WO2010014663A2

WO2010014663A2 - Method for adaptive control and equalization of electroacoustic channels

Info

Publication number: WO2010014663A2
Application number: PCT/US2009/052042
Authority: WO
Inventors: Matthew C. Fellers; Grant A. Davidson; Rongshan Yu; Eric M. Benjamin; Kenneth J. Gundry
Original assignee: Dolby Laboratories Licensing Corporation
Priority date: 2008-07-29
Filing date: 2009-07-29
Publication date: 2010-02-04
Also published as: JP2011530218A; EP2311271A2; CN102113346A; WO2010014663A9; EP2311271B1; WO2010014663A3; CN102113346B; JP5241921B2; US20110142247A1; WO2010014663A8; US8693699B2

Abstract

An electroacoustic channel soundfield is altered. An audio signal is applied by an electromechanical transducer to an acoustic space, causing air pressure changes therein. Another audio signal is obtained by a second electromechanical transducer, responsive to air pressure changes in the acoustic space. A transfer function estimate of the electroacoustic channel is established, responsive to the second audio signal and part of the first audio signal. The transfer function estimate is derived to be adaptive to temporal variations in the electroacoustic channel transfer function. Filters are obtained with transfer functions based on the transfer function estimate. Part of the first audio signal is filtered therewith.

Description

METHOD FOR ADAPTIVE CONTROL AND EQUALIZATION OF ELECTROACOUSTIC CHANNELS

Cross-Reference to Related Applications This application claims pπoπty to U S Patent Provisional Application No

61/137,377, filed 29 July 2008, hereby incorporated by reference in its entirety

Field of the Invention

Various aspects of the invention relate to audio signal processing Aspects of the invention include methods for alteπng the soundfield in an electroacoustic channel and methods for obtaining a set of filters whose linear combination estimates the impulse response of a time- varying transmission channel Aspects of the invention also include apparatus for performing such methods and computer programs, stored on a computer- medium, for causing a computer to perfoπn such methods In particular, aspects of the invention are particularly useful for improving the audibility of portable multimedia and communication devices, particularly by reducing the effect of external environmental noise and/or by improving the understandabihty of speech in noisy environments Aspects of the invention are useful generally in any environment for active noise control (ANC) and vaπous types of equalization (including line enhancement and acoustic echo cancellation)

Background of the Invention Active noise control (ANC) and adaptive equalization may be used to reduce the effect of external environmental noise and/or to improve the understandabihty of speech m noisy environments For example, ANC systems detect the disturbing noise signal and then generate a sound wave of equal amplitude and opposite phase, thereby reducing the perceived disturbance level Summary of the Invention

According to a first aspect of the present invention, a method for alteπng the soundfield in an electroacoustic channel in which a first audio signal is applied by a first electromechanical transducer to an acoustic space, causing changes in air pressure in the acoustic space, and a second audio signal is obtained by a second electromechanical transducer in response to changes in air pressure in the acoustic space, compπses (a) establishing, m response to the second audio signal and at least a portion of the first audio signal, a transfer function estimate of the electroacoustic channel, the transfer function estimate being deπved from one or a combination of transfer functions selected from a group of transfer functions, the transfer function estimate being adaptive m response to temporal

DQ7044WO01 1 variations in the transfer function of the electroacoustic channel, and (b) obtaining one or more filters whose transfer function is based on the transfer function estimate and filtering with the one or more filters at least a portion of the first audio signal, which portion of the first audio signal may or may not be the same portion as the first recited portion of the first audio signal

The method may further compπse implementing the transfer function estimate with one or more of a plurality of time-invariant filters The one or more filters whose transfer function is based on the transfer function estimate may have a transfer function that is an inverted version of the transfer function estimate The transfer function estimate may be adaptive in response to a time average of temporal variations in the transfer function of the electroacoustic channel The one or more of a plurality of time-invaπant filters may be HR filters Alternatively, the one or more of a plurality of time-invariant filters may be two filters in cascade, the first filter being an HR filter and the second filter being an FIR filter In addition, the one or more filters whose transfer function is based on the transfer function estimate may be HR filters Alternatively, the one or more filters whose transfer function is based on the transfer function estimate may be two filters in cascade, the first filter being an IIR filter and the second filter being an FIR filter

The transfer function estimate may be deπved from one or a combination of transfer functions selected from a group of transfer functions by employing an error minimization technique Alternatively, the transfer function estimate may be established by cross fading from one to another of the one or combination transfer functions selected from a group of transfer functions by employing an error minimization technique Yet as a further alternative, the transfer function may be established by selecting two or more of the transfer functions from the group of transfer functions and forming a weighted linear combination of them based on an error minimization technique

The characteπstics of one or more of the group of transfer functions may include the impulse responses of the electroacoustic channel across a range of vaπations in impulse responses with time. The impulse responses may be measured impulse responses of real and/or simulated transmission channels The characteπstics of the group of transfer functions may obtained according to an eigenvector method For example, the group of transfer functions may be obtained by deriving the eigenvectors of the autocorrelation matπx of the time-mvaπant filter characteπstics. Alternatively, the defined group of time-invaπant filter characteπstics may be obtained by deπvmg the eigenvectors resulting from performing a singular value

D07044WO01 2 decomposition of a rectangular matrix in which the rows of the matrix are a larger group of time-invariant filter characteristics.

The first electromechanical transducer may be one of a loudspeaker, an earspeaker, a headphone ear piece, and an ear bud. The second electromechanical transducer is a microphone.

