starts determining classes of loads on every single amplifier output or at least on a number of user defined outputs. When the amplifier or computer connected to the amplifier has finished the classification, the result 112 will be presented to the user e.g. on a display mounted on the amplifier or on a computer connected to the amplifier. The primary result of the automatic classification is an indication of which loads from among a predefined set of loads, e.g. from a database, are connected to which amplifier output. In case of an unknown or unclassifiable load on a specific channel, the result will in a simple embodiment merely contain that the load on that channel cannot be determined. Other secondary results can among other things be a determination or estimation of the number of loads coupled in parallel to one channel of the amplifier, a reference impedance curve for use in subsequent live monitoring of the loads, or any other information derivable from the measured characteristics and obtainable from the database by cross-referencing with the determined load type or model, e.g. information about rated power handling, temperature handling, etc.

Fig. HB illustrates a flow diagram of the principle of automatic classification according to an alternative embodiment of the invention. The principle illustrated in fig HB differs from the principle in fig HA by the output step 114 providing more information to the user than the output step 111 of fig. 1 IA. Such extra information may e.g. regard plain information such as the probabilities of the load classifications being correct or detailed information about the loads or it may regard action- demanding information such as options for the user to choose from, or action points to carry out. Examples of options for the user to choose from may be providing the user with 2 or more probable load class matches for each channel or a number of problematic channels and let the user tell the system which class from among the few probable options is correct, etc. Examples of action points for the user to carry out may be providing the user with information about apparently significantly worn loads and have the user do a manual inspection, etc. In any case, the user may be able to accept the result as it is, or input information or change the connections, and have a new classification carried out to reflect any changes. In an advanced embodiment of the invention, the amplifier will not provide a power signal to a load which it does not know, or which seems to be excessively worn or damaged, or which does not match the output channel type and power rating, before the user has actively confirmed to the system that the connection is deliberate and desired.

Fig. 12A illustrates a flow diagram of the principle of automatic verification according to one embodiment of the invention. The user starts with a step of selecting loads 121, which may as described above comprise browsing through available loads and selecting one for each connected output channel, or e.g. by uploading a system configuration plan to a central network controller. Also as described above, the user may in an advanced embodiment input reference features or classifiers for otherwise unknown loads in order for the system to be able to verify such loads at the output channels.

Then an automatic verification 122 is carried out where the amplifier starts testing loads on every single amplifier output or at least on a number of user defined outputs, with regards to the degree of correspondence with the expected classes predefined by the user in the first step. When the amplifier or computer connected to the amplifier has finished the verification, the result 123 will be presented to the user e.g. on a display mounted on the amplifier or on a computer connected to the amplifier, e.g. a laptop computer connected to a wireless data network to which also the amplifiers are connected. The primary result of the automatic verification is an indication of the output channels where the actual load belongs to the load class predefined by the user. Other secondary results can among other things be a verification of whether the number of loads coupled in parallel to one channel of the amplifier corresponds to the expected number, or e.g. a reference impedance curve for use in subsequent live monitoring of the loads, or any other information derivable from the measured characteristics and obtainable from the database by cross- referencing with the verified load type or model, e.g. information about rated power handling, temperature handling, etc.

Fig. 12B illustrates a flow diagram of the principle of automatic verification according to an alternative embodiment of the invention. The principle illustrated in fig 12B differs from the principle in fig 12A by the output step 126 providing more information to the user than the output step 123 of fig. 12A. Such extra information may e.g. regard plain information such as the probabilities of the load verifications being correct, or detailed information about the loads or it may regard action- demanding information such as options for the user to choose from, or action points to carry out. Examples of action-demanding information may e.g. be providing the user with information about a problematic verification and have the user do a manual verification, etc. In any case, the user may be able to accept the result as it is, or input information or change the connections, and have a new verification carried out to reflect any changes. In an advanced embodiment of the invention, the amplifier will not provide a power signal to a load which it cannot verify as being the expected load, or which seems to be excessively worn or damaged, or which does not match the output channel type and power rating, before the user has actively confirmed to the system that the connection is deliberate and desired.

As mentioned above, the user decisions 115 or 127 may further comprise inputting data from which a cable component or cable impedance can be determined.

Fig. 13A illustrates an embodiment of the present invention. Several amplifiers 1 with the capability to determine a class of a load according to the present invention exist. They may be part of a single setup, owned by the same company, or they may be owned by different users, located at different places and using them for different purposes with a different data port 130. By means of a data medium 131, e.g. a flash memory stick, suitable for use with the data port 130, classifiers or measured data may be transferred to and from the amplifiers. A central data storage CD is provided, also comprising a data port 130.

