EP2656633B1 - Device and method for calculating speaker signals for a plurality of speakers using a delay in the frequency domain - Google Patents

Description

The present invention relates to an apparatus and a method for calculating loudspeaker signals for a plurality of loudspeakers using frequency-domain filtering, such as a wave-field synthesis renderer device and a method of operating such a device.

In the field of consumer electronics, there is a constant need for new technologies and innovative products. An example here is the most realistic reproduction of audio signals.

Methods for multi-channel loudspeaker reproduction of audio signals have been known and standardized for many years (see, for example, US Pat. WO 2009/046223 and US 2010/0208905 ). All the usual techniques have the disadvantage that both the installation site of the loudspeakers and the position of the listener are already impressed on the transmission format. If the speakers are arranged incorrectly with respect to the listener, the audio quality suffers significantly. An optimal sound is only possible in a small area of the playback room, the so-called sweet spot.

A better natural spatial impression as well as a stronger envelope in the audio reproduction can be achieved with the help of a new technology. The basics of this technology, Wave Field Synthesis (WFS), were researched at the TU Delft and first introduced in the late 1980s (Berkhout, AJ, de Vries, D .; Vogel, P.). : Acoustic Control By Wavefield Synthesis JASA 93, 1993).

Due to the enormous demands of this method on computer performance and transmission rates, wave field synthesis has rarely been used in practice. Only the advances in the areas of microprocessor technology and audio coding allow the use of this technology in concrete applications.

The basic idea of WFS is based on the application of Huygens' principle of wave theory: every point, which is detected by a wave, is the starting point of an elementary wave, which spreads in a spherical or circular manner.

Applied to the acoustics can be simulated by a large number of speakers, which are arranged side by side (a so-called speaker array), any sound field. For this purpose, the audio signal of each speaker is generated by the application of a so-called. WFS operator from the audio signal of the source. In the simplest case, e.g. when playing a point source and a linear loudspeaker array, the WFS operator corresponds to an amplitude scaling and a time delay of the input signal. The application of this amplitude scaling and time delay is hereafter referred to as Scale & Delay.

In the case of a single point source to be reproduced and a linear arrangement of the speakers, a time delay and amplitude scaling can be applied to the audio signal of each loudspeaker so that the radiated sound fields of the individual loudspeakers are properly superimposed. With multiple sound sources, the contribution to each speaker is calculated separately for each source and the resulting signals added together. If the sources to be reproduced are in a room with reflective walls, reflections must also be reproduced as additional sources via the loudspeaker array. The cost of the calculation therefore depends heavily on the number of sound sources, the reflection characteristics of the recording room and the number of speakers.

The advantage of this technique is in particular that a natural spatial sound impression over a large area of the playback room is possible. In contrast to the known techniques, the direction and distance of sound sources are reproduced very accurately. To a limited extent, virtual sound sources can even be positioned between the real speaker array and the listener.

The application of wave field synthesis provides good, if the assumed assumptions in theory, such as ideal speaker characteristics, regular, gapless speaker arrays or free field conditions for sound propagation are at least approximately fulfilled. In practice, however, these conditions are often violated, for. B. by incomplete speaker arrays or a significant influence of room acoustics.

An environmental condition can be described by the impulse response of the environment.

This will be explained in more detail with reference to the following example. It is assumed that a loudspeaker emits a sound signal against a wall whose reflection is undesirable.

For this simple example, the space compensation using wavefield synthesis would be to first determine the reflection of that wall to determine when a sound signal reflected from the wall will return to the loudspeaker and what amplitude this reflected sound signal will be Has. If the reflection from this wall is undesirable, then with the wave field synthesis it is possible to eliminate the reflection from this wall by impressing the loudspeaker with a signal of opposite amplitude to the reflection signal in addition to the original audio signal, so that the traveling compensating wave is the Reflectance wave extinguished, so that the reflection from this wall in the environment that is considered, is eliminated. This can be done by first computing the impulse response of the environment and determining the nature and position of the wall based on the impulse response of that environment. In this case, the sound reflected by the wall is represented by an additional WFS sound source, a so-called mirror sound source, whose signal is generated by filtering and delay from the original source signal.

If the impulse response of this environment is first measured and the compensation signal is then calculated, which must be impressed on the audio signal superimposed on the loudspeaker, a cancellation of the reflection from this wall will take place, such that a listener in this environment has the impression that this wall does not exist at all.

Decisive for an optimal compensation of the reflected wave, however, is that the impulse response of the room is accurately determined, so that no overcompensation or undercompensation occurs.

The wave field synthesis thus allows a correct mapping of virtual sound sources over a large playback area. At the same time it offers the sound engineer and sound engineer new technical and creative potential in the creation of even complex soundscapes. Wave field synthesis, as developed at the TU Delft in the late 1980s, represents a holographic approach to sound reproduction. The basis for this is the Kirchhoff-Helmholtz integral. This states that any sound fields within a closed volume are distributed by means of a distribution of monopoly and dipole sound sources (speaker arrays) can be created on the surface of this volume.

In wave field synthesis, from an audio signal that emits a virtual source at a virtual position, a synthesis signal is calculated for each loudspeaker of the loudspeaker array, the synthesis signals being designed in amplitude and delay such that a wave resulting from the superimposition of the individual the sound wave present in the loudspeaker array will correspond to the wave that would result from the virtual source at the virtual position if that virtual source at the virtual position were a real source with a real position.

Typically, multiple virtual sources exist at different virtual locations. The computation of the synthesis signals is performed for each virtual source at each virtual location, typically resulting in one virtual source in multiple speaker synthesis signals. Seen from a loudspeaker, this loudspeaker thus receives several synthesis signals, which go back to different virtual sources. A superimposition of these sources, which is possible due to the linear superposition principle, then gives the reproduced signal actually emitted by the speaker.

The possibilities of wave field synthesis can be better exploited the larger the speaker arrays are, ie. H. the more individual speakers are provided. However, this also increases the computing power which a wave field synthesis unit has to accomplish, since channel information also typically has to be taken into account. This means in more detail that from each virtual source to each speaker in principle a separate transmission channel is present, and that in principle there may be the case that each virtual source leads to a synthesis signal for each speaker, or that each speaker a number of synthesis signals which equals the number of virtual sources.

If, in particular, in cinema applications the possibilities of wave field synthesis are to be exploited to the extent that the virtual sources can also be mobile, then it can be seen that due to the calculation of the synthesis signals, the calculation of the channel information and the generation of the reproduction signals by combining the channel information and the synthesis signals quite considerable computing power has to be mastered.

Another important extension of wave field synthesis is the reproduction of virtual sound sources with complex, frequency-dependent directional characteristics. For each source / loudspeaker combination, in addition to a delay, the convolution of the input signal with a special filter must also be taken into account here, which then generally exceeds the computation effort in existing systems.

The object of the present invention is to provide an efficient concept for calculating loudspeaker signals for a plurality of loudspeakers using audio sources.

This object is achieved by a loudspeaker signal calculation apparatus according to claim 1, a loudspeaker signal calculating method according to claim 18 or a computer program according to claim 19.

The present invention is advantageous in that it provides an efficient concept through the combination of an out-of-order stage, a memory, a memory access controller, a filter stage, a summer stage, and a re-transform stage, characterized in that the The number of round-trip transformation calculations need not be made for each individual audio source / speaker combination, but only for each individual audio source.

Similarly, the re-transformation need not be calculated for each individual audio-signal-speaker combination, but only for the number of speakers. This means that the number of Hin-Transform calculations is equal to the number of audio sources and the number of reverse-transformation calculations is equal to the number of loudspeaker signals or loudspeakers to be driven when a loudspeaker signal drives a loudspeaker. It is also particularly advantageous that the introduction of the delay in the frequency domain is achieved by a memory access control in an efficient manner by advantageously exploiting the feed used in the transformation based on a delay value for an audio signal / loudspeaker combination. In particular, for every audio signal, the out-of-order stage provides a sequence of short-term spectra stored in memory for each audio signal. The memory access controller thus has access to a sequence of temporally successive short-term spectra. Based on the delay value, for an audio signal-speaker combination, the short-term spectrum is selected from the sequence of short-term spectra that best matches the delay value provided by, for example, a wave field synthesis operator. For example, if the feed value in the calculation of the individual blocks of a short-term spectrum to the next short-term spectrum is 20 ms, and if the wave-field synthesis operator requires a delay of 100 ms, then this entire delay can easily be implemented by not having the most recent short-term spectrum in the memory for the audio / loudspeaker combination under consideration is used, but also stored fifth past spa time spectrum. Thus, the device according to the invention is already able to implement a delay solely on the basis of the stored short-term spectra in a certain grid, which is determined by the feed. If this grid is already sufficient for a particular application, no further action is required. However, if a finer delay control is needed, it can also be implemented in the frequency domain by using in the filtering stage to filter a particular short-term spectrum a filter whose impulse response has been manipulated with a certain number of zeros at the beginning of the filter impulse response. Thus, a finer delay granulation can be achieved, which does not take place now, as in the memory access control in periods according to the block feed, but now much finer in terms of a sampling period, ie the time interval between two samples. Moreover, if even finer granulation of the delay is needed, it can also be implemented in the filter stage by implementing the impulse response, which has already been supplemented with zeros, using a fractional delay filter. In embodiments of the present invention, therefore, all the necessary delay values can be implemented in the frequency domain, that is to say between the Hin transformation and the Rück transformation, wherein the greatest proportion of the delay is achieved simply by a memory access control, in which case a granulation according to the block Feed is achieved or according to the time period corresponding to a block feed. If finer delays are needed, this finer delay is implemented by modifying the filter impulse response for each audio signal / speaker combination in the filter stage to insert zeros at the beginning of the impulse response. This represents a kind of delay in the time domain, which, however, according to the invention "imprinted" on the short-term spectrum in the frequency domain, so that the delay application with fast folding algorithms, such as the overlap-save algorithm or the overlap-add algorithm is consistent or within the framework given by the fast convolution can be efficiently implemented.