The acoustic space may be a small acoustic space at least partially bounded by an over-the-ear or an around-the-ear cup, the degree to which the small acoustic space is enclosed being dependant on the closeness and centering of the ear cup with respect to the ear. Variations in the transfer function of the electroacoustic channel may result from changes in the location of the small acoustical space with respect to the ear.

Each estimate of the transfer function of the electroacoustic channel may be an estimate of the channel's magnitude response within a range of frequencies. The acoustic space may also receive an audio disturbance signal. The acoustic space may also receive an audio disturbance and the first audio signal may include (1) an error feedback signal derived from the difference between the second audio signal and an audio signal obtained by applying the first audio signal to the filter based on the estimate of the transfer function of the electroacoustic channel, the difference being filtered by the one or more filters whose transfer function is an inverted version of the transfer function estimate, and (2) a speech and/or music audio signal. Aspects of the invention may provide an active noise canceller in which the perceived audio response of the electroacoustic channel reduces or cancels the audio disturbance.

The first audio signal may include an audio input signal filtered by a target response filter and by the one or more filters.

Aspects of the invention may provide an equalizer in which the perceived audio response of the electroacoustic channel emulates the response of the target response filter. The acoustic space may also receive an audio disturbance and the first audio signal may include (1) an error feedback signal derived from the difference between the second audio signal and an audio signal obtained by applying the first audio signal to the estimate of the transfer function of the electroacoustic channel, the difference being filtered by the one or more filters whose transfer function is an inverted version of the transfer function estimate, and (2) a speech and/or music audio signal filtered by a target response filter and also filtered by the one or more filters whose transfer function is an inverted version of the transfer function estimate.

DQ7044WO01 Aspects of the mvention may provide an active noise canceller in which the perceived audio response of the electroacoustic channel reduces or cancels the audio disturbance and also provides an equalizer in which the perceived audio response of the electroacoustic channel emulates the response of a target response filter The target response filter may have a flat response, in which case the filter may be omitted Alternatively, the target response filter has a diffuse field response or the target response filter characteπstic may be user- specified

The one or more filters whose tiansfer function is an inverted version of the transfer function estimate may comprise a lower-frequency HR filter and an uppei frequency FIR filter in cascade

The first audio signal comprises an artificial signal selected to be inaudible The establishing may respond to the second audio signal and at least a portion of the second audio signal as digital audio signals in the frequency domain

According to another aspect of the invention, a method for altering the soundfield in an electroacoustic channel in which a first audio signal is applied by a first electromechanical transducer to an acoustic space, causing changes in air pressure in the acoustic space, and a second audio signal is obtained by a second electromechanical transducer in response to changes in air pressure in the acoustic space, compnses (a) establishing, in response to the second audio signal and at least a portion of the first audio signal, a transfer function estimate of the electroacoustic channel for a range of audio frequencies lower than an upper range of audio frequencies, the transfer function estimate being derived from one or a combination of transfer functions selected from a group of transfer functions, the transfer function estimate being adaptive in response to temporal variations m the transfer function of the electroacoustic channel, (b) obtaining one or more filters whose transfer function for the range of audio frequencies lower than an upper range of audio frequencies is based on the transfer function estimate and filtering with the one or more filters at least a portion of the first audio signal, which portion of the first audio signal may or may not be the same portion as the first recited portion of the first audio signal, and (c) obtaining one or more filters whose transfer function for a range of frequencies higher than the lower range of frequencies is variably controlled by a gradient descent minimization process

This aspect of the invention may further compπse implementing the transfer function estimate for the range of audio frequencies lower than an upper range of audio frequencies with one or more of a plurality of time-mvaπant filters

DQ7044WO01 4 The one or more filters whose transfer function for the range of audio frequencies lower than an upper range of audio frequencies may be based on the transfer function estimate have a transfer function that is an inverted version of the transfer function estimate for the range of frequencies The gradient descent minimization process may be responsive to the difference between the second audio signal and an audio signal obtained by applying at least a portion of the first audio signal to the seπes arrangement of (a) a filter or filters estimating the electroacoustic channel transfer function for the range of audio frequencies lower than an upper range of audio frequencies and (b) a filter or filters having a time-invariant transfer response for a range of frequencies higher than the lower range of frequencies

The filter or filters estimating the electroacoustic channel transfer function for the range of audio frequencies lower than an upper range of audio frequencies may be one or more HR filters and the filter or filters having a time-invariant transfer response for a range of frequencies higher than the lower range of frequencies may be one or more FIR filters The acoustic space may also receive an audio disturbance and the first audio signal may include (1) an error feedback signal deπved from the difference between the second audio signal and an audio signal obtained by applying the first audio signal to the seπes arrangement of (a) a filter or filters estimating the electroacoustic channel transfer function for the range of audio frequencies lower than an upper range of audio frequencies and (b) a filter or filters having a time-mvaπant transfer response for a range of frequencies higher than the lower range of frequencies, the difference being filtered by a series arrangement of (a) the one or more filters whose transfer function for the range of audio frequencies lower than an upper range of audio frequencies is an inverted version of the transfer function estimate and (b) one or more filters whose transfer function for a range of frequencies higher than the lower range of frequencies is variably controlled by a gradient descent minimization process, and (2) a speech and/or music audio signal

Alternatively, the acoustic space also receives an audio disturbance and the first audio signal may include (1) an error feedback signal deπved from the difference between the second audio signal and an audio signal obtained by applying the first audio signal to the seπes arrangement of (a) a filter or filters estimating the electroacoustic channel transfer function for the range of audio frequencies lower than an upper range of audio frequencies and (b) a filter or filters .having a time-mvaπant transfer response for a range of frequencies higher than the lower range of frequencies, the difference being filtered by a seπes arrangement of (a) the one or more filters whose transfer function for the range of audio

DQ7044WO01 5 frequencies lower than an upper range of audio frequencies is an inverted version of the transfer function estimate and (b) one or more filters whose transfer function for a range of frequencies higher than the lower range of frequencies is variably controlled by a gradient descent minimization process, and (2) a speech and/or music audio signal filtered by a target response filter and also filtered by the series arrangement of filters.