Fig. 13B illustrates a preferred embodiment of the present invention. Several amplifiers 1 with the capability to determine a class of a load according to the present invention exist. They may be part of a single setup, owned by the same company, or they may be owned by different users, located at different places and using them for different purposes with different loads. Each amplifier comprises a data port 130. Some amplifiers may be connected in a data network 132, e.g. the Internet, a LAN, a mobile network, etc., e.g. by cabled network connections 133, wireless network connections 134, or any other suitable connection means. Some amplifiers may not be connected but requires a data medium 131 to transfer data. Such data may be provided to the data network 132 by means of a laptop or PC with a suitable data port and a suitable network connection. One or more central data storages CD may also preferably be connected to the data network 132. In an embodiment of the invention, the network further comprises one or more classifier providers CP, which are companies dedicated to establishing classifiers and distributing them to the central data storages CD or amplifiers 1.

The embodiments shown in fig. 13A and 13B can be thought of as communities for distributing classifiers or data related to classifiers, requesting classifiers, or verifying classifiers. The central data storage CD may e.g. be able to receive classifiers or data from the classifier providers CP or from the amplifiers 1. The amplifiers may e.g. be able to receive classifiers from the central data storage CD, the classifier providers CP or directly from other amplifiers.

A conventional, non-interrupting and substantially non-intrusive measuring means disclosed in the prior art comprises measuring the voltage and current at the power output of the amplifier, and calculating the impedance function from these two measurements. An amplifier comprising such measuring and calculating means is described in U.S. patent No. 5,719,526, hereby incorporated by reference, where voltage and current are measured at the power output signal, converted into digital representations, and an impedance function calculated by a digital signal processor.

Fig. 14 illustrates an embodiment of a preferred non- interrupting and substantially non-intrusive measuring means for estimating and/or measuring e.g. voltage and current of the signal provided to the load from which features are to be extracted. It comprises an amplifier 1 for amplifying a digital audio signal DAS, which is provided at an amplifier input AI. An amplifier output AO is provided to a load or loudspeaker LS. The amplifier 1 comprises within the scope of the present invention any kind of audio amplifier, as described in more detail below, and the digital audio signal DAS may within the scope of the present invention be provided in any suitable digital representation and by any suitable physical means, provided a suitable interface is implemented in the amplification means. The amplifier output AO is any signal suitable for distribution to a load or loudspeaker. It is mentioned that the conversion from the digitally represented input signal AI to the amplified output signal AO may be performed at any suitable point, and possibly even at several points, within the block labelled amplifier 1 within the scope of the present invention. The load or loudspeaker LS may comprise any kind of load or loudspeaker suitable for connection to an amplifier output, including several loudspeakers coupled in parallel, 2-, 3-, or more way loudspeakers etc. The load may further include non-ideal, i.e. real life, cabling, connectors, etc.

Fig. 14 further comprises a digital reading point DR for determining a digital signal representation DSR on the basis of the digital audio signal DAS. In some embodiments the digital signal representation DSR may be read from a register containing a current sample of the digital audio signal DAS, in a different embodiment the digital signal representation DSR may be read from a buffer containing several samples of the digital audio signal DAS, and in yet a different embodiment, the digital signal representation DSR may be established by splitting a data bus providing the digital audio signal DAS to the amplifier input AI. According to the present invention, any suitable implementation of the digital reading point DR is within the scope of the present invention, and the specific way of determining the digital signal representation DSR in a specific embodiment highly depends on the physical implementation of the digital audio signal DAS and does not affect the subject matter of the present invention.

Fig. 14 further comprises an analog reading point AR for measuring a current signal representation CSR of the current provided via the amplifier output AO to the load or loudspeaker LS by the amplifier 1. The analog reading point may comprise any suitable means for determining current. Numerous methods for current measurements are described in the prior art, and any method suitable for use at a sensitive, amplified audio signal, is within the scope of the present invention. The current signal representation CSR provided by most of the possible current measurement methods is an analog representation, but any representation is within the scope of the present invention.

Fig. 14 further comprises a monitoring means MM which receives the digital signal representation DSR and the current signal representation CSR, and, possibly among other things, establishes an impedance function IF on the basis of those representations. The monitoring means MM is described in more detail below.