The present invention is particularly suitable for static sources because the static virtual sources also have static delay values for each audio-signal-speaker combination. Therefore, for every position of a virtual source the memory access control will be fixed. In addition, the impulse response for the particular loudspeaker audio signal combination in each individual block of the filter stage can be pre-set before the actual rendering algorithm is executed. For this purpose, the actually required for this audio signal speaker combination impulse response is changed so that a corresponding number of zeros at the beginning of the impulse response is inserted in order to achieve a finely resolved delay. Then this impulse response is transformed into the spectral range and stored there in a single filter. In the actual wave field synthesis rendering calculation, it is then always possible to resort to stored transfer functions of the individual filters in the individual filter blocks. Then, when a static source goes from one position to the next, it is necessary to readjust the memory access control and readjust the individual filters, but, for example, when a static source goes from one position to another, for example at 10 second intervals , already calculated in advance. The frequency domain transfer functions of the individual filters can thus be calculated in anticipation, while the static source is still rendered in its old position, so that when the static source is to be rendered at its new position, it is already stored again in the individual filter stages Transfer functions are present, which has been calculated due to an impulse response with the corresponding inserted number of zeros.

A preferred wavefield synthesis renderer device or method for operating a wavefield synthesis renderer device comprises N virtual sound sources that provide sampling values for the source signals x ₀ ... X _N-1 , and a signal processing unit composed of the Source signals x ₀ ... x _N-1 Sampling values for M loudspeaker signals y ₀ ... y _M-1 generated, wherein in the signal processing unit for each source-speaker combination a filter spectrum is stored, each source signal x ₀ ... x _{N-1 is} transformed into the spectra with a plurality of FFT calculation blocks of block length L, the FFT calculation blocks having an overlap of length (LB) and a length B feed, multiplying each spectrum by the respective filter spectra of the same source is, from which the spectra are generated, the access to the spectra is such that the speakers each with a r predetermined delay corresponding to an integer multiple of the feed B, all spectra of the same speaker i are added, from which the spectra Q _{j are} generated, and each spectrum Q _j with an IFFT calculation block in the sampling values for the M loudspeaker signals y ₀ ... y _{M-1 is} transformed.

In one implementation, the block-by-block shift of the individual spectra can be exploited to generate a delay of the loudspeaker signals y ₀ ... Y _M-1 by a targeted access to the spectra. The computational effort for this delay depends only on the targeted access to the spectra, so that no additional computing power is required for the introduction of delays, as long as the delay corresponds to an integer multiple of the feed B.

Overall, the invention thus relates to the wave field synthesis of Richteten sound sources or sound sources with directional characteristics. For real listening scenes and WFS setups, which consist of multiple virtual sources and a large number of speakers, the need to apply individual FIR filters for each combination of virtual source and loudspeaker often prevents easy implementation.

To reduce this rapidly increasing complexity, the invention proposes an efficient processing structure based on time / frequency techniques. Combining the components of a fast convolution algorithm into the structure of a WFS rendering system allows the efficient reuse of operations and intermediate results, and thus a significant increase in efficiency. Although the potential acceleration increases with the number of virtual sources and speakers, significant savings are also made for moderate-size WFS assemblies. In addition, the performance gains are relatively consistent for a wide variety of parameter choices for the filter size and block delay value. The handling of time delays, which is an inherent requirement of sound reproduction techniques, such as e.g. WFS requires a modification of the overlap-save technique. This is efficiently solved by partitioning the delay value and using frequency-domain delay lines or frequency-line-implemented delay lines.

The invention is thus not limited to the processing of directional sound sources or directional sound sources in the WFS, but is also applicable to other processing tasks that use massive multi-channel filtering with optional time delays.

According to a preferred embodiment, it is provided that the generation of the spectra takes place according to the overlap-save method. The overlap save method is a fast folding method. In this case, the input sequence x ₀ ... x _{N-1 is} decomposed into overlapping subsequences. From the formed periodic folding products (cyclic Convolution), then those parts are taken which agree with the aperiodic, fast convolution.

According to a further preferred embodiment, it is provided that the filter spectra are transformed by means of an FFT from time-discrete impulse responses. The filter spectra can be provided before the actual execution of the time-critical calculation steps, so that the calculation of the filter spectra does not influence the time-critical part of the calculation.

According to a further preferred embodiment, it is provided that each impulse response is preceded by a number of zeros in such a way that the loudspeakers are each actuated with a predetermined delay, which corresponds to the number of zeros. In this way, delays can be realized that do not correspond to an integer multiple of the feed B. The desired delay is divided into two parts: The first part is an integer multiple of the feed B, while the second part represents the rest. This second fraction is thus inevitably smaller than the feed B in the case of such a decomposition.

Fig. 1a

ein Blockschaltbild einer Vorrichtung zur Berechnung von Lautsprechersignalen gemäß einem Ausführungsbeispiel der vorliegenden Erfindung;

Fig. 1b

eine Übersichtsdarstellung zur Bestimmung der anzuwendenden Verzögerungen durch die Speicherzugriffssteuerung und die Filterstufe;

Fig. 1c

eine Darstellung einer bevorzugten Implementierung der Filterstufe, um ein gefiltertes Kurzzeitspektrum zu erhalten, wenn ein neuer Verzögerungswert einzustellen ist;

Fig. 1d

eine Übersichtsdarstellung über das Overlap-Save-Verfahren im Kontext der vorliegenden Erfindung;

Fig. 1e

eine Übersichtsdarstellung über das Overlap-Add-Verfahren im Kontext der vorliegenden Erfindung;

Fig. 2

die prinzipielle Struktur der Signalverarbeitung bei der Verwendung eines WFS-Rendering-Systems ohne frequenzabhängige Filterung mittels Verzögerung und Amplitudenskalierung (Scale&Delay) im Zeitbereich;

Fig. 3

die prinzipielle Struktur der Signalverarbeitung bei der Verwendung der Overlap & Save - Technik;

Fig. 4

die prinzipielle Struktur der Signalverarbeitung bei der Verwendung einer Frequency-Domain Delay Line gemäß der Erfindung;

Fig. 5

die prinzipielle Struktur der Signalverarbeitung mit einer Frequency-Domain Delay Line gemäß der Erfindung;

Fig. 6

eine vergleichende Darstellung des Berechnungsaufwands für verschiedene Faltungsalgorithmen;

Fig. 7

die Geometrie der Bezeichnungen, die in dieser Schrift verwendet werden;

Fig. 8a

eine Impulsantwort für eine Audiosignal-Lautsprecher-Kombination; und

Fig. 8b

eine Impulsantwort für eine Audiosignal-Lautsprecher-Kombination nach dem Einfügen von Nullen.

Further details and advantages of the invention will become apparent from the embodiments described with reference to the drawings. In these show:

Fig. 1a

a block diagram of a device for calculating loudspeaker signals according to an embodiment of the present invention;

Fig. 1b

an overview representation for determining the applicable delays by the memory access control and the filter stage;

Fig. 1c

a representation of a preferred implementation of the filter stage to obtain a filtered short-term spectrum when a new delay value is to be set;

Fig. 1d

an overview of the overlap save method in the context of the present invention;

Fig. 1e

an overview of the overlap-add method in the context of the present invention;

Fig. 2

the basic structure of the signal processing when using a WFS rendering system without frequency-dependent filtering by means of delay and amplitude scaling (scale & delay) in the time domain;

Fig. 3

the basic structure of signal processing when using the overlap & save technique;

Fig. 4

the basic structure of the signal processing when using a frequency domain delay line according to the invention;

Fig. 5

the basic structure of signal processing with a frequency domain delay line according to the invention;

Fig. 6

a comparative representation of the computational effort for different convolution algorithms;

Fig. 7

the geometry of the terms used in this font;

Fig. 8a

an impulse response for an audio signal speaker combination; and

Fig. 8b

an impulse response for an audio signal speaker combination after the insertion of zeros.

Fig. 1a shows a device for calculating loudspeaker signals for a plurality of loudspeakers, which may for example be arranged at predetermined positions in a reproduction room, using a plurality of audio sources, wherein an audio source comprises an audio signal 10. The audio signals 10 are supplied to an out-transformation stage 100, which is designed to carry out a block-by-block transformation of each audio signal into a spectral range, so that a plurality of temporally consecutive short-time spectra are obtained for each audio signal. Furthermore, a memory 200 is provided, which is designed to store a number of temporally successive short-term spectra for each audio signal. Depending on the implementation of the memory and the manner of storage, each short-term spectrum of the plurality of short-term spectra may be assigned a time-increasing time value, and the memory then stores the temporally-consecutive short-term spectra for each audio signal in association with the time values. However, the short-term spectra in the memory do not have to be arranged in chronological succession here. Instead, the short-term spectra, for example, in a RAM memory at any point be stored as long as a memory contents table is present, which identifies which time value corresponds to which spectrum, and which spectrum belongs to which audio signal.

The memory access controller is thus configured to access a specific short-term spectrum from the plurality of short-term spectra for a combination of loudspeaker and audio signal based on a delay value given for this audio-signal-loudspeaker combination. The determined short-term spectra determined by the memory access controller 600 are then applied to a filtering stage 300 for filtering the determined short-term spectra for combinations of audio signals and loudspeakers to perform filtering with a filter provided for the respective audio signal and loudspeaker combination for each Such combination of audio signal and speaker to obtain a sequence of filtered short-term spectra. The filtered short-term spectra are then supplied from the filter stage 300 to a summing stage 400 to sum the filtered short-term spectrum for a loudspeaker such that a summed short-term spectrum is obtained for each loudspeaker. The accumulated short-term spectra are then applied to a back-transformation stage 800 for block-wise back-transforming the summed short-term spectra for the loudspeakers to obtain the short-term spectra in a time range from which the loudspeaker signals can be determined. The loudspeaker signals are thus output from the back-transformation stage 800 at an output 12.

The delay values 701 are provided by a Wave Field Synthesis Operator (WFS operator) 700 for each embodiment of the invention, in which the device is a wave field synthesis device, which is fed via an input 702 for each individual audio signal and loudspeaker combination and, depending on the loudspeaker positions, ie the positions in which the loudspeakers are arranged in the reproduction room, and which are supplied via an input 703, the delay values 701 are calculated. If the device is designed for a different application than for the wave field synthesis, so z. For example, for an Ambisonics implementation or the like, an element corresponding to the WFS operator 700 will also be present, which calculates delay values for individual loudspeaker signals or calculates delay values for individual audio signal loudspeaker combinations. Depending on the implementation, the WFS operator 700 will calculate not only the delay values but also scaling values, which typically can also be considered in the filter stage 300 by a scaling factor. Thus, these can be achieved by scaling the filter coefficients used in filter stage 300 be taken into account without incurring additional calculation effort.