According to a further aspect of the invention, a method for obtaining a set of filters whose linear combination estimates the impulse response of a time-varying transmission channel, comprises (a) obtaining M filter observations, the observations including the impulse responses of the transmission channel across its range of possible variations with time, (b) selecting N of M filters according to an eigenvector method, and (c) determining, in real-time, a linear combination of the N filters that forms an optimal estimate of the transmission channel.

The N selected filters may be determined by deriving the eigenvectors of the autocorrelation matrix of the M observations. Alternatively, the N selected filters may be deteπnined by deriving the eigenvectors resulting from performing a Singular Value Decomposition of a rectangular matrix in which the rows of the matrix are the M observations.

A scaling factor for each of the N eigenvector filters may be obtained using a gradient-descent optimization. The gradient-descent optimization may employ an LMS algorithm.

The M observations may be measured impulse responses of real or simulated transmission channels.

Aspects of the invention may improve the listening experience under typical (non- ideal) conditions of electroacoustic channels and their environment. An "electroacoustic channel" may be defined as an acoustic space relative to an ear in which an electromechanical transducer, such as a loudspeaker or earspeaker, causes changes in air pressure in the acoustic space, the electroacoustic channel thus including the electromechanical transducer and the acoustic space between that transducer and a listener's ear drum. In some applications such an electroacoustic channel may be bounded at least in part by a flexible or rigid ear cup. In various exemplary embodiments of the invention, a further electromechanical transducer, such as a microphone, is suitably located within the acoustic space in order to sense changes in air pressure in the acoustic space, thereby allowing the derivation of an estimate of the electroacoustic channel response.

D07Q44WO01 According to aspects of the invention, an ANC and/or equalizer may adapt itself in response to short-time vaπations in the transfer function of the electroacoustic channel The effect of this adaptation is to expand the listening "sweet spot" A sweet spot is the region in which the playback device may be physically located while still achieving effective results Example embodiments of the invention provide both ANC and equalization separately or together — equalization may be added to ANC with negligible increase in implementation cost

Aspects of the invention are applicable, for example, at least to acoustic envπonments characterized by high compliance transducers and relatively few, widely spaced transducer resonances The transducer, when modeled as a linear filter, should result in the model being or approximating a minimum-phase filter The requirement for minimum-phase transducers may be applied to a limited frequency range because ANC is generally most effective for noise signals below 1 5 kHz ANC is particularly well suited for deployment m portable multimedia devices such as earbuds, Bluetooth headsets, portable headphones, and mobile phones, where voice communication and music playback commonly occur under conditions of highly dynamic environmental noise Furthermore, the electroacoustic channels involved may be small (for example, mobile phone pressed against the pinna, earbuds inserted directly into the ear canal, and partially or fully-sealed headphones), implying that the acoustic resonant frequencies are further apart and variable channel resonances can be more readily accounted for m the system Such properties may be exploited in aspects of the present invention to simplify the design of adaptive "earspeaker" systems (sound reproduction devices that are located m close proximity to a listener's ears)

Aspects of the invention address a leading cause of low performance in earspeakers ~ variability m the transfer function of the electroacoustic channel from the loudspeaker to the ear canal Mobile phone users experience this phenomenon while listening to a far-end talker and, often unconsciously, "optimize" the channel by making mmute adjustments to the position and angle of the phone relative to the ear Even when sealed headphones are used, the transfer function vanes depending on the quality of the acoustic seal between the earcup and the head, the position of the earcup, and specific attπbutes of the listener such as pmna size and shape and whether the listener is wearing eyeglasses In an aircraft passenger environment, in which the listener is using a non-adaptive, sealed headphone, an air gap as small as 1 mm may result in a reduction of up to 1 1 dB of low- frequency cancellation of aircraft engine noise

D07044WO01 7 Some digital implementations of aspects of the present invention employ, adaptively, one or a linear combination of a plurality of time-mvaπant IIR (infinite impulse response) filters Such an arrangement is useful, for example, in rapidly tracking changes in the electroacoustic channel Brief Description of the Drawings

FIG 1 is a functional block diagram of an example of a feedback-based active noise control processor or processing method according to aspects of the present invention

FIG 2 is a functional block diagram of an example of an earspeaker equalizing processor or processing method according to aspects of the present invention FIG 3 is a functional block diagram of an example of a combination feedback-based active noise control and earspeakei equalizing processor or processing method according to aspects of the piesent invention

FIG 4 is a hypothetical magnitude versus frequency response showing an example of an injection of a narrowband pilot noise signal in the presence of a wideband disturbance signal

FIG 5 is a functional block diagram of an example of a feedback-based active noise control processor or processing method according to aspects of the present invention in which the adaptive analysis operates in the frequency domain rather than the time domain

FIG 6 is a functional block diagram of an example of a processor or processing method according to aspects of the present invention m which either or both of the control filtering and plant estimate filtering are factored into two or more filters or filtering functions arranged m cascade