To establish an impedance function associated with the loudspeaker is in principle needed measurements of the voltage and the current supplied to the loudspeaker. As described above, it is well-known to simply measure these representations at the amplifier output AO, convert them to digital signals, and calculate the impedance function by digital processing means. The present measuring method, however, requires with the embodiment of figure 14 only measurement of the current at the amplifier output signal. The signal voltage also required to determine the impedance function is derived from the digital audio signal input to the amplifier. Ideally, the amplifier 1 comprises merely a gain, and the difference between the digital audio signal and the analog amplifier output is thus only a gain factor and the type of representation, digital vs. analog. As the impedance function is calculated by digital processing means, it is relevant to use the exact digital representation instead of a measured analog representation of the output voltage of the amplifier. Even without knowing the gain of the amplifier 1 , it is thereby possible to determine a relative or normalized impedance function on the basis of the digital signal representation DSR and an analog-to-digital converted version of the current signal representation CSR. A normalized impedance function suffices for several classification or verification purposes.

In a more advanced embodiment of the measuring means, the gain of the amplifier 1 is known by the monitoring means MM, and it is thereby possible to determine the absolute impedance function of the loudspeaker. The absolute impedance function may be used for even better classification or verification, e.g. when several reference loudspeaker are quite similar, and may be used for further purposes such as determining the number of loads coupled in parallel. Also the reference impedance possibly used for live monitoring of a loudspeaker is a better starting point if it comprises an absolute impedance function.

In real amplifiers, the amplifier 1 comprises not only a gain, but also a delay and a transfer function often causing less gain at in particular very low and very high frequencies. Also non-linear distortion exists to some, however low, degree in the amplifier. Hence, the presumption that a normalized or absolute impedance function can be calculated from the digital signal representation derived prior to the amplifier, is not true if a very accurate impedance function for in particular low and high frequencies is desired. In such cases, and depending on the degree of accuracy desired or required, the digital signal representation DSR may be processed before use in the impedance calculation to compensate for some of the above errors. Embodiments covering this aspect are described in more detail below.

Fig. 15A - 15D illustrate different implementations of the digital audio signal DAS and the digital reading point DR. In most audio amplifiers some degree and kind of signal processing is desired before the amplification, and essentially all contemporary amplifiers implement such signal processing by digital means, e.g. digital signal processors DSP's, microcontrollers or microprocessors, field programmable gate arrays FPGA's, application specific integrated circuits ASIC's, etc. The signal processing may, e.g., comprise equalization to compensate for known errors in the amplifier, output impedance, output filter, loudspeaker, cables or other components, limitation or compression to avoid distortion from clipping in the amplifier, filtering to, e.g., perform channel separation, signal delaying to improve cooperation with other amplifiers and taking physical distributions into consideration, etc. Fig. 15A - 15D illustrate different implementation of such signal processing in an embodiment of measuring means according to figure 14. It is noted, however, that any implementation of signal processing, including distributing the signal processing to several points, and/or analog signal processing, is within the scope of the present invention.

Fig. 15A illustrates an embodiment where the signal processor SP is implemented prior to the digital reading DR of the digital signal representation DSR. The signal processor SP is preferably a digitally implemented processor, e.g. inside a DSP or any other digital processing means as mentioned above, and it provides the digital audio signal DAS on the basis of a digital input signal DS.

Fig. 15B illustrates a different embodiment where the signal processor SP is implemented subsequently to the digital reading DR of the digital signal representation DSR. The signal processor provides the amplifier input AI on the basis of the digital audio signal DAS, derived from a digital input signal DS. The signal processor is preferably digitally implemented.

Fig. 15C illustrates yet a different embodiment with signal processing SP and digital reading DR arranged as in figure 2B, but with an analog input signal AS. An analog- to-digital converter ADC is provided for facilitating digital processing of the analog input signal, and for facilitating establishment of the digital audio signal DAS. In an alternative embodiment, the signal processing, or part of it, may be performed on the analog input signal AS, and the A/D-converter located subsequently, but prior to the digital reading point. Fig. 15D illustrates a preferred embodiment where the digital signal representation DSR is derived from within the signal processor SP, i.e. where signal processing is or may be performed prior to the digital reading point by a first signal processor SPl, subsequent to the digital reading point by a second signal processor SP2 and optionally also by a third signal processor SP3 on the digital signal established by the digital reading point and from which the digital signal representation is derived. This embodiment facilitates using the signal processor for performing processing on the digital signal representation DSR instead of merely forwarding a copy of the digital audio signal DAS. It also facilitates a combination of the embodiments of figures 2 A and 2B, so that processing of the digital input signal DS can be done both before and after the digital reading point, i.e. basing the digital signal representation DSR on a partly processed digital audio signal. In this embodiment, the first signal processor SPl will typically comprise shaping of the audio signal with regard to desired listening preferences, the second signal processor SP2 will typically comprise compensation of errors of the subsequent stages, e.g. the amplifier, output impedance, cable or loudspeaker in order to facilitate a true reproduction of sound, and the third signal processor SP3 will typically comprise processing needed to adapt the digital audio signal to a signal usable by the impedance calculation circuit.