The memory access controller 600 may therefore be configured in a particular implementation to obtain delay values for various audio signal and speaker combinations, and to calculate an access value to the memory for each combination, as further described with reference to FIG Fig. 1b is pictured. Accordingly, as also referring to Fig. 1b The filter stage 300 may be configured to obtain delay values for various combinations of audio signal and loudspeaker to calculate therefrom a number of zeros to be considered in the impulse responses for the individual audio signals / speaker combinations. Generally speaking, the filter stage 300 is therefore configured to implement a finer granularity delay in multiples of the sample period while the memory access controller 600 is configured to provide, through efficient memory access, delays in the granularity of the feed B from the out-of-stage is applied to implement.

Fig. 1b FIG. 12 shows a sequence of functions performed by elements 700, 600, 300 of FIG Fig. 1a can be executed.

In particular, the WFS operator 700 is configured to provide a delay value D as shown in step 20 in FIG Fig. 1b is shown. In a step 21, for example, the memory access controller 600 will divide the delay value D into a multiple of the block size or the feed B and a remainder. In particular, the delay value D is equal to the product of the feed B and the multiple D _b and the remainder. Alternatively, the multiple D _b on the one hand and the remainder D _r on the other hand can also be calculated by performing an integer division, namely one integral division of the time duration corresponding to the delay value D and the time duration corresponding to the feed B. The result of the integer division is then D _b and the remainder of the integer division is D _r . Thereafter, the memory access controller 600 performs a memory access control with the multiple D _b in a step 22, as still referring to FIG Fig. 9 will be explained in more detail. The delay D _b is thus efficiently implemented in the frequency domain because it is simply implemented by random access to a particular stored short-term spectrum selected according to the delay value or the multiple D _b . In a further embodiment of the present invention where a very fine delay is desired, in a step 23, which is preferably performed in the filter stage 300, the remainder D _{r is} a multiple of the sample code T _A and a remainder D _r 'split. The sampling period T _A , still referring to FIG Fig. 8a and 8b in detail, the sample period represents between two values of the impulse response typically associated with the sample period of the discrete audio signals at the input 10 of the out-of-step stage 100 of FIG Fig. 1 matches. The multiple D _{A of} the sampling period T _A is then used in a step 24 to control the filter by inserting D _A zeroes into the impulse response of the filter. The remainder of the division in step 23, which is denoted by D _r ', is then, if an even finer delay control than is already necessary by the quantization of the sampling periods T _A , used in a step 25, where a fractional delay Filter (FD filter or a filter with a broken delay) according to D _r 'is set. Thus, the filter in which a number of zeros have already been inserted is further executed as an FD filter.

The delay achieved by controlling the filter in step 24 may be considered as a "time-domain" delay, although due to the particular implementation of the filter stage, this delay in the frequency domain is due to the particular short-term spectrum read from memory 200 using the multiple D _b . Thus, for the total delay, there is a division into three blocks, as at 26 in FIG Fig. 1b is shown. The first block is the time that corresponds to the product of D _b , which is the multiple of the block size with the block size. The second delay block is the multiple D _{A of} the sampling period T _A , that is to say a time duration which corresponds to this product D _A x T _A. This leaves a fractional delay delay or a delay residue D _r 'left over. D _r 'is smaller than T _A , and D _A x T _A is less than B, which is directly due to the two division equations adjacent to blocks 21 and 23 in FIG Fig. 1b results.

Subsequently, reference will be made to Fig. 1c Referring to a preferred implementation of filter stage 300.

In step 30, an impulse response is provided for an audio signal speaker combination. Especially for directional sound sources you will have your own impulse response for every combination of audio signal and loudspeaker. However, for other sources as well, there are different impulse responses, at least for certain combinations of audio signal and loudspeaker. In a step 31, the number of zeros to be inserted, that is, the value D _{A is} determined, as determined by step 23 in FIG Fig. 1b has been shown. Then, in a step 32, a number of zeroes equal to D _{A are} inserted in the impulse response at the beginning of the impulse response to obtain a modified impulse response. This is on Fig. 8a Referenced. Fig. 8a shows an example of an impulse response that is too short compared to a real application h (t), which has a first value in Sample 3. Thus, one may consider the period between the value t = 0 to t = 3 as the delay that a sound needs from a source to a recording location, such as a microphone or a listener. This is followed by various samples of the impulse response spaced at T _A , that is, the sampling period which is equal to the inverse of the sampling frequency. Fig. 8b shows an impulse response, namely the same impulse response after the insertion of T _A = four zeroes for the audio signal-loudspeaker combination. In the Fig. 8b The impulse response shown is thus an impulse response as obtained in step 32. This is followed in a step 33, as in Fig. 1c is shown, a transformation of this modified impulse response, ie the impulse response according to Fig. 8b performed in the spectral range. Then, in a step 34, preferably a spectrally value-wise multiplication of the determined short-term spectrum, ie the short-term spectrum, which has been read from the memory due to D _b and thus determined, with the transformed modified impulse response obtained in step 33 is performed Finally, to obtain a filtered short-term spectrum.

In the embodiment, the out-of-step stage 100 is configured to determine the sequence of short-term spectra with the feed B from a sequence of temporal samples, such that a first sample of a first block of temporal samples converted to a short-term spectrum is spaced from a first sample of a second subsequent block of temporal samples by a number of samples equal to the feed value. The feed value is thus defined by the respective first sample value of the new block, this feed value, as it still is based on the Fig. 1d and 1e is present for both the overlap save method and the overlap add method.

Moreover, to allow random storage in the memory 200, preferably, a time value associated with a short-term spectrum is stored as a block index indicating how many advancement values the first sample of the short-term spectrum is temporally distant from a reference value. The reference value is z. For example, the index 0 of the short-term spectrum at 249 in Fig. 9 ,

Moreover, the memory access means is preferably configured to determine the determined short-term spectrum based on the delay value and the time value of the determined short-term spectrum such that the time value of the determined short-term spectrum equals the integer result of a division from the time duration corresponding to the delay value and the time duration. which corresponds to the feed value is or is greater by one. In an implementation, exactly the integer result used, which is always smaller than the actually required delay. Alternatively, however, the integer result plus one could also be used, whereby these values are to a certain extent a "rounding up" of the actually required delay. In the case of a rounding up, a delay that is a bit too long is reached, but this can easily be enough for applications. Depending on the implementation, it may also be dependent on the amount of remainder, whether rounded up or rounded down. Is the rest z. B. greater than or equal to 50% of the time corresponding to the feed, so can be rounded up, so be taken by one greater value. On the other hand, if the remainder is less than 50%, it can be "rounded off", ie the exact result of the integer division can be taken. From a rounding can actually be spoken, if the rest not z. B. is also implemented by inserting zeros.

In other words, the rounding implementation described above will be useful if delay is applied only with granulation of a block length, that is, if no finer delay is achieved by inserting zeroes in an impulse response. If, on the other hand, a finer delay is achieved by inserting zeroes into an impulse response, the block offset is rounded and not rounded up to determine the block offset.

To illustrate this implementation is on Fig. 9 Referenced. Fig. 9 shows a special memory 300 having an input interface 250 and an output interface 360. Of each audio signal, so the audio signal 1, the audio signal 2, the audio signal 3 and the audio signal 4 is stored in the memory, a temporal sequence of short-term spectra with exemplary seven short-term spectra. In particular, the spectra are read into the memory so that there are always seven short-term spectra in the memory and then, when the memory is filled and another new short-term spectrum is introduced into the memory, the corresponding short-term spectrum "drops out" at the output 260 of the memory. This falling out is implemented by overwriting the memory cells, for example, or by resorting the indices to the individual memory fields accordingly, and is in Fig. 9 illustrated for illustrative purposes only. The access control accesses via an access control line 265 in order to read out certain memory fields, that is to say certain short-term spectra, which are then sent via a readout output 267 to the filter stage 300 of FIG Fig. 1a to be delivered.

For example, a particular example access control could be used to implement Fig. 4 and there for specific OS blocks, as in Fig. 9 are shown, ie for certain audio signal speaker combinations corresponding short-term spectra of the audio signals at the corresponding time value, which is a multiple of B in Fig. 9 is at 269, read. In particular, the delay value could be such that a delay of two feed lengths 2B is required for the combination OS 301. Furthermore, no delay, ie a delay of 0 through the delay value, could be required for the combination OS 304, while a delay of five feed values, ie 5B, is required for OS 302, as described in US Pat Fig. 9 is shown. As such, at some point in time, the memory access controller 265 would be in accordance with the table 270 in FIG Fig. 9 and then provide the appropriate short-term spectra, via output 267, to the filter stage, as still referring to FIG Fig. 4 is set out. The storage depth is at the in Fig. 9 As an example, seven short-term spectra are shown, so that a delay can be implemented that is at most equal to the time duration corresponding to six feed values B. This means that with the memory in Fig. 9 a value of D _b of Fig. 1b , Step 21 of a maximum of 6 can be implemented. Depending on how the delay requests and feed values B are set in a particular implementation, the memory may be larger or smaller or deeper or less deep.

In a specific implementation, as already referenced Fig. 1c The filter stage is designed to determine a modified impulse response from an impulse response of a filter provided for the combination of loudspeaker and audio signal by inserting a number of zeros at the beginning of the impulse response, the number of zeros depends on the delay value for the combination of audio signal and loudspeaker and the selected specific short-term spectrum for the combination of audio signal and loudspeaker. Preferably, the filter stage is adapted to insert a number of zeros such that a time duration equal to the number of zeros and equal to the value D _A may be less than or equal to the remainder of the integer division from the residual value D _r and Sampling time T _A of Fig. 1b is. Still referring to Fig. 1b 25, the impulse response of the filter may be an impulse response for a fractional delay filter configured to achieve a delay according to a fraction of a time between adjacent discrete impulse response values, the fraction equal to the delay value (D - D _b x B - D _A x T _A ) of Fig. 1b is, as it also from 26 in Fig. 1b is apparent.

Preferably, the memory 200 for each audio source comprises a frequency domain delay line or FDL 201, 202, 203 of FIG Fig. 4 where FDL stands for Frequency Delay Line. The FDL 201, 202, 203, which also in Fig. 9 shown schematically accordingly , allows random access to the short-term spectra stored for the corresponding source or audio signal, with access via a time value or index 269 executable for each short-term spectrum.