FIG 7 is a functional block diagram of an example of an active noise control processor or processing method according to aspects of the present invention in which adaptation based on temporal vaπations of the plant is combined with a supplemental adaptive filtering designed to optimize the control filter based on characteπstics of the disturbance signal

FIG 8 is a functional block diagram of an example of an active noise control and equalization processor or processing method according to aspects of the present invention m which adaptation based on temporal vaπations of the plant is combined with a supplemental adaptive filtering designed to optimize the control filter based on characteristics of the disturbance signal

D07044WO01 FIG 9 is a functional block diagram of an example of an adaptive analysis device or process according to aspects of the present invention m which parameters for a single filter or filtering function are obtained

FIG 10 is a functional block diagram of an example of an adaptive analysis device or process according to aspects of the present invention in which parameters for multiple filters or filtering functions are obtained

FIG 11 is a functional block diagram of a feedback gradient-descent arrangement for denving an inverted filtering iesponse in response to a filtering response

FIG 12 is a functional block diagram of an example of a substantially analog example embodiment of a portion of an active noise control processor (or processor function) and/or equalization processor (or processor function) according to aspects of the pi esent invention

FIG 13 is a functional block diagram of a gradient-descent minimization anangement for determining the optimal weighting of a set of set of filters or filtering functions

Description of Example Embodiments The present invention and its various aspects may involve analog or digital signals, as noted In the digital domain, devices and processes operate on digital signal streams in which audio signals are represented by samples

It is well known that the low frequency response of an earspeaker, such as a headphone, is attenuated as it is pulled away from the ear Likewise, if the headphone is not in the optimal position, an air gap (acoustic leakage) may form around the headphone, and thus the low frequency response may also lowered by an amount proportional to the degree of acoustic leakage The inventors have observed that this change in the frequency response as a function of acoustic leakage is limited to frequencies below a particular frequency value, wherein this value may be different for different earspeakers The vaπation in magnitude frequency response above this frequency value may be assumed to vary less as a function of headphone leakage The vaπation of the magnitude frequency response may be as much as about 15 dB at very low frequencies (about 100 Hz)

When there is a small acoustic space between an earspeaker and the ear canal, typical room reflections are not a factor m the measurements One may assume that room acoustics do not affect such an electroacoustic channel This simplification yields a channel that is, over a nominal frequency range, substantially minimum phase with the exception of a delay, and that has a magnitude frequency response that is mvertible over a bandlimited range The last simplification band limits the range of the electroacoustic model to a frequency range that

D07044WO01 yields minimal or shallow notches m the magnitude response so as to prevent resonant peaks that is annoying to the listener or would create potential instabilities m operation

Frequencies below about 1 5 kHz may be ideal for electroacoustic channel system identification One reason is that m modern analog or digital broadband noise-cancelmg systems (as opposed to systems that cancel periodic disturbances), the frequency range that benefits the greatest from ANC are those frequencies below 1 5 kHz This is because the passive isolation on typical earspeakers are less effective at isolating frequencies with wavelengths longer than 1/3' of a meter, than they are for shorter wavelengths Also, because waveforms with wavelengths greater than l/3^ld of a meter are less affected by system latencies m the hardware, it is desirable that one should focus system identification over the range of frequencies that are most important to relevant and effective noise cancellation Because it vanes continuously across a range of magnitude responses, an electroacoustic channel may be modeled as a linear, continuously time-varying filter

FIG 1 shows an example of a feedback-based active noise control processor or processing method, with an audio ("speech/music") input, employing aspects of the present invention In FIG 1 and other figures herein, solid lines indicate audio paths and dotted lines indicate the conveyance of filter defining information, including for example, parameters, to one or more filters Certain components not necessary to the understanding of the example are not shown explicitly in FIG 1, nor are they shown in other exemplary embodiments of aspects of the invention For example, when the processors or processing methods of the examples of FIGS 1-3 and 5-8 operate principally in the digital domain, a digital-to-analog converter and suitable amplification is required in order to dπve the earspeaker 2 and suitable amplification along with an analog-to-digital converter is required at the output of the microphone 4 In the various figures, a like or corresponding device or function is assigned the same reference numeral

An ANC processor or processing method, such as shown m the example of FIG 1 , seeks to alter the perceived audio output of an electroacoustic channel G m such a way as to reduce the audibility of an environmental disturbance sound Such sounds may be any of a variety of sources including, for example, human speakers, airplane engines, room noise, street noise, acoustic echoes, etc A first audio signal is applied to a first electromechanical transducer, such as an earspeaker 2 (shown symbolically), that causes changes in air pressure in an acoustic space, for example, a small acoustic space close to an ear (ear not shown) The acoustic space also has a second electromechanical transducer, such as a microphone 4 (shown symbolically), that responds to changes in air pressure in the acoustic space and

DQ7Q44WO01 10 produces a microphone signal e The acoustic space also undergoes changes in air pressure resulting from an environmental sound disturbance d The electroacoustic response between the earspeaker 2 and the microphone 4 may be represented as an electromechanical filter G, which mathematically models the ratio of the microphone output to the earspeaker input This model is known m the art as the ' plant "