It should be noted that any other implementation of signal processing and digital reading point, and any combination of the above-described features, is within the scope of the present invention.

Fig. 16A - fig. 16D illustrates different embodiments of amplifier 1 according to the present invention. As mentioned above, any kind of audio amplifier implementing the amplifier 1 is within the scope of the present invention.

Fig. 16A comprises a switching amplifier SA receiving the amplifier input AI and delivering the amplifier output AO. The switching amplifier may comprise any kind of switching amplifier implementation suitable for audio amplifiers, and preferably comprises at least a modulator for modulating the digital audio signal DAS at the amplifier input AI into a pulse width modulated signal, pulse density modulated signal or other suitable representation, which is then fed to a switching power stage. The output of the power stage is preferably demodulated, e.g. by means of an inductance-capacitance-implemented low-pass filter. Any specific implementation of the modulation and power stages is within the scope of the present invention, including self-oscillating PWM amplifiers, amplifiers with feedback, advanced modulation techniques comprising additional processing and error compensation, any kind of PWM modulation, e.g. 2-level, 3-level, etc., any kind of power stage, etc. In a preferred embodiment of figure 16A the modulation stage is digital and thus able to receive the digital audio signal DAS at the amplifier input AI. In alternative embodiments the pulse width modulation is performed in the analog domain and a D/A-converter is required for facilitating the digital audio signal input.

It should be noted that any representation or format of the amplifier output AO is within the scope of the present invention, e.g. single-ended or balanced outputs. Fig. 16B comprises an embodiment of the amplifier 1, comprising a switching amplifier SA as described above regarding figure 16A, but with a balanced amplifier output AO.

Fig. 16C illustrates an alternative embodiment of an amplifier 1, comprising a D/A- converter DAC and an analog amplifier AA. Any kind of analog amplifier is within the scope of the present invention, including any variations of, e.g. class B, class AB, class D, class H, class G, etc., amplifiers.

Fig. 16D illustrates a preferred embodiment of an amplifier 1 for use in an embodiment of the present invention. It comprises a so-called class TD, or "tracked class D" amplifier, which utilizes an analog power stage AA supplied by switched power supplies controlled by the audio signal amplitude. A positive offset means

POM establishes a control signal that has a value always a bit above the audio signal, and a negative offset means NOM establishes a control signal that has a value always a bit below the audio signal. These control signals are pulse width modulated by modulators PWM, and used as power supply for the analog amplifier AA, preferably a class AB amplifier. This implementation causes much less power loss in the analog power stage compared to a conventional class AB amplifier, as the transistors are only provided the required voltage for amplifying the actual audio signal. A D/ A- converter DAC is provided for converting the digital audio signal DAS into an analog audio signal for the analog power stage AA. Fig. 16D shows a feedback from the amplifier output AO to the input of the analog power stage for error suppression, but this feedback is optional. It is noted that the amplifier illustrated in figure 16D is described in much more detail, including a specific implementation thereof, in U.S. patent No. 5,200,711, hereby incorporated by reference.

It should be noted that any other amplifier implementation or combination of above- described features is within the scope of the present invention.

Fig. 17A and 17B illustrates different embodiments of the monitoring means MM. Fig. 17A illustrates a monitoring means MM comprising an impedance calculation circuit ICP. The monitoring means receives the current signal representation CSR, which is converted to a digital representation by an A/D-converter ADC, and the digital signal representation DSR, which is delayed by delay means DM before provided to the impedance calculation circuit ICP. Because of the delay added to the audio signal by the amplifier 1, possibly comprising also a delay from a D/ A- converter, and the delay added to the current signal representation by the A/D- converter, the digital signal representation derived from the digital audio signal DAS before entering the amplifier 1 has to be delayed correspondingly in order to be in synchronism with corresponding current measurements derived from the audio signal subsequent to the amplifier 1. In a preferred amplifier, the delay means DM may delay the signal by, e.g., 0.25 - 1.0 ms. Because of the delay means DM, the impedance calculation circuit ICP is able to calculate the impedance function IF on the basis of corresponding samples of digital signal value and analog output current, i.e. the analog output current caused by a certain digital signal value. If the accuracy requirement for the impedance function is not extremely high, and/or if the transfer function of the amplifier 1 except for delay and DC gain is close to unity for the relevant frequencies, the embodiment of fig. 17A may be sufficient to establish a useful impedance function IF. In a more advanced embodiment, the delay means DM adds a frequency dependent delay, as the delay added to the audio signal by the amplifier is often frequency- dependent, i.e. is different for different frequencies.