In addition, as it is in Fig. 4 12, the Hin transformation stage is formed with a number of transformation blocks 101, 102, 103 equal to the number of audio signals. Further, the re-transformation stage 800 is formed with a number of transformation blocks 101, 102, 103 equal to the number of speakers. In addition, a frequency-domain delay line 201, 202, 203 is provided for each audio source for each audio signal, and further the filter stage is designed such that it contains a number of individual filters 301, 302, 303, 304, 305, 306, 307, 308, 309, wherein the number of individual filters is equal to the product of the number of audio sources and the number of speakers. In other words, this means that for each audio signal-speaker combination, a separate individual filter is included for simplicity Fig. 4 labeled OS is present.

In a preferred embodiment, the down-transformation stage 100 and the back-transformation stage 800 are formed according to an overlap-save method, which will be described below with reference to FIG Fig. 1d is explained. The overlap save method is a fast folding method. Here, in contrast to the overlap-add method, the in Fig. 1e is explained, the input sequence is decomposed into overlapping subsequences, as at 36 in FIG Fig. 1d is shown. From the formed periodic folding products (cyclic folding) then those parts are taken that match the aperiodic, fast folding. The overlap save method can also be used to efficiently implement higher order FIR filters. The blocks formed in step 36 are then processed in the out-of-step stage 100 of FIG Fig. 1a each transformed, as shown at 37, to obtain the sequence of short-term spectra. Then, the short-term spectra are processed by the entire functionality of the present invention in the spectral domain, as summarized at 38. Further, the processed short-term spectra are again transformed back in a block 800, that is, the back-transformation block, as shown at 39, to obtain blocks of time values. The output signal, which results from the convolution of two finite signals, can generally be divided into three parts, the transient response, the stationary behavior and the decay behavior. In the overlap-save method, the input signal is split into segments and each segment is individually folded by cyclic convolution with a filter. The partial convolutions are then reassembled, with the decay region of each of these partial convolutions now overlapping the subsequent convolution result and thereby disturbing it. Therefore, this one will Decay range, which leads to a false result, discarded in the process. The individual stationary parts of the individual folds now collide directly with each other and thereby deliver the correct result of the folding. In general, in a step 40, disturbing components are thus discarded from the blocks of time values obtained after the block 39, and the remaining samples are combined in a step 41 in the correct time sequence to finally obtain the corresponding loudspeaker signals ,

Alternatively, both the out-of-step stage 100 and the back-to-back stage 800 may be configured to perform an overlap-add method. The overlap-add technique, also referred to as segmented convolution, is also a fast convolution method and is controlled such that an input sequence is split into actually contiguous blocks of samples at a feed B, as shown at 43 , However, these blocks become consecutive overlapping blocks due to the addition of zeros (also referred to as zero-padding) for each block, as shown at 44. The input signal is thus divided into sections of length B, which are then lengthened by zero-padding according to step 44 to bring the result of the convolution operation to a greater length. Then, the zero-padded blocks produced by step 44 are transformed in a step 45 by the out-of-step stage 100 to obtain the sequence of short-term spectra. Then, according to the processing in the block 39 of FIG Fig. 1d processing of the short-term spectra in the spectral range is performed in a step 46, to then perform a back transformation of the processed spectra in a step 47 to obtain blocks of time values. Then, in step 48, an overlapping addition of the blocks of time values takes place to obtain a correct result. The results of the individual convolutions are therefore added up where the individual convolution products overlap, and the result of the operation corresponds to the convolution of a theoretically infinitely long input sequence. In contrast to the overlap-save method, in which, as it were, "piecing together" in step 41, in the overlap-add method in Fig. 1e in step 48, an overlapping addition of the blocks of time values is performed.

Depending on the implementation, the out-transformation stage 100 and the back-transformation stage 800 are considered as individual FFT blocks, as in FIG Fig. 4 or IFFT blocks as in Fig. 4 also shown trained. In general, a DFT algorithm, that is to say a discrete Fourier transformation algorithm, which may also deviate from the FFT algorithm is preferred. In addition, other frequency domain transformation techniques, such as discrete sine transform (DST) techniques, may also be used. Discrete cosine transformation (DCT) methods, modified discrete cosine transformation (MDCT) methods or similar methods may be used, as long as they are suitable for the particular application.

As already stated by Fig. 1a The apparatus of the present invention is preferably used for a wave-field synthesis system such that there is a wave-field synthesis operator 700 that is configured to listen to any combination of speaker or audio source using a virtual position of the audio source and position of the loudspeaker to calculate the delay value, on the basis of which the memory access controller 600 and the filter stage 300 can then operate.

There are several approaches to the generation of directional sound sources using directional wave synthesis. In addition to experimental results, most approaches are based on expansion or evolution of the sound field into circular or spherical harmonics. The approach presented here also uses expansion of the sound field of the virtual source into circular harmonics to obtain a drive function for the secondary sources. This drive function is also referred to below as WFS operator.

Fig. 7 shows the geometry of the labels used in the general equations of wave field synthesis, ie in the wave field synthesis operator. In summary, for directional sources, the WFS operator is frequency-dependent, thus having a dedicated amplitude and phase corresponding to a frequency-dependent delay for each frequency. For rendering (or "rendering") of arbitrary signals, this frequency dependent operation requires filtering of the time domain signal. This filtering operation can be implemented as FIR filtering, where the FIR coefficients are determined by suitable design methods from the frequency-dependent WFS operator. The FIR filter also contains a delay, the main part of the delay (delay) being determined by the signal propagation time between the virtual source and the loudspeaker and thus being frequency-independent, ie constant. Preferably, this frequency-dependent delay is achieved by the in conjunction with the Fig. 1a-1e described procedures edited. However, the present invention may also be applied to alternative implementations where the sources are non-directional, or where there are only frequency independent delays, or where fast convolution is generally to be employed along with a delay between certain audio signal speaker combinations.

The following is an exemplary description of the wave field synthesis process. Alternative descriptions and designs are also known. The sound field of the primary source is generated in the region y <y _L by using a linear distribution of secondary monopole sources along x (black dots).

Using the geometry of Fig. 7 is the two-dimensional Rayleigh-I integral in the frequency domain given by $$\begin{array}{l}{P}_R\left({\overrightarrow{r}}_R,\overrightarrow{r},\omega \right)=\frac{1}{2\pi }\underset{-\infty }{\overset{\infty }{\int }}{\mathit{j\omega \rho \nu }}_{\tilde{n}}\left(\tilde{r},\omega \right)\\ x\left(-\mathit{j\pi }{\mathrm{<figure-callout\; id="0"\; label="H"\; filenames="imgf0014.png"\; state="\{\{state\}\}">H</figure-callout>}}_0^{\left(2\right)}\left(\frac{\omega }{c}\right)\left|{\overrightarrow{r}}_R-\overrightarrow{r}\right|\right)\mathit{dx}\end{array}$$

der Primärquelle Ψ an den Positionen der Sekundärquellen bekannt sein gemäß ihrer Normalen n . In Gleichung (1), ist ω die Winkelfrequenz, c die Schallgeschwindigkeit und ${\mathrm{H}}_{\mathrm{0}}^{\left(\mathrm{2}\right)}\left(\frac{\mathrm{\omega }}{\mathrm{c}}\left|{\overrightarrow{\mathrm{r}}}_{\mathrm{R}}-\overrightarrow{\mathrm{r}}\right|\right)$

ist die Hankel-Funktion zweiter Art der Ordnung 0. Der Weg von der Primärquellenposition zu der Sekundärquellenposition ist bezeichnet durch r . Gleichartig dazu ist r _R der Weg von der Sekundärquelle zu dem Empfänger R. Das zweidimensionale Schallfeld, das durch eine Primärquelle ψ mit jeder beliebigen Richtcharakteristik abgestrahlt wird, kann durch eine Expansion in kreisförmige Harmonische beschrieben werden. $${\mathrm{P}}_{\mathrm{\psi }}\left(\overrightarrow{\mathrm{r}},\mathrm{\omega }\right)=\mathrm{S}\left(\mathrm{\omega }\right){\displaystyle \sum _{\mathrm{v}=\mathrm{\infty }}^{\mathrm{\infty }}}{\stackrel{\mathrm{‿}}{\mathrm{C}}}_{\mathrm{m}}^{\left(\mathrm{2}\right)}\left(\mathrm{\omega }\right){\mathrm{H}}_{\mathrm{\nu }}^{\left(\mathrm{2}\right)}\frac{\mathrm{\omega }}{\mathrm{c}}\left|\overrightarrow{\mathrm{r}}\right|{\mathrm{e}}^{\mathrm{j\nu a}},$$

wobei S(ω) das Spektrum der Quelle ist und α der Azimuthwinkel des Vektors r . ${\stackrel{‿}{C}}_v^{\left(2\right)}$

(w) sind die Kreisförmige-Harmonische-Ausdehnungskoeffizienten der Größenordnung v. Unter Verwendung der Bewegungsgleichung ist die WFS-Sekundärquellenansteuerfunktion Q (....) gegeben als $$-{\mathit{j\omega \rho \nu }}_{\overrightarrow{n}}=\frac{\partial P_{\psi }\left(\overrightarrow{r},\omega \right)}{\partial \overrightarrow{n}}\equiv Q\left(\dots \right)\mathrm{.}$$