In accordance with aspects of the invention, an estimate of the plant model G may be implemented as one or more filters or filter functions, and is shown as a plant estimating function or device ("Plant Estimate Filtering, G' ") A feedback signal is obtained by subtracting the output g of the plant model estimate G' fiom the output e of the plant model G m a subtractive combiner or combining function 6 If the Plant Estimate Filtering G' is ideal in its estimation of the model of the electroacoustic channel, i e G' = G, then the feedback path signal x from subtracter 6 is equal to the disturbance signal d A path containing Plant Estimate Filtering G' is often referred to in the literature as the secondary path The feedback path signal x is applied to one or more filters or filtering functions ("Control Filtering, W"), the filtering characteristics of which, in one exemplary embodiment of the invention, are substantially the inverse of the Plant Estimate Filtering G', to produce a disturbance- canceling antiphase signal x' that is summed in an additive combiner or combining function 10 with an input speech and/or music audio signal for application to the earspeaker 2 Regarding notation, G, G' and W are the z-domam transfer functions for digital systems, or the S-domain transfer function for analog systems The disturbance signal d and microphone signal e are equivalent time domain representations of D (see below) and E (see below), respectively

An adaptive analyzer or adaptive analysis function ("Adaptive Analysis") 12 receives the speech and/or music audio signal directly as one input and the microphone 4 signal as another input Ideally, one would like for the right-hand ("Microphone") input to the

Adaptive Analysis 12 to be an acoustic-space-processed version of its left-hand ("Signal") input so that the Adaptive Analysis 12 input signals differ only by the condition of the plant G (this avoids a bias in obtaining the plant estimate G' filtering) For example, that may be accomplished by providing a path parallel to Adaptive Analysis 12 having another instance, a copy, of the plant estimating function or device ("Copy of Plant Estimate Filtering, G'") and adding its output "V" in an additive combiner 14 to the output of combiner 6 Thus, the secondary path G' output subtracts from the V path G' output, effectively leaving the microphone output of the acoustic space as the input to the πght hand side of the Analysis

DQ7044WO01 1 1 In one exemplary embodiment of the invention, the left-hand Signal Input of the Adaptive Analysis 12 represents a known signal, while the right-hand Microphone Input ideally contains only the known signal processed by the plant The Microphone signal e contains the music signal filtered by the unknown plant G However, environmental noise is acquired by the microphone in addition to sound from the earspeaker The environmental noise is considered to be measurement noise from the point of view of performing system identification on the plant The Adaptive Analysis 12 selects a filter that best models the current state of the plant Because the measurement noise is typically uncorrelated with the speech/music signal in Adaptive Analysis 12, it does not effect the optimal filter selection Alternate means for generating the left-hand and πght-hand inputs of Adaptive

Analysis 12 aie possible without departing fiom the spirit of the invention For example, the left-hand input signal can be derived from the plant input signal, and the πght-hand signal can be derived from an estimate of the acoustic-space-processed music signal (the Microphone signal e) As descπbed further below, the Adaptive Analysis 12 generates filtering parameters that, when applied to the Plant Estimate Filtering, G' and the Copy of Plant Estimate Filtering, G' , result in one or more filters, respectively, that estimate the transfer function of the electro acoustic channel G The transfer function estimate G' may be implemented by one or more of a plurality of time-invariant filters, the transfer function estimate G' being adaptive in response to vaπations in the transfer function G of the electroacoustic channel As explained below, Adaptive Analysis 12 may have one of several modes of operation There is a mapping from the filter characteristics determined by Adaptive Analysis 12 and the filterings G' and W

The arrangement of the FIG 1 ANC example is intended to provide a perceived audio response of the electroacoustic channel G such that the speech and/or music is heard while minimizing the audibility of the disturbance Ideally, the antiphase signal x' acoustically cancels the disturbance signal d while not affecting the speech and/or music signal This may be accomplished by minimizing the gam H from the disturbance D to the microphone 4 Minimizing the gain H from the disturbance D to the microphone 4 minimizes the energy transfer from the disturbance D to the error output E

(1)

D07044WO01 12 From the above equation, one may observe that if G' ≠G (indicating that the estimate of the plant G is imperfect), then the denominator is less than one and H is larger than for an ideal plant estimate For the ideal case in which H is set to zero, one may solve for W (assuming that G' = G), and obtain an optimal control filter W

W = - G ₍2)

The plant estimate G' may be modeled as a minimum phase filtei in cascade with a delay In piactice, the delay is approximately 3 to 4 samples at a sampling fiequency of 48 kHz due to acoustic and speaker excitation latencies associated with G But this delay may be factored out when measuring G and the resultant filter, by design, iepiesents a transducer that is minimum phase The above also demonstrates that adapting the system based on changes in the plant also optimizes the control filter W In this case, W is optimal with respect to plant variation

Inverse filtering characteπstics are obtained in any suitable way by a filter inverting device or function ("Inversion") 16 For example, Inversion 16 may calculate the inversion (particularly if the filtering is a single filter), employ a lookup table, or determine the inversion in a side process or off-line by, for example, a gradient-descent method An example of such an out-of-circuit method is described below in connection with the example of FIG 11

As noted above, a music or speech signal is summed with the antiphase signal at the output of Control Filtering, W The speech/music signal is removed from the feedback path by the G' path, leaving only the disturbance as a component in the antiphase signal The effectiveness of such signal removal is dependent on the closeness of the match between G and G'

Aspects of the present invention also envision the adaptive pre-filtermg of audio signals to compensate for physical attπbutes of an electroacoustic channel - m other words, to provide equalization As with ANC, a pπmary contπbutor to the magnitude response of the electroacoustic channel is imparted by the earspeaker Because the electroacoustic channel dπver affects the magnitude response of the electroacoustic channel, a pre-fllter allows the desired audio signal to compensate, within reasonable distortion limits, characteπstics of the electroacoustic channel Also, in an equalizer configuration, a desired magnitude response may be imparted upon the resultant acoustic presentation at the ear based on, for example: (1) simulation of the diffuse field response such as that described in ISO