Figure 17B illustrates an embodiment of monitoring means MM which better takes into account additional errors added to the audio signal by the amplifier 1, and thereby it is necessary to add to the digital signal representation DSR to be able to calculate an impedance function that most accurately resembles the impedance function of the load, i.e. based on the signal that is provided to the load including the errors added by the amplifier 1. The improvement comprises the digital signal representation DSR being processed by an amplification means model AMM. This model ideally comprises the transfer function of the amplifier 1. As the full transfer function is in most real-life cases impossible to establish perfectly correct, even in the relevant frequency band, the amplification means model AMM may comprise the most significant errors caused by the amplifier 1, to a degree that facilitates calculation of a sufficiently accurate impedance function IF. Such significant errors preferably comprise the above-mentioned delay, preferably frequency dependent, the DC-gain, any frequency-dependent gain at low and high frequencies within the relevant band, and any significant non-linearities, e.g. frequency-dependent clipping values.

In a preferred embodiment, the amplification means model AMM is extended to also include a model of the loudspeaker cable, or significant errors related to the loudspeaker cable. In loudspeaker setups with relatively long cables the impedance of the cable, in particular it's DC resistance, becomes significant compared to the loudspeaker impedance, and will thus influence the impedance calculation significantly. A certain loudspeaker cable of 40 meters may for instance add a resistance of 1Ω (Ohm), and as the analog reading point AR in any practical case is located at the amplifier's end of the loudspeaker cable, the impedance function calculated will be an impedance function of the combined loudspeaker cable and loudspeaker. By compensating for the cable impedance in the extended amplification means model AMM, calculation of the loudspeaker impedance is facilitated, even with long, non-ideal cable connections.

The establishment of a cable model or an estimate of the most significant errors introduced by the cable may, e.g., be made by allowing the user to input cable characteristics such as cable length, cross section and resistivity into the processing means by means of a user interface. Alternatively, an amplifier with impedance calculation can be used to estimate the cable impedance by shorting the cable at the loudspeaker end during measuring, and subsequently establish a cable model to include in an extended amplification means model AMM from the measurements. Alternatively, as a neglected, significant cable resistivity will typically make a calculated impedance function indicate a very hot loudspeaker, the amplifier may provide a user interface means for providing to the processing means the information that the loudspeaker is definitely not hot, and the impedance features indicating a hot loudspeaker should instead be considered as cable impedance and, e.g., regarded as a cable model for subsequent measurements.

The amplification means model AMM may be established by measurements at the time of manufacture of the amplifier, or it may be configurable or adjustable in order to change with any changes of the amplifier 1 over time. In an advanced embodiment, the transfer function, or significant characteristics thereof, of the amplifier is measured at each start-up or at user-defined times, and the result is used to calibrate the amplification means model AMM. For this purpose the amplifier may comprise means for measuring the voltage of the amplifier output signal, and an A/D-converter to provide this signal to the amplification means model AMM for calibration purposes. It is noted, however, that such voltage measurement does not require the same degree of quality, e.g. in regard to the A/D-converter, as if it is used for runtime impedance calculation as described in the prior art, as timing is not an important issue in a calibration situation.

In a preferred embodiment of the invention, the impedance calculation circuit ICP comprises windowing in the time domain of the input signals, and/or weighted averaging of the calculated impedance in order to establish a good estimate of the impedance function, and in order to avoid impedance functions calculated at uncertain signals or under uncertain conditions, e.g. during clipping, to influence the established impedance function significantly.

In a preferred embodiment, the impedance calculation circuit ICP comprises a multirate fast Fourier transform FFT algorithm in order to establish impedance functions in relevant time windows, but any method of estimating or calculating an impedance function on the basis of the digital signal representation DSR and a voltage signal representation VSR or a current signal representation CSR is within the scope of the present invention.

Figure 18 comprises a preferred embodiment of a measuring means for providing features to the classification methods of the present invention, established by combining the above-described preferred embodiments of sub-components. Figure 18 further comprises centralization of all digital processing within one digital signal processor DSP. In an alternative embodiment, the digital processing is distributed to several digital signal processors or any other means for performing programmable or logical processing.