It says that the switching pressure P _R ( r _R , r , ω) of a primary switching source at the receiver position R can be generated using a linear distribution of secondary monopole line noise sources with y = y _L. For this purpose, the speed must be $V_{\overrightarrow{n}}\left(\overrightarrow{r},\omega \right)$

the primary source Ψ be known at the positions of the secondary sources according to their normals n , In equation (1), ω is the angular frequency, c is the speed of sound and ${\mathrm{<figure-callout\; id="0"\; label="H"\; filenames="imgf0014.png"\; state="\{\{state\}\}">H</figure-callout>}}_{\mathrm{0}}^{\left(\mathrm{2}\right)}\left(\frac{\mathrm{\omega }}{\mathrm{c}}\left|{\overrightarrow{\mathrm{r}}}_{\mathrm{R}}-\overrightarrow{\mathrm{r}}\right|\right)$

is the second order Hankel function of order 0. The path from the primary source position to the secondary source position is designated by r. The same is true r _{R is} the path from the secondary source to the receiver R. The two-dimensional sound field radiated by a primary source ψ with any directional characteristic can be described by expansion into circular harmonics. $${\mathrm{P}}_{\mathrm{\psi }}\left(\overrightarrow{\mathrm{r}},\mathrm{\omega }\right)=\mathrm{S}\left(\mathrm{\omega }\right){\displaystyle \underset{\mathrm{v}=\mathrm{\infty }}{\overset{\mathrm{\infty }}{\Sigma }}}{\stackrel{\mathrm{<figure-callout\; id="2"\; label="‿\; m"\; filenames="imgf0013.png,imgf0014.png"\; state="\{\{state\}\}">‿\; </figure-callout>}}{\mathrm{C}}}_{\mathrm{m}}^{\mathrm{}}\left(\mathrm{\omega }\right){\mathrm{<figure-callout\; id="2"\; label="H\; \nu "\; filenames="imgf0013.png,imgf0014.png"\; state="\{\{state\}\}">H\; </figure-callout>}}_{\mathrm{\nu }}^{\mathrm{}}\frac{\mathrm{\omega }}{\mathrm{c}}\left|\overrightarrow{\mathrm{r}}\right|{\mathrm{e}}^{\mathrm{j\nu a}}.$$

where S (ω) is the spectrum of the source and α is the azimuth angle of the vector r , ${\stackrel{‿}{C}}_v^{\left(2\right)}$

(w) are the circular-harmonic expansion coefficients of the order of magnitude v. Using the equation of motion, the WFS secondary source drive function Q (....) is given as $$-{\mathit{j\omega \rho \nu }}_{\overrightarrow{n}}=\frac{\partial P_{\psi }\left(\overrightarrow{r},\omega \right)}{\partial \overrightarrow{n}}\equiv Q\left(...\right)\mathrm{,}$$

In order to obtain viable synthesis operators, two assumptions are made: First, real speakers behave more like point sources if the size of the speaker is small compared to the radiated wavelength. Therefore, the secondary source driving function secondary point sources should be used instead of line sources. Second, only the efficient processing of the W FS drive function is considered here. While the calculation of the Hankel function is relatively expensive, the near-field directivity is of lesser importance from a practical point of view.

As a result, only the far field approximation of the Hankel function is applied to the secondary and primary source descriptions (1) and (2). This leads to the secondary source drive function $$\begin{array}{c}Q\left({\overrightarrow{r}}_R,\overrightarrow{r},\omega ,\alpha \right)=j\frac{\sqrt{\left|{\overrightarrow{r}}_R-\overrightarrow{r}\right|}}{\pi }\cos \phi \frac{e^{-j\frac{\omega }{c}\left|\overrightarrow{r}\right|}}{\sqrt{\left|\overrightarrow{r}\right|}}S\left(\omega \right)\\ \underset{G\left(\omega ,a\right)}{\underset{\}}{x{\displaystyle \underset{\nu =-\infty }{\overset{\infty }{\Sigma }}}\stackrel{‿}{C}\frac{\left(2\right)}{\nu }\left(\omega \right)j^{\nu }{e}^{\mathit{jva}}}}\end{array}$$

Consequently, the synthesis integral can be expressed as $$P_R\left({\overrightarrow{r}}_R,\overrightarrow{r},\omega \right)=\underset{-\infty }{\overset{\infty }{\int }}Q\left({\overrightarrow{r}}_R,\overrightarrow{r},\omega ,\alpha \right)\frac{e^{-j\frac{\omega }{c}\left|\overrightarrow{r}\right|}}{\overrightarrow{r}}\mathit{dx}$$

ein Verzögerungsterm $$D\left(\overrightarrow{r},\omega \right)=e^{-j\frac{\omega }{c}\left|\overrightarrow{r}\right|},$$

der einer frequenzunabhängigen Zeitverzögerung von $\frac{\left|\overrightarrow{r}\right|}{c}$

entspricht, und eine konstante Phasenverschiebung von j an das Sekundärquellensignal angelegt.For a virtual source with ideal monopole characteristics, the directivity term of the source driving function is simplified to G (ω, α) = 1. In this case, only one gain will be made $$A_M\left({\overrightarrow{r}}_R,\overrightarrow{r}\right)=\frac{1}{\pi }\sqrt{\frac{\left|{\overrightarrow{r}}_R-\overrightarrow{r}\right|}{\left|\overrightarrow{r}\right|}}\cos \phi$$

a delay term $$D\left(\overrightarrow{r},\omega \right)=e^{-j\frac{\omega }{c}\left|\overrightarrow{r}\right|}.$$

that of a frequency independent time delay of $\frac{\left|\overrightarrow{r}\right|}{c}$

and a constant phase shift of j is applied to the secondary source signal.

In addition to the synthesis of monopole sources, a common WFS system makes it possible to render planar wavefronts, called plane waves. These may be considered monopole sources arranged at an infinite distance. As in the case of monopole sources, the resulting synthetic operator consists of a static filter, a gain factor, and a time delay.

For complex directional characteristics, the gain factor A (...) depends on the directional characteristic, the orientation and the frequency of the virtual source as well as on the positions of the virtual and secondary sources. Thus, the synthesis operator contains a non-trivial filter specific to each secondary source $$A_D\left({\overrightarrow{r}}_R,\overrightarrow{r},\omega ,\alpha \right)=\frac{j}{\pi }\sqrt{\frac{\left|{\overrightarrow{r}}_R-\overrightarrow{r}\right|}{\left|\overrightarrow{r}\right|}}\cos \phi G\left(\omega ,\alpha \right)$$

As in the case of basic source types, the delay due to the propagation time between virtual and secondary sources can be extracted from (4) $$D\left(\overrightarrow{r},\omega \right)=e^{-j\frac{\omega }{c}\left|\overrightarrow{r}\right|}$$

For practical processing, time-discrete filters for the directional characteristics of the frequency response (8) must be determined. Due to their ability to approximate arbitrary frequency responses and their inherent stability, only FIR filters are considered here. These directivity filters are hereafter referred to as h _{m, n} [k] , where n = 0, ..., M -1 denotes the virtual source index, n = 0,..., M- 1 is the speaker index and k is a time domain index. K is the order of magnitude of the directivity filter. Since such filters are required for any combination of N virtual sources and M loudspeakers, the generation must be relatively efficient.

A simple window (or frequency scan design) is used here. The desired frequency response (9) is evaluated at K + 1 equidistantly sampled frequency values in the interval 0 ≤ ω < 2π . The discrete filter coefficients h _{m, n} [k], k = 0, ..., K are obtained by an inverse discrete Fourier transform (IDFT) and the application of a suitable window function w [k] to reduce the Gibbs phenomenon which is caused by the clipping of the impulse response. $$H_{m.n}\left[k\right]=w\left[k\right]\mathit{IDFT}\left\{A_D\left({\overrightarrow{r}}_R,\overrightarrow{r},\omega ,\alpha \right)\right\}$$

(ω) sind für alle Ansteuerfunktionen einer gegebenen virtuellen Quelle identisch und werden folglich nur einmal berechnet. Die Richtwirkungsfilter h_m,n [k] führen Synthesefehler auf zwei Weisen ein. Einerseits führt die begrenzte Filtergrößenordnung zu einer unvollkommenen Näherung von A_D ( r _R , r ,ω,α). Andererseits muss die unendliche Summierung von (4) durch eine finite Grenze ersetzt werden. Als Folge kann die Strahlbreite der generierten Richtcharakteristika nicht unendlich schmal werden.The implementation of this design method allows for several optimizations. First, the conjugate symmetry of the frequency response A _D ( r _R , r , ω, α), this function only has to be evaluated for about half of the halftone dots. Second, several parts of the secondary source drive function, eg the expansion coefficients ${\stackrel{‿}{C}}_v^{\left(2\right)}$

(ω) are identical for all drive functions of a given virtual source and are therefore calculated only once. The directivity filters h _{m, n} [ k ] introduce synthesis errors in two ways. On the one hand, the limited filter order leads to an imperfect approximation of A _D ( r _R , r , Ω, α). On the other hand, the infinite summation of (4) must be replaced by a finite limit. As a result, the beam width of the generated directivity characteristics can not become infinitely narrow.

Fig. 2 shows the basic structure of signal processing when using a simple WFS operator based on a scale & delay operation. Shown is the signal processing structure of WFS processing systems for the synthesis of basic primary source types. The secondary source drive signals can be determined by processing a scaling and a delay operation for each primary source-secondary source combination (S & D = Scaling and Delaying) and a static input filter H (ω).

WFS rendering is commonly implemented as a discrete-time processing system. It consists of two general tasks: computation of the synthesis operator and application of this operator to the time-discrete source signals. The latter is referred to below as WFS processing.

The effect of the synthetic operator on overall complexity is typically low because it is relatively rarely calculated. If the source properties change only discretely, the operator is calculated as needed. For continuously changing source properties, e.g. in the case of moving sound sources, it is typically sufficient to compute these values on a coarse grid and use simple interpolation techniques in between.

In contrast, applying the synthesis operator to the source signals must be done at the full audio sampling rate. Fig. 2 shows the structure of a typical WFS rendering system with N virtual sources and M speakers. As shown in Section 2.2, the secondary source driving function consists of a fixed prefilter H (ω) = j, and the application of a time delay D ( r , ω) and a scaling factor A _M ( r _R , r ). Since H (ω) is independent of the positions of the source and the loudspeaker, it is applied to the input signals before being stored in a time domain delay line. Using this delay line, a component signal is calculated for each combination of a virtual source and a loudspeaker, which is represented by a scale-and-delay operation (S & D). In the simplest case, the delay value is rounded down to the nearest integer multiple of the sample period and applied to the delay line as an indexed access. In the case of moving source objects, more complex algorithms are required to interpolate the source signal at arbitrary positions between samples. Finally, the component signals for each loudspeaker are accumulated to form the drive signals.

The number of scaling and delaying operations is formed by the product of the number of virtual sources N and the number of loudspeakers M. Thus, this product typically reaches high values. Consequently, the scaling and delaying operation is the most power-critical part of most WFS systems, even if only integer delays are used.

Fig. 3 shows the basic structure of the signal processing when using the overlap & save technique. The overlap save method is a fast folding method. In this case, in contrast to the overlap-add method, the input sequence x [n] is decomposed into overlapping subsequences. From the formed periodic folding products (cyclic folding) then those parts are taken that match the aperiodic, fast folding.