DQ7044WO01 13 454 (see reference 13, above), (2) user-specified equalization settings, or (3) a flat magnitude response A diffuse field response imparts a head shadowing effect to coarsely simulate the experience of listening to music in a room A flat response may be desirable for certain types of recordings such as binaural recordings where the spatial presentation has a prion been applied to the content under audition The desired response of the electroacoustic channel may be specified according to a usage model, and need not have a flat magnitude response The desired response may be static (time-mvariant) or dynamic (time-vaπant)

FIG 2 shows an example of an earspeaker equalizing processoi oi piocessing method with an audio ("speech/music") input employing aspects of the present invention The audio input is applied to a target response filter or filtering process ("Target Response Filtering, S") The target response filtering characteristic S may be static or dynamic In seπes with filtering S is an inverse plant filter or filtering process (Inverse Plant Filtering, W") so as to apply a version of the audio input filtered by the series combination of filtering chaiacteπ sties S and W to the earspeaker 2 As in the FIG 1 ANC exemplaiy embodiment, an electroacoustic channel G receives an input from earspeaker 2 and provides an output from microphone 4 The earspeaker 2 input and the microphone 4 output are each applied as respective inputs to Adaptive Analysis 12 that generates parameters for one or more filters or filtering functions that estimate the plant response G An inverter or inversion process ("Inversion") 16 inverts the Plant Estimate Filtering G' characteristics in any suitable manner, such as the alternatives mentioned m connection with the descπption of the FIG 1 example The inverted filtering characteristics control the Inverse Plant Filtering W

It is desired that the perceived audio response of the electroacoustic channel G approximate as closely as possible the response of the target response filter S The optimal equalizer may be characteπzed as the ratio of the desired response to that of the electroacoustic channel response

E _a = SW = — " G ₍₄₎

Thus, if W is the inverse of G, the perceived output heard through the seπes combination of the S, W and G transfer characteristics is the S characteπstic S should be limited according to the capabilities of the audio playback system to avoid distortion and non-lmeaπties when the earspeaker is m a non-optimal position (which may require an alteration in bass response)

D07044WO01 14 FIG 3 shows an example of a combination feedback-based ANC and earspeaker equalizing processor or processing method employing aspects of the invention The example of FIG 3 adds equalization to the ANC example of FIG 1 In the FIG 3 example, m order to provide equalization in addition to ANC, the S-filtered speech/music signal is applied to the Control Filtering W This requires inserting a copy of the control filtering W in the left-hand input path to Adaptive Analysis 12 and in the "V" path Because the control filtering W ideally is the inverse of the electro acoustic channel (up to a reasonable working frequency, and withm the constraints of the audio playback system), there is no need for a filter W nor for a filter G' in the secondary path, because the convolution of the control filter W with respect to the estimate of the electroacoustic channel results in a uniform delay ("N-sample delay") 18

The ANC/EQ example of FIG 3 provides for applying the speech/music signal through a desired target response filtering S ("Target Response Filtering, S"), which may be a flat response, in which case the target response filtering is unity If S is unity, W m cascade with the plant G, theoretically results in a flat response Inversion 16 in FIG 3 inverts the Plant Estimate Filtering G' m any suitable manner, such as the alternatives mentioned in connection with the description of the FIG 1 example The Adaptive Analysis 12 may be implemented as descπbed below, by taking its inputs from the speech/music signal and the microphone signal In the FIG 3 example, the additive combiner 10 is located before rather than after the Control Filtering W in order that it affects the S filtered speech/music signal (as in the FIG 2 example)

A requirement of processors or processing methods in accordance with the examples of FIGS 1 and 3 is that in order to adapt the secondary path filter G', a speech or music signal needs to be present In order to ameliorate this problem, one may freeze the adaptation when the level of the speech or music drops below a threshold, the threshold, for example, being chosen such that the signal -to-noise ratio (SNR) permits the Adaptive Analysis 12 to make a sufficiently accurate identification of the plant An alternate solution is to inject a signal at the Adaptive Analysis 12 Input Signal that is inaudible to the listener but is recognizable by the system, even when the injected signal is below the level of the environmental noise (disturbance) Such a pilot narrowband noise may be varied m bandwidth, center frequency, and/or intensity Such parameters may be vaπable over time and be selected so as to optimize the masking of this signal according to psychoacoustic principles For example, such parameters may be selected on-line in order to keep the level

DQ7044WO01 1 5 of the signal at the just-noticeable-difference (JND) boundary between audibility and inaudibility

An example of an injection of a signal is shown with respect to an arbitrary magnitude versus frequency response m FIG 4 Because Adaptive Analysis 12 has a prion information of the injected pilot tone (the Input Signal), the Microphone Signal may be narrowband filtered to consider only frequencies coincident with the frequencies of the pilot narrowband noise Also, if the system has optimized the selection of parameters of the pilot noise to result m inaudibility, the pilot noise may be injected even when speech or music is present This may improve the accuracy of the Adaptive Analysis 12 for instances when the log SNR between the music and the disturbance is negative