Fig. 19 illustrates a further alternative embodiment of the present invention. Fig. 19 corresponds to fig. 14 except from the amplifier 1, which is a current amplifier in the embodiment of fig. 19, and the signal measured by the analog reading point AR, which is a voltage signal representation VSR in the embodiment of fig. 19.

The analog reading point AR is in the present embodiment of the measuring means measuring a voltage signal representation VSR of the voltage provided via the amplifier output AO to the load or loudspeaker LS by the current amplifier 1. The analog reading point may comprise any suitable means for determining voltage. Numerous methods for voltage measurements are described in the prior art, and any method suitable for use at a sensitive, amplified audio signal, is within the scope of the present invention. The voltage signal representation VSR provided by most of the possible voltage measurement methods is an analog representation, but any representation is within the scope of the present invention. The signal current also required to determine the impedance function is derived from the digital audio signal input to the current amplifier on the basis of knowledge of the current gain and possibly also errors or transfer function of the current amplifier 1. As the impedance function is calculated by digital processing means, it is relevant to use the exact digital representation instead of a measured analog representation of the output current of the current amplifier.

In an embodiment of the invention, the monitoring means MM comprises means for identifying the load or class of load on the basis of the calculated impedance or other load characteristics.

Further details and alternatives to methods and amplifiers enabled to establish a load impedance function are e.g. described in PCT patent application No. PCT/DK2007/050099 filed on 16 July 2007, hereby incorporated by reference as regards a method and amplifier for establishing an impedance function of a connected load.

Claims

Patent claims

1. Method of determining a class of a load by

- providing at least one classifier representing at least one class, - measuring and extracting at least one feature (EFC) of a measured load (LS; 2, 6) while a signal is provided to said measured load,

- determining the class (CL) of said measured load statistically on the basis of said at least one feature and said at least one classifier.

2. Method of determining a class of a load according to claim 1, whereby said method is an observing method.

3. Method of determining a class of a load according to claim 1 or 2, whereby said measuring employs a substantially non-intrusive measuring method as regards affecting an interaction between said signal and said measured load.

4. Method of determining a class of a load according to any of the claims 1 to 3, whereby said method is non-interrupting as regards a reproduction of an audio signal by said measured load.

5. Method of determining a class of a load according to any of the claims 1 to 4, whereby said measuring comprises multi-bit sampling.

6. Method of determining a class of a load according to any of the claims 1 to 5, whereby said measuring comprises measuring on the basis of a complex audio signal.

7. Method of determining a class of a load according to any of the claims 1 to 6, whereby at least one of said at least one feature (EFC) comprises a feature derived from an impedance function of said measured load.

8. Method of determining a class of a load according to any of the claims 1 to 7, whereby said measuring comprises measuring at least one of a voltage and a current of said signal, and at least one of said at least one feature (EFC) is derived from an impedance of said load determined on the basis of a measured or estimated voltage and a measured or estimated current of said signal.

9. Method of determining a class of a load according to any of the claims 1 to 8, whereby a result of said measuring is a frequency dependent result.

10. Method of determining a class of a load according to any of the claims 1 to 9, whereby said measured load (LS; 2, 6) is connected to an amplifier (1).

11. Method of determining a class of a load according to any of the claims 1 to 10, wherein the method is applied within an amplifier (1) and wherein said measured load (LS; 2, 6) is connected electrically to said amplifier (1).

12. Method of determining a class of a load according to claim 10 or 11, wherein the amplifier executes the method by means of data processing means (3) according to instructions stored in memory means.

13. Method of determining a class of a load according to any of the claims 10 to 12, wherein the amplifier comprises a data port by means of which models may be transferred to or from the amplifier.

14. Method of determining a class of a load according to any of the claims 1 to 13, wherein said classifier or data related with said classifier is stored in a data storage (5).

15. Method of determining a class of a load according to claim 14, wherein said amplifier (1) comprises said data storage (5).

16. Method of determining a class of a load according to claim 14 or 15, wherein said data storage (5) is comprised by a central data storage to which said amplifier

(1) has irregular or continuous access.

17. Method of determining a class of a load according to any of the claims 1 to 16, wherein said method comprises a decision layer.

18. Method of determining a class of a load according to any of the claims 1 to 17, wherein said at least one classifier comprises a parametric classifier.

19. Method of determining a class of a load according to any of the claims 1 to 18, wherein said at least one classifier comprises a non-parametric classifier.

20. Method of determining a class of a load according to any of the claims 1 to 19, wherein said at least one class is established by providing a set of reference features (RFC) of a plurality of reference loudspeaker units (RLS).