Based on Fig. 2 It has been explained that the scaling and delaying operation applied to each combination of a virtual source and a loudspeaker is highly performance critical to traditional WFS rendering systems. For sound sources having a directional characteristic, an additional filtering operation, typically implemented as an FIR filter, is required for each such combination. Considering the computational burden of FIR filters, the resulting complexity is not economically feasible for most real WFS rendering systems.

To substantially reduce the required computational resources, the invention proposes a signal processing scheme based on two interacting effects.

The first effect concerns the fact that the efficiency of FIR filters can often be increased by using fast convolution techniques in the transform domain , such as overlap-save or overlap-add. Generally, these algorithms transform segments of the input signal into the frequency domain by fast Fourier transform (FFT) techniques, perform convolution due to frequency domain multiplication, and transform the signal back into the time domain. Although the actual performance is highly hardware dependent, the filter order where transform based filtering becomes more efficient than direct convolution is typically between 16 and 50. For overlap add algorithms and overlap save algorithms, the forward and inverse FFT operations form the Much of the computational effort.

Preferably, only the overlap save method is considered since it does not require the addition of components from adjacent output blocks. In addition to the reduced arithmetic complexity compared to overlapp-add, this property results in a simpler control logic for the proposed processing scheme.

Another embodiment for reducing computational effort utilizes the structure of the WFS processing scheme. On the one hand, here is every input signal for a large Number of delay and filter operations used. On the other hand, the results are summed for a large number of sound sources for each speaker. Thus, partitioning the signal processing algorithm, which performs common operations only once for each input or output signal, promises great efficiencies. In general, such partitioning of the WFS rendering algorithm provides significant performance improvements for moving sound sources from basic source types.

When transform-based fast convolution is used to render directional sound sources, the forward and inverse Fourier transform operations are obvious candidates for this partitioning. The resulting processing scheme is in Fig. 3 shown. The input signals x _n [ k ] , n = 0,..., N -1 are segmented into blocks and transformed into the frequency domain using fast Fourier transforms (FFT). The frequency domain representation is used several times to convolute the individual loudspeaker signal components by an overlap save operation, ie a complex multiplication. The loudspeaker signals are calculated in the frequency domain by accumulating the component signals of all sources. Finally, a fast inverse Fourier transform (IFFT) of these blocks and concatenation according to the overlap-save scheme yields the loudspeaker drive signals y _m [ k] , m = 0,..., M -1 in the time domain. In this way, the most performance critical parts of the transform domain convolution, namely the FFT and IFFT operations, are performed only once for each source or speaker.

Fig. 4 shows the basic structure of the signal processing in the use of a frequency domain delay line according to the invention. Shown is a block-based transform domain WFS signal processing scheme. OS stands for overlap-save and FDL stands for Frequency-Domain Delay Line.

Fig. 4 shows a specific implementation of the embodiment of Fig. 1a which has a matrix-shaped structure, wherein the out-of-step stage 100 comprises individual FFT blocks 101, 102, 103. In addition, the memory 200 includes various frequency-domain delay lines 201, 202, 203, which may be accessed via the memory access controller 600 shown in FIG Fig. 4 is not shown, to determine for each filter stage 301-309 the correct short-term spectrum and the corresponding filter stage at a given time, as shown by Fig. 9 has been set out. In addition, the summer 400 includes schematically summed summers 401-406, and the re-transform stage 800 includes individual ones IFFT blocks 801, 802, 803 to finally receive the loudspeaker signals. Preferably, both the blocks 101-103 and 801-803 are designed to perform the correspondingly necessary processing steps before the actual transformation or after the actual back transformation, which are required by fast folding methods, such as the overlap-save method or the overlap-add method.

As based on Fig. 7 has been explained, the WFS operator determines a single delay for each source-speaker combination. Although the proposed signal processing scheme allows efficient multi-channel convolution, the application of these delays requires detailed consideration. In the conventional time domain algorithm, integer-value sample delays can be implemented by accessing a time domain delay line with little effect on overall complexity. In the frequency domain, a time delay can not be implemented in the same way.

Conceptually, any time delay can be readily incorporated into the FIR directivity filter. However, due to the large range of delay value in a typical WFS system, this approach results in very large filter lengths and hence large FFT block sizes. On the one hand, this significantly increases the computational effort and the storage requirements. On the other hand, the latency to form input blocks is unacceptable for many applications due to the blocking delay required for such large FFT sizes.

For this reason, a processing scheme based on a frequency-domain delay line and a partitioning of the delay value is proposed here. Similar to the conventional overlap save method, the input signal is segmented into overlapping blocks of size L and a feed (or delay block size) of B between adjacent blocks. The blocks are transformed into the frequency domain and are denoted by X _n [ l ], where n denotes the source and l is the block index. These blocks are stored in a structure that allows indexed access of the form X _n [li ] to the most recent frequency domain blocks. This data structure is conceptually identical to Frequency Domain Delay Lines used in the context of partitioned convolution.

The delay value D, given in samples, is partitioned into a multiple of the block delay magnitude and a remainder D _r and D _r ', respectively. $$D=D_{b}B+D_r\mathit{With}0\le D_r\le B-1.D_b\in \mathrm{N}\mathrm{,}$$

The block delay D _b is applied as an indexed access in the frequency domain delay line. In contrast, the residual part is included in the directivity filter h _{m, n} [ k ], which is formally expressed by a convolution with the delay operator δ (k - D _r ) $$H_{m.n}^d\left[k\right]=H_{m.n}\left[k\right]*\delta \left(k-D_r\right)\mathrm{,}$$

durch eine FFT erhalten.For integer delay values, this operation corresponds to prefixing h _{m, n} [ k ] with D _r zeros. The resulting filter is padded with zeroes according to the requirements of the overlap save operation. Thereafter, the frequency domain filter representation $H_{m.n}^d$

obtained by an FFT.

wobei eine elementweise komplexe Multiplikation bezeichnet. Die Frequenzbereichsdarstellung des Ansteuersignals für den Lautsprecher m wird bestimmt durch Akkumulieren der entsprechenden Komponentensignale, was implementiert wird als eine komplexwertige Vektoraddition $$Y_m\left[l\right]=N-1{\displaystyle \sum _{n=0}^{N-1}}{C}_{m,n}\left[l\right]\mathrm{.}$$

The frequency domain representation of the signal component from the source n to the loudspeaker m is calculated as $$C_{m.n}\left[l\right]=H_{m.n}^d\cdot X_n\left[l-D_b\right]$$

where an elementwise complex multiplication denotes. The frequency domain representation of the drive signal for the loudspeaker m is determined by accumulating the corresponding component signals, which is implemented as a complex valued vector addition $$Y_m\left[l\right]=N-1{\displaystyle \underset{n=0}{\overset{N-1}{\Sigma }}}{C}_{m.n}\left[l\right]\mathrm{,}$$

The remainder of the algorithm is identical to the usual overlap-save algorithm. The blocks Y _m [ l ] are transformed into the time domain, and the speaker drive signals y _m [k] are formed by deleting a predetermined number of samples from each time domain block. This signal processing structure is in Fig. 4 shown schematically.

haben.The lengths of the transformed segments and the displacement between adjacent segments follow from the derivation of the conventional overlap-save algorithm. A linear convolution of a segment of length L with a sequence of length P, L <P, corresponds to a complex multiplication of two frequency domain vectors of size L and yields L-P + 1 output samples. Thus, the input segments must be shifted by this amount, hereinafter referred to as B = LP +1. Conversely, to obtain B output samples from each input segment for convolution with a FIR filter of order K (length P = K + 1), the transformed segments must have a length of $$L=K+B\mathrm{,}$$

to have.

gemäß (12), führt die erforderliche Größenordnung für $h_{m,n}^d\left[k\right]$

zu K' = K + B - 1. Dies liegt daran, dass $h_{m,n}^d$

höchstens B - 1 Nullen vorangestellt werden, was der Maximalwert für D_r (11) ist. Somit ist die erforderliche Segmentlänge für den vorgeschlagenen Algorithmus gegeben durch $$L=K+2B-1.$$

If the integer part of the residual D _{r of} the delay is embedded in the filter $H_{m.n}^d\left[k\right]$

According to (12), the required order of magnitude for $H_{m.n}^d\left[k\right]$

to K '= K + B - 1. This is because $H_{m.n}^d$

at most B - 1 zeros, which is the maximum value for D _r (11). Thus, the required segment length for the proposed algorithm is given by $$L=K+2B-1.$$

Hier werden nur FIR-FD-Filter berücksichtigt, da dieselben ohne weiteres in den vorgeschlagenen Algorithmus integriert werden können. Zu diesem Zweck wird die Restverzögerung D_r partitioniert in einen Ganzzahlteil D_int und einen Bruchteilverzögerungswert d, wie es bei dem FD-Filterentwurf üblich ist. Der Ganzzahlteil wird integriert in $h_{m,n}^d\left[k\right]$

durch Voranstellen von D_int Nullen zu h_m,n [k]. Der Bruchteilverzögerungswert wird angelegt an $h_{m,n}^d\left[k\right]$

durch Falten desselben mit einem FD-Filter, das für diesen Bruchteilwert d entworfen ist. Somit erhöht sich die erforderliche Größenordnung von $h_{m,n}^d\left[k\right]$

um die Größenordnung des FD-Filters K_FD und die erforderliche Blockgröße L (16) ändert sich zu $$L=K+K_{\mathit{FD}}+2B-1.$$

So far, only integer sample delay values D have been considered. However, the proposed processing scheme can be extended to arbitrary delay values by including a FD (Fractional Delay) filter, a so-called directivity filter $H_{m.n}^d\left[k\right]\mathrm{,}$

Only FIR FD filters are considered here, as they can be easily integrated into the proposed algorithm. For this purpose, the residual delay D _{r is} partitioned into an integer part D _int and a fractional delay value d, as is usual in the FD filter design. The integer part is integrated in $H_{m.n}^d\left[k\right]$

by prefixing D _int zeros to h _{m, n} [ k ]. The fractional delay value is applied $H_{m.n}^d\left[k\right]$

by folding it with an FD filter designed for this fractional value d. Thus, the required order of magnitude increases $H_{m.n}^d\left[k\right]$

by the magnitude of the FD filter K _FD and the required block size L (16) changes to $$L=K+K_{\mathit{FD}}+2B-1.$$

However, the benefits of using any delay values are very limited. It has been found that fractional delay values are only required for moving virtual sources. However, these do not have a positive effect on the quality of static sources. On the other hand, the synthesis of moving directional or directional sound sources would require a constant temporal variation of synthesis filters whose design would dominate the overall complexity of rendering in a simple implementation.