The processor or processing method examples of FIGS 1, 2 and 3 may be implemented principally in the digital or analog domains The processor or processing method example of FIG 5 operates principally in the digital domain It differs from the example of FIG 1 mamly m that in a digital implementation of FIG 1, the Adaptive Analysis 12 operates in the frequency domain rather than the time domain Forward transforms 18 and 20, respectively, such as Discrete Fouπer Transforms (DFT) or other suitable transforms, are applied to the Adaptive Analysis 12 inputs As is further descπbed below, the magnitude of the complex coefficients over the frequencies of most interest (10 Hz to 500 Hz, for example) are used by the Adaptive Analysis 12 to compute the error energy The Forward transform may be eliminated if the source audio is already in a frequency-domain representation and if the ANC system is implemented m conjunction with an upstream frequency-domam processor Such upstream frequency-domam processors may be an audio coding system decoder (which include, but is not limited to MPEG-4 AAC, Dolby Digital, etc ) In this case, the particular selection of the frequency-domam transform may be selected to match the coded audio transform Other frequency-domam processing algonthms may be used, and as long as the ANC system can coordinate with such processes, the forward transform on the microphone path may be eliminated

The processor or processing method example of FIG 6 shows aspects of the present invention in which either or both of the control filtering and plant estimate filtering are factored into two or more filters or filtering functions arranged in cascade Depending on the particular electroacoustic channel in use, it may be that within a certain frequency range, the magnitude and phase response vaπations are small so that a single filter models the earspeaker response with sufficient accuracy For example, frequencies above 1 5 kHz may vary by less than 6 dB in the worst case, and by less than 3 dB in the average case If the

D07044WO01 16 Adaptive Analysis 12 filters and the Low Order Filters are each single IIR digital filters, Inversion 16 may implement the Low-Order IIR Control filter by swapping the feedforward coefficients (the zeros) with the feedback coefficients (the poles) The equation for the upper frequency control filter may then be derived from the target control filtering and the lower- frequency IIR filter as follows

W

W₁J _F = ^■

W I₁IR (5)

Likewise, for the secondary path filter σ

G¹UF -

G' ΠR (6)

In this example, the lower-frequency filter may be a low-order IIR filter, while the upper frequency may be implemented as either an FIR or IIR filter of appropriate length to model the higher-frequency features of the earspeaker Other exemplary embodiments are possible with varying combinations of filter-types (FIR or IIR), adaptive versus static, number of filter stages, or even parallel rather than series configurations Because the product of W G may be constrained to be open-loop stable through an offline design of W, then the product of Wii_R W_UF G is also stable The length of the adaptive filter N for WU_F may be reduced because W_LF IS canceling frequencies with wavelengths longer than N A short N improves the response of the system because the N is directly proportional to the convergence time

The upper- frequency filters G_UF and W_UF may be static or adaptive If adaptive, they may switch between optimal filter coefficients based on the system identification from the Adaptive Analysis 12 Alternatively, they may be independently adaptive, entirely separate from the Adaptive Analysis, whereby a gradient-descent algorithm such as the LMS may be employed to converge to optimal upper- frequency filter coefficients Either or both the control and the secondary path upper-frequency filters, G_UF and/or WU_F, may be adaptive The employment of Factored filters is also applicable to the frequency-domam example of FIG 5

FIG 7 shows another example of a processor or processing method in accordance with aspects of the present invention This example combines adaptation based on temporal vaπations of the plant with a supplemental adaptive filtering designed to optimize the control filter based on characteπstics of the disturbance signal Such a supplemental adaptive filtering may be based on the well-known FX-LMS algoπthm A controller may implement an LMS algorithm or a vaπant of the LMS algoπthm, such as the Normalized LMS, in order

DQ7044WO01 17 to attenuate narrowband sound disturbances such as from certain types of machinery and tonal disturbances such as speech harmonics In this case, the upper-frequency control filter W_UF, of section 4 3 is replaced by an adaptive FIR filter with coefficients deπved from the classic LMS update equation w(n + l) = w(n) + μx(n)e(n) n = 0 N - I (7) where w is the FIR filter coefficient vector, N is the length of the control filter WU_F, and x is a vectorized input array read from the feedback path and filtered by the plant model G' The x vector is updated by first shifting all stored values one index value back m time, and then storing the new x sample at index = 0 e is the current (scalar) sample read from the microphone μ is the step size that is chosen to best balance stability against convergence speed

Comparing the example of FIG 7 to the example of FIG 6, the Upper Frequency Control Filter, which is static, is replaced by an adaptive Upper Frequency Control filter WU_F in which the filter coefficients are w, and an LMS Updating device or function 20 implements the LMS update equation Because the example is a feedback-based system, the x input to the LMS update Module is derived from the feedback path, which, in accordance with the FX-LMS algorithm, is filtered by the plant model G' The LMS Updating 20 also needs access to the microphone signal This microphone signal contains the speech/music signal filtered by the plant, which would bias the convergence of w to a suboptimal filter Therefore, it is necessary to remove the speech/music signal from the error update path e, which is shown as the additive combination 22 into e before it enters the LMS Updating 20 In this case, speech/music signal must be filtered by the plant estimate G' because the speech/signal in the error signal has been filtered by the plant G

Thus, the example of FIG 7 employs 1) the combination of the well known FX-LMS system to optimize the control filter based on characteπstics of the distuibance with Adaptive Analysis 12 to optimize the system based on changes in the plant, and 2) the Upper Frequency Control Filter W_UF m seπes with the Lower Frequency Control Filter W_LF, which uses coefficients deπved from the Adaptive Analysis 12 The lower frequency control filter, when implemented by an HR filter, is most effective at modeling the plant at low frequencies (below 1 5 kHz) due to the long time response of an HR filter This improves the degree of noise reduction at low frequencies, which dominate most environmental signal disturbances To a certain extent, the upper frequency control filter is also capable of correcting mismatches between the plant and plant model This form of dual-adaptation is advantageous