21. Method of determining a class of a load according to claim 20, wherein at least one of said reference features (RFC) is provided as a function of frequency.

22. Method of determining a class of a load according to any of the claims 1 to 21, wherein at least one of said features (EFC) is determined as a function of frequency.

23. Method of determining a class of a load according to any of the claims 20 to 22, wherein said determination of a class of said load (LS; 2,6) is based on a probability of match between said measured and extracted features (EFC) of said measured load and one or several reference features (RFC).

24. Method of determining a class of a load according to any of the claims 1 to 23, wherein said determination of a class of said load (LS; 2,6) involves a statistical determination.

25. Method of determining a class of a load according to any of the claims 1 to 24, wherein said determination of a class of said load (LS; 2,6) involves a non-linear signal processing.

26. Method of determining a class of a load according to any of the claims 1 to 25, wherein the determination is performed automatically.

27. Method of determining a class of a load according to any of the claims 1 to 26, wherein the determination is performed semi-automatically.

28. Method of determining a class of a load according to any of the claims 1 to 27, wherein the determination is performed during a calibration phase.

29. Method of determining a class of a load according to any of the claims 1 to 28, wherein the determination is performed during a verification phase.

30. Method of determining a class of a load according to any of the claims 1 to 29, wherein said determination of a class of said load (LS; 2,6) involves indication of one or more of the make, model, band, driver and/or number of parallel coupled loudspeakers of the measured load.

31. Method of determining a class of a load according to any of the claims 1 to 30, wherein said determination of a class of said load (LS; 2,6) involves indication of the type of the measured load.

32. Method of determining a class of a load according to any of the claims 1 to 31, wherein said determination of a class of said load (LS; 2,6) involves indication of possible classes to a user and where the possible classes are associated with indication of calculated probabilities.

33. Method of determining a class of a load according to any of the claims 1 to 32, wherein said determination of a class of said load (LS; 2,6) involves indication of the load being a predefined load, or the probability of the load being a predefined load.

34. Method of determining a class of a load according to any of the claims 1 to 33, wherein said features (EFC) are extracted in at least two separate or overlapping feature extraction bands (Bl, B2, ..., B7).

35. Method of determining a class of a load according to any of the claims 1 to 34, wherein said features (EFC) are extracted in at least three overlapping feature extraction bands (Bl, B2, ..., B7).

36. Method of determining a class of a load according to any of the claims 1 to 35, wherein said features (EFC) are extracted in at least seven overlapping feature extraction bands (Bl, B2, ..., B7).

37. Method of determining a class of a load according to any of the claims 1 to 36, wherein the determination of a class of said measured load is applied for deriving of information associated with said class (CL).

38. Method of determining a class of a load according to any of the claims 1 to 37, wherein said at least one feature (EFC) comprises at least one electrical measurement or is derived from at least one electrical measurement.

39. Method of determining a class of a load according to any of the claims 1 to 38, wherein said at least one feature (EFC) comprises impedance of the load as function of frequency, variance and/or mean value of the impedance of the load, resonance frequency of the load, DC resistance of the load, etc.

40. Method of determining a class of a load according to any of the claims 1 to 39, wherein said measured and extracted features are compensated for a cable component before said determination of class is performed on the basis of said features.

41. Method of determining a class of a load according to any of the claims 1 to 40, wherein at least two features (EFC) are established and analyzed in different feature extraction bands (Bl, B2, ..., B7), overlapping or non-overlapping.

42. Load class determining amplifier, comprising an amplifier (1), a data processing means (3) and an amplifier output (AO; 4) connected to a load (LS; 2, 6), said amplifier (1) comprising means for measuring and extracting at least one feature (EFC) of said load while a signal is provided to said load, and said data processing means (3) comprising means for determining the class (CL) of said load statistically on the basis of said at least one feature and at least one classifier.

43. Load class determining amplifier according to claim 42, wherein said amplifier comprises an amplifier input (AI) provided with a complex audio signal, and said means for measuring comprises means for measuring characteristics of a signal provided to said load, said signal being derived from said complex audio signal.

44. Load class determining amplifier according to claim 42 or 43, wherein said means for measuring comprises a multi-bit analog-to-digital converter.

45. Load class determining amplifier according to any of the claims 42 to 44, wherein said means for measuring employs a non-interrupting measurement method as regards the reproduction of an audio signal by said load.

46. Load class determining amplifier according to any of the claims 42 to 45, wherein said means for measuring employs multi-bit sampling.