Fig. 5 shows the basic structure of the signal processing with a frequency domain delay line according to the invention. The source signal x _k is transformed into overlapping FFT calculation blocks 502 of the block length L into the spectra, the FFT calculation blocks having an overlap of length (LB) and a length B feed.

In a next step, the fast convolution after the overlap-save method (OS) and the backward transformation with an IFFT into the loudspeaker signals y ₀ ... Y _{M-1 are} performed at the step 503. The decisive factor here is the way in which access to the spectra takes place. Exemplary accesses 504, 505, 506 and 507 are shown in the figure. Based at the time of access 507, the accesses 504, 505 and 506 are in the past.

Now, when the speaker 511 is accessed with the access 507 and the speakers 510, 512 are simultaneously driven with the access 506, it appears to the listener as if the speaker signals of the speakers 510, 512 are delayed from the speaker signal of the speaker 511 , The same applies to the access 505 and the loudspeaker signals of the loudspeakers 509, 513 as well as to the access 504 and the loudspeaker signals of the loudspeakers 508, 514.

In this way, each individual speaker can be controlled with a delay which corresponds to a multiple of the block feed B. If a further delay is to be provided which is less than the block feed B, then this can be achieved by prefixing the respective impulse response of the filter which is the subject of the overlap-save operation.

Fig. 6 a - d shows a comparative representation of the computational effort for different convolution algorithms. Shown is a complexity comparison of 3 different Directional sound sources or sound sources with directional characteristic processing algorithms. The number of instructions is shown in each case in order to calculate a single sample for all loudspeaker signals. The default parameters are N = 16, M = 128, K = 1023, B = 1024. For the transform-based algorithms, the proportionality constant for the FFT complexity is set to p = 3.

To evaluate the potential increase in efficiency gained by the proposed processing structure, a performance comparison based on the number of arithmetic instructions is given here. It must be clear that this comparison can give only rough estimates of the relative performance of the different algorithms. The actual performance may differ due to the characteristics of the actual hardware architecture. In particular, the performance characteristics of the involved FFT operations differ significantly, depending on the library used, the actual FFT sizes, and the hardware. In addition, the memory performance of the hardware used can have a significant impact on the efficiency of the algorithms being compared. For this reason, the memory requirements for the filter coefficients and the delay line structures, which are the main sources of memory consumption, are also noted.

The main parameters that determine the complexity of a directional sound source processing algorithm are the number of virtual sources N, the number of loudspeakers M , and the filtering order of the directional filter K. For processes based on fast convolution, the Displacement between adjacent input blocks, also referred to as block delay B, performance and memory requirements. In addition, the blockwise operation of the fast convolution algorithms introduces an implementation latency of B-1 samples. The maximum allowable delay value, referred to as D _max , given as a number of samples, affects the amount of memory required for the delay line structures.

N(D_max+K) MN(K+B) vorgeschlagenes Verarbeitungs-schema $$\frac{K+2B-1}{B}\left[\begin{array}{l}\left(M+N\right)p\\ {\log }_2\left(K+2B-1\right)+M\left(4N-1\right)\end{array}\right]$$

$$N\left[\frac{D_{\max }}{B}\right]\left(K+2B-1\right)$$

MN(K+2B-1) Three different algorithms are compared: linear convolution, filter-wise fast convolution and the proposed processing structure. The method based on linear convolution performs NM time domain convolutions of order K. This amounts to NM ( 2K + 1) commands per sample. In addition, M (N -1 ) real additions are needed to accumulate the speaker drive signals. The memory required for a single delay line is D _max + K floating point values. Each of the MN FIR filters h _{m, n} [ k ] requires K + 1 floating-point memory words. These performance figures are summarized in the following table. The table shows a power comparison for wave field synthesis signal processing schemes for directional sound sources. The number of instructions is given for the calculation of one sample for all speakers. The memory requirements are specified as numbers of floating-point values. algorithm commands Verzögerungslei-tung storage filter storage linear convolution M [N (2 K +1) + (N- 1)] N (D _max + K) MN (K +1 ) fast filter-wise folding $$M\left[N\frac{K+B}{B}\left(2p{\mathrm{<figure-callout\; id="2"\; label="log"\; filenames="imgf0013.png,imgf0014.png"\; state="\{\{state\}\}">log</figure-callout>}}_2\left(K+B\right)+3\right)+N-1\right]$$

N (D _max + K) MN (K + B) proposed processing scheme $$\frac{K+2B-1}{B}\left[\begin{array}{l}\left(M+N\right)<figure-callout\; id="2"\; label="\; p\; log"\; filenames="imgf0013.png,imgf0014.png"\; state="\{\{state\}\}">\; </figure-callout>p& \\ {\log }_{}{}_2\left(K+2B-1\right)+M\left(4N-1\right)\end{array}\right]$$

$$N\left[\frac{D_{\mathrm{Max}}}{B}\right]\left(K+2B-1\right)$$

MN (K + 2 B- 1)

The second algorithm, called filter-wise linear convolution, calculates the MN FIR filters separately using the overlap-save fast convolution method. According to (15), the size of the FFT blocks is to calculate B samples per block, L = K + B. For each filter, a real-valued FFT of size L and an inverse FFT of the same size are performed. A command count of pLlog ₂ (L) is assumed for a forward or inverse FFT of size L, where p is a proportionality constant that depends on the actual implementation. For p, a value between 2.5 and 3 can be assumed.

für einen einzigen Ausgangsabtastwert auf allen Lautsprechersignalen. Ähnlich in dem direkten Faltungsalgorithmus beläuft sich der Aufwand für die Akkumulation der Lautsprechersignale M(N-1) auf Befehle. Der Verzögerungsleitungsspeicher ist identisch mit dem linearen Faltungsalgorithmus. Im Gegensatz dazu sind die Speicheranforderungen für die Filter erhöht aufgrund der Null-Auffüllungen der Filter h_m,n [k] vor der Frequenztransformation. Es ist anzumerken, dass eine Frequenzbereichsdarstellung eines realen Filters der Länge L in L reellwertigem Gleitkommawerten gespeichert werden kann aufgrund der Symmetrie der transformierten Sequenz.Since the frequency transformations of real-valued sequences are symmetric, the complex vector multiplication of length L performed in the overlap-save method requires about L / 2 complex multiplications. Since a single complex multiplication is implemented by 6 arithmetic instructions, the overhead for vector multiplication is 3L instructions. Thus, filtering requires the use of the overlap-save method $MN\frac{K+B}{B}\left[2p{\log }_2\left(K+B\right)+3\right]$

for a single output sample on all speaker signals. Similarly in the direct convolution algorithm, the overhead of accumulating the loudspeaker signals M ( N- 1) amounts to commands. The delay line memory is identical to the linear convolution algorithm. In contrast, the memory requirements for the filters are increased due to the zero fillings of the filters h _{m, n} [ k ] before the frequency transformation. It should be noted that a frequency domain representation of a real filter of length L may be stored in L real floating point values due to the symmetry of the transformed sequence.

Da die Frequency-Domain Delay Line das Eingangssignal in Blöcken der Größe L speichert, mit einer Verschiebung von B, ist die Anzahl von Speicherpositionen, die für ein einzelnes Eingangssignal erforderlich ist, $\left[\frac{D_{\max }}{B}\right]\left(K+2B-1\right)\mathrm{.}$

Gleichartig dazu erfordert ein frequenztransformiertes Filter K+2B-1 Speicherwörter.For the proposed efficient processing scheme, the block size for a block delay B is L = K + 2B -1 (16). Thus, a single FFT or inverse FFT operation requires p (K + 2B-1) log ₂ (K + 2B-1) instructions. However, only N forward and M inverse FFT operations are required for each audio block. The complex multiplication and addition are both performed on the frequency domain representation and require 3 (K + 2B-1) and K + 2B-1 instructions, respectively, for each symmetric frequency domain block of length K + 2B-1. Since each processed block B gives output samples, the total number of instructions for a sample clock iteration is $\frac{K+2B-1}{B}\left[\left(M+N\right)p{\log }_2\left(K+2B-1\right)+M\left(4N-1\right)\mathrm{,}\right]$

Since the frequency-domain delay line stores the input signal in blocks of size L , with a shift of B, the number of memory positions required for a single input signal is $\left[\frac{D_{\mathrm{Max}}}{B}\right]\left(K+2B-1\right)\mathrm{,}$

Similarly, a frequency-transformed filter K + 2B-1 requires memory words.

To assess the relative performance of these algorithms, an exemplary wave-field synthesis processing system for 16 virtual sources, 128 speaker channels, directivity filters of the order of 1023, and a block delay of 1024 is assumed. Each parameter is varied separately to assess its impact on overall complexity.

Fig. 6a As shown, the efficiency of the filter-wise fast convolution algorithm exceeds that of the linear convolution algorithm by an almost constant factor. The efficiency gain of the proposed algorithm as compared to the filter-wise fast convolution increases as N increases, thereby rapidly achieving a relatively constant ratio. It is noteworthy that the proposed algorithm is more efficient even for a single source. However, it only requires M + N = 129 transformations of size K + 2B-1 compared to 2MN = 256 for filter-wise fast convolution. This difference is not amortized by the larger block size and the increased multiplication and addition overhead required by the proposed algorithm.

The influence of the number of speakers is in Fig. 6b shown. As expected from the complexity analysis, the functions are qualitatively very similar to Fig. 6a , Thus, the proposed processing structure achieves significant complexity reduction even for small to medium sized speaker configurations.

The effect of the magnitude of the directivity filters is in Fig. 6c examined. As inherent in fast convolution algorithms, their performance improvement over the linear convolution increases with the filter order. It is observed that the break-even where filter-wise fast folding is more efficient than direct folding is between 31 and 63. In contrast, the efficiency of the proposed algorithm is much higher regardless of the filter size order. In particular, the break-even where linear convolution would become more efficient is much lower than for fast convolution. This is due to the fact that the number of FFT and IFFT operations, which is the main complexity in the case of a filter-wise fast convolution, is substantially reduced by the proposed processing scheme. It should be noted that in this experiment the block delay size B is chosen to be proportional to the filter length (actually B = K + 1), as this choice has been found to be reasonable for the overlap save algorithm.

In Fig. 6d the effects of the block delay quantity B for a fixed filter order K are examined. Because the linear convolution is not block-oriented, that is Complexity constant for this algorithm. It is observed that the efficiency of the proposed algorithm exceeds that of the filter-wise fast convolution by an approximately constant factor. This implies that the increased block size L = K + 2B -1 as compared to K + B for the filter-wise fast convolution has no adverse effect on the efficiency, regardless of the block delay.

For the configuration under consideration ( N = 16 , M = 16 , K = 1023, B = 1024) and a maximum delay value D _max = 48000 corresponding to a delay value of one second at a sampling frequency of 48 kHz, the linear convolution algorithms require about 2 , 9 10 ⁶ memory words. For the same parameters, the fast filter-by-convolution algorithm uses about 5.0 × 10 ⁶ floating point memory locations. The increase is due to the size of the precalculated frequency domain filter representations. The proposed algorithm requires about 8.6 x 10 ⁶ words of memory due to the frequency domain delay line and the increased block size for the frequency domain representations of the input signal and the filters. Thus, the performance improvement of the proposed algorithm as compared to filter-wise fast convolution is gained by an increase in required memory of about 72.7%. Thus, the proposed algorithm may be considered as a space-time tradeoff that uses additional memory to store pre-computed results, such as frequency domain representations of the input signal, to allow for more efficient implementation.

The additional memory requirements can have a detrimental effect on performance, such as reduced cache locality. At the same time, it is likely that the reduced number of instructions that imply a reduced number of memory accesses will minimize this effect. It is therefore necessary to examine and evaluate the performance gains of the proposed algorithm for the intended hardware architecture. Likewise, the parameters of the algorithm, such as the FFT block size L or the block delay B, must be matched to the specific target platform.

Although certain elements are described as device elements, it should be understood that this description is likewise to be regarded as a description of steps of a method and vice versa.

Depending on the circumstances, the method according to the invention can be implemented in hardware or in software. The implementation may be on a non-transitory storage medium, a digital storage medium, in particular a floppy disk or CD with electronically readable control signals, which may be used with a programmable computer system that the process is performed. In general, the invention thus also consists in a computer program product with a program code stored on a machine-readable carrier for carrying out the method when the computer program product runs on a computer. In other words, the invention can thus be realized as a computer program with a program code for carrying out the method when the computer program runs on a computer.

Claims (19)

1. Device for calculating loudspeaker signals for a plurality of loudspeakers while using a plurality of audio sources, an audio source comprising an audio signal (10), said device comprising:

   a forward transform stage (100) for transforming each audio signal (10), block-by-block, to a spectral domain so as to obtain for each audio signal a plurality of temporally consecutive short-term spectra;

   a memory (200) for storing a plurality of temporally consecutive short-term spectra for each audio signal;

   a memory access controller (600) for accessing a specific short-term spectrum among the plurality of temporally consecutive short-term spectra for a combination consisting of a loudspeaker and an audio signal on the basis of a delay value (701);

   a filter stage (300) for filtering the specific short-term spectrum for the combination of the audio signal and the loudspeaker by using a filter provided for the combination of the audio signal and the loudspeaker, so that a filtered shot-term spectrum is obtained for each combination of an audio signal and a loudspeaker;

   a summing stage (400) for summing up the filtered short-term spectra for a loudspeaker so as to obtain summed-up short-term spectra for each loudspeaker; and

   a backtransform stage (800) for backtransforming, block-by-block, summed-up short-term spectra for the loudspeakers to a time domain so as to obtain the loudspeaker signals.
2. Device as claimed in claim 1, wherein the forward transform stage (100) is configured to determine the sequence of short-term spectra with a stride value (B) from a sequence of temporal samples, so that a first sample of a first block of temporal samples that are converted to a short-term spectrum is spaced apart from a first sample of a second subsequent block of temporal samples by a number of samples which is equal to the stride value,
   wherein a short-term spectrum has a block index (269) associated with it which indicates the number of stride values by which the first sample of the short-term spectrum is temporally spaced apart from a reference value (249),
   the memory access controller (600) being configured to determine the short-term spectrum on the basis of the delay value (701) and the block index (269) of the specific short-term spectrum such that the block index (269) of the specific short-term spectrum equals an integer result of a division of a time duration which corresponds to the delay value and of a time duration which corresponds to the stride value, or is larger than same by 1.
3. Device as claimed in claims 1 or 2,
   wherein the filter stage (300) is configured to determine, from an impulse response of a filter provided for the combination of a loudspeaker and an audio signal, a modified impulse response in that a number of zeros is inserted at a temporal beginning of the impulse response, the number of zeros depending on the delay value (701) for the combination of the audio signal and the loudspeaker, and on the block index of the specific short-term spectrum for the combination of the audio signal and the loudspeaker.
4. Device as claimed in claim 3, wherein the filter stage (300) is configured to insert such a number of zeros that a time duration corresponding to the number of zeros is smaller than or equal to the remainder of an integer division of the time duration which corresponds to the delay value and of the time duration which corresponds to the stride value.
5. Device as claimed in claim 4, wherein the filter comprises a fractional delay filter configured to implement a delay by a fraction of a time duration between two adjacent discrete impulse response values, said fraction depending on the integer result of the division of the time duration which corresponds to the delay value and of the time duration which corresponds to the stride value, and on the number of zeros inserted into the impulse response.
6. Device as claimed in any of the previous claims, wherein the filter stage (300) is configured to multiply, spectral value by spectral value, the specific short-term spectrum by a transmission function of the filter.
7. Device as claimed in claim 1, wherein the memory (200) comprises, for each audio source, a frequency-domain delay line (201, 202, 203) with an optional access to the short-term spectra stored for said audio source, an access operation being performable via a block index (269) for each short-term spectrum.
8. Device as claimed in any of the previous claims,
   wherein the forward transform stage (100) comprises a number of transform blocks (101, 102, 103) that is equal to the number of audio sources,
   wherein the backtransform stage (800) comprises a number of transform blocks (801, 802, 803) that is equal to the number of loudspeaker signals,
   wherein a number of frequency-domain delay lines (201, 202, 203) is equal to the number of audio sources, and
   wherein the filter stage (300) comprises a number of single filters (301, 302, 303, 304, 305, 306, 307, 308, 309) that is equal to the product of the number of audio sources and the number of loudspeaker signals.
9. Device as claimed in any of the previous claims,
   wherein the forward transform stage and the backtransform stage are configured in accordance with an overlap-save method,
   wherein the forward transform stage (100) is configured to decompose the audio signal into overlapping blocks while using a stride value (B) so as to obtain the short-term spectra, and
   wherein the backtransform stage (800) is configured to discard, following backtransform of the filtered short-term spectra for a loudspeaker, specific areas in the backtransformed blocks and to piece together any portions that have not been discarded, so as to obtain the loudspeaker signal for the loudspeaker.
10. Device as claimed in any of claims 1 to 8,
    wherein the forward transform stage (100) and the backtransform stage (800) are configured in accordance with an overlap-add method,
    wherein the forward transform stage (100) is configured to decompose the audio signal into adjacent blocks, while using a stride value (B), which are padded with zeros in accordance with the overlap-add method, a transform being performed with the blocks that have been zero-padded in accordance with the overlap-add method,
    wherein the backtransform stage (800) is configured to sum up, following the backtransform of the spectra summed up for a loudspeaker, overlapping areas of backtransformed blocks so as to obtain the loudspeaker signal for the loudspeaker.
11. Device as claimed in any of the previous claims,
    wherein the forward transform stage (100) and the backtransform stage (800) are configured to perform a digital Fourier transform algorithm or an inverse digital Fourier transform algorithm.
12. Device as claimed in any of the previous claims, further comprising:

    a wave field synthesis operator (700) configured to produce the delay value (700) for each combination of a loudspeaker and an audio source while using a virtual position of the audio source and the position of the loudspeaker, and to provide same to the memory access controller (600) or to the filter stage (300).
13. Device as claimed in any of the previous claims, wherein the audio source has a directional characteristic, the filter stage (200) being configured to use different filters for different combinations of loudspeakers and audio signals.
14. Device as claimed in any of the previous claims,
    wherein the forward transform stage (100) is configured to perform the block-by-block transform while using a stride (B),
    wherein the memory access controller (600) is configured to partition the delay value into a multiple of the stride and a remainder, and to access the memory (200) while using the multiple of the stride, so as to retrieve the specific short-term spectrum.
15. Device as claimed in claim 14, wherein the filter stage (300) is configured to form the filter while using the remainder.
16. Device as claimed in any of claims 1 to 13,
    wherein the forward transform stage (100) is configured to use a block-by-block fast Fourier transform, the length of which equals K + B, B being a stride in the generation of consecutive blocks, K being an order of the filter of the filter stage when the filter is configured to provide no further contribution to a delay.
17. Device as claimed in any of claims 1 to 15,
    wherein the forward transform stage (100) is configured to use a block-by-block fast Fourier transform, the length of which equals K + 2 B - 1, B being a stride in the generation of consecutive blocks, and K being an order of the filter without any delay line, a maximum of (B - 1) zeros having been inserted into an impulse response, so that an additional delay is provided by the filter.
18. Method of calculating loudspeaker signals for a plurality of loudspeakers while using a plurality of audio sources, an audio source comprising an audio signal (10), said method comprising:

    transforming each audio signal (10), block-by-block, to a spectral domain so as to obtain for each audio signal a plurality of temporally consecutive short-term spectra;

    storing a plurality of temporally consecutive short-term spectra for each audio signal;

    accessing a specific short-term spectrum among the plurality of temporally consecutive short-term spectra for a combination consisting of a loudspeaker and an audio signal on the basis of a delay value (701);

    filtering the specific short-term spectrum for the combination of the audio signal and the loudspeaker by using a filter provided for the combination of the audio signal and the loudspeaker, so that a filtered shot-term spectrum is obtained for each combination of an audio signal and a loudspeaker;

    summing up the filtered short-term spectra for a loudspeaker so as to obtain summed-up short-term spectra for each loudspeaker; and

    backtransforming, block-by-block, summed-up short-term spectra for the loudspeakers to a time domain so as to obtain the loudspeaker signals.
19. Computer program comprising a program code for performing the method as claimed in claim 18 when the program code runs on a computer or processor.

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