47. Load class determining amplifier according to any of the claims 42 to 46, wherein at least one of said at least one feature (EFC) comprises a feature derived from an impedance function of said load.

48. Load class determining amplifier according to any of the claims 42 to 47, comprising means for carrying out a method of determining a class of a load according to any of the claims 1 to 41.

49. Method of verifying if a load (LS; 2, 6) connected to an amplifier output (AO; 4) corresponds to a predefined load, said method comprising the steps of

- measuring and extracting at least one feature (EFC) of a measured load (LS; 2, 6) while a signal is provided to said measured load,

- determining statistically on the basis of said at least one feature (EFC) and at least one classifier representing at least one class if said measured load belongs to the class of said predefined load.

50. Method of verifying if a load corresponds to a predefined load according to claim 49, whereby said method is an observing method.

51. Method of verifying if a load corresponds to a predefined load according to claim 49 or 50, whereby said measuring employs a substantially non-intrusive measuring method as regards affecting an interaction between said signal and said measured load.

52. Method of verifying if a load corresponds to a predefined load according to any of the claims 49 to 51, whereby said method is non- interrupting as regards a reproduction of an audio signal by said measured load.

53. Method of verifying if a load corresponds to a predefined load according to any of the claims 49 to 52, whereby said measuring comprises multi-bit sampling.

54. Method of verifying if a load corresponds to a predefined load according to any of the claims 49 to 53, whereby said measuring comprises measuring on the basis of a complex audio signal.

55. Method of verifying if a load corresponds to a predefined load according to any of the claims 49 to 54, whereby at least one of said at least one feature (EFC) comprises a feature derived from an impedance function of said measured load.

56. Method of verifying if a load corresponds to a predefined load according to any of the claims 49 to 55, whereby said measuring comprises measuring at least one of a voltage and a current of said signal, and at least one of said at least one feature (EFC) is derived from an impedance of said load determined on the basis of a measured or estimated voltage and a measured or estimated current of said signal.

57. Method of verifying if a load corresponds to a predefined load according to any of the claims 49 to 56, whereby a result of said measuring is a frequency dependent result.

58. Method of verifying if a load corresponds to a predefined load according to any of the claims 49 to 57, whereby said measured load (LS; 2, 6) is connected to an amplifier (1).

59. Method of verifying if a load corresponds to a predefined load according to any of the claims 49 to 58, whereby the method is applied within an amplifier (1) and wherein said measured load (LS; 2, 6) is connected electrically to said amplifier (1).

60. Method of verifying if a load corresponds to a predefined load according to any of the claims 49 to 59, whereby said determining statistically on the basis of said at least one feature (EFC) and at least one classifier representing at least one class if said measured load belongs to the class of said predefined load, comprises a method of determining a class of a load according to any of the claims 1 to 41.

61. Load verification amplifier comprising an amplifier (1), a data processing means (3) and an amplifier output (AO; 4) connected to a load (LS; 2, 6), said amplifier (1) comprising means for measuring and extracting at least one feature (EFC) of said load while a signal is provided to said load, and said data processing means (3) comprising means for determining statistically on the basis of said at least one feature (EFC) and at least one classifier representing at least one class if said load belongs to the class of said predefined load.

62. Load verification amplifier according to claim 61, wherein said amplifier comprises an amplifier input (AI) provided with a complex audio signal, and said means for measuring comprises means for measuring characteristics of a signal provided to said load, said signal being derived from said complex audio signal.

63. Load verification amplifier according to claim 61 or 62, wherein said means for measuring comprises a multi-bit analog-to-digital converter.

63. Load verification amplifier according to any of the claims 61 to 64, wherein said means for measuring employs a non- interrupting measurement method as regards the reproduction of an audio signal by said load.

64. Load verification amplifier according to any of the claims 61 to 63, wherein said means for measuring employs multi-bit sampling.

65. Load verification amplifier according to any of the claims 61 to 64, wherein at least one of said at least one feature (EFC) comprises a feature derived from an impedance function of said load.

66. Load verification amplifier according to any of the claims 61 to 65, comprising means for carrying out a method of verifying if a load corresponds to a predefined load according to any of the claims 49 - 60.

67. System comprising an amplifier (1) according to any of the claims 42 to 48 or 61 to 66, and at least one load (2, 6).

68. Use of a method according to any of the claims 1 to 41 or 49 to 60.

69. Method of determining a class of a load by - providing at least one classifier representing at least one class,

- measuring and extracting at least one feature (EFC) of a measured load (LS; 2, 6), - determining the class (CL) of said measured load statistically on the basis of said at least one feature and said at least one classifier.