D07044WO01 18 compared to a single-adaptation method based solely on FX-LMS To compensate for plant response changes at very low frequencies (100 Hz), a single-adaptation system would require a larger number of adaptive filter taps than a dual-adaptation system This leads to higher computational complexity and longer adaptive filter convergence times compared to a system based on a combination of switched-adaptive filters (such as HR filters) and FX-LMS filters

FIG 8 shows a hybrid processor or processing method arrangement similar to the example of FIG 7, but also piovidmg adaptive equalization, although with differences from the equalizer examples of FIGS 3 and 6 In the FIG 8 example, it is not possible to apply the response of the W_UF filter to the speech/music signal because this filter is solely determined by characteπstics of the disturbance Characteristics of the disturbance are in no way related to the speech/music signal, and so the application of W_UF should be applied only to the antiphase canceling signal Then, a suitable method for applying the equalizing filter W_LF to the speech/music signal is to present a new copy of W_LF m cascade with the Target Response filter Variations on where W_LF IS positioned m the system are possible, such as commuting the filter to locations after either the first or second speech/music branches

FIGS 9 and 10 show two examples of an Adaptive Analysis 12 such as that which may be employed in the processor or processing method examples of FIGS 1 -3 and 5-8 In each of those examples, the Adaptive Analysis 12 is effectively in parallel with the electroacoustic channel (plant) G For example, the optimal filter or filters are selected by computing a measure of similarity between the filter transfer function and that of the electroacoustic channel, at least at low frequencies (for example, below about 1 5 kHz) However, any constrained frequency range may be employed provided that it yields accurate system identification

The Adaptive Analysis 12 may operate by reference to a bank of parallel filters that represent G' for different vaπations of the plant Each of these filters may represent, for example, a unique positioning of a headphone earpiece on a dummy head that may be used for measuring the impulse response of G in a particular position Because the parallel filters only need to modify the signal at low frequencies, and because the response of electroacoustic channels varies relatively slowly across frequency, they may be implemented at very low computational cost using low to moderate-order filters For a digital implementation, the mean-squared error between the output of each of the filters and the microphone error signal may be used to identify which of the filters best matches the plant G For an analog implementation, comparators and logic circuitry may be used to select an optimal filter, as is descnbed further below in connection with FIG 12

DQ7044WO01 19 In the course of implementing an ANC system such as in any of the examples above, a designer may quantify the impulse response of the acoustic path at different headphone positions in order to determine limits imposable upon the adaptive algorithm during real-time operation. Because this quantification may be conducted for a known earspeaker electroacoustic path, the electroacoustic parameters of the path may be fully specified before measurement.

FIG. 9 shows an example of an Adaptive Analysis 12 for the case in which only one filter is chosen (K=I). Generally, from a set of M filters, which one may refer to as observations, the Adaptive Analysis 12 chooses N filters. From these N filters, one filter K is chosen and its index may be provided as the Analysis output.

In this example, one filter out of a possible N is selected based on a minimum mean- square error criterion. The N filters are connected in a parallel arrangement, producing in a bank of filters or filtering functions ("N Parallel Filters") 24 in which each filter processes the same bandpassed version of the Input Signal. A controller or controlling function ("Control") 26 selects the k^th filter, depending on which of the N filters returns the minimum time- averaged mean-squared error. Adaptive Analysis 12 receives an Input Signal (corresponding to the left-hand input to Analysis 12 in FIGS. 1-3 and 5-8) and a Microphone Signal (corresponding to the right-hand input to Analysis 12 in FIGS. 1-3 and 5-8). The Input Signal and Microphone Signal, respectively, are applied via substantially identical bandpass filters 24 and 30. Their passbands include the largest variation across the different observations M. Both the Input Signal and the Microphone Signal are digital audio samples in this example. In response to those input signals, Control 2626 selects one optimal filter and produces as its output the Kth index for identifying the selected filter K. A mapper or mapping function ("Mapping") 34 may map the index to a corresponding set of filter parameters. The inputs to Control 26 are the outputs of subtractive combiners 32-0 through 32-(N-I) that subtract the bandpass-filtered Microphone Signal from each of the N- filtered bandpass-filtered Input Signals, each producing an error signal, the magnitude of which is smallest for the filter N that most closely approximates the response of the plant G (see FIGS. 1-3 and 5-8). Subject to averaging, Control 26 selects the filter having the closest approximation to the plant G and outputs the index K of that filter.

Averaging may be implemented using a simple pole-zero smoothing filter. A 3 dB time constant of 70 msec (milliseconds) (f_s=50 kHz) has been found useful. To change from one filter selection to another, only the filter coefficients and not the filter states need to be changed. The change may be applied as an instantaneous switch from one set of coefficients

D07044WO01 20 to the next. In order to minimize audible artifacts incurred during the switching, the change, with respect to pole and zero values, should be small. For the K=I case, as in this FIG. 9 example, Inversion 16 (see FIGS. 1-3 and 5-8) may be applied by pre-computing and storing an inverse filter corresponding to each of the N filters. It is possible to crossfade from one set of filter coefficients for G' to another nearby set (in teπns of the relative distance between the poles and zeros). This can be accomplished by replacing the old coefficients with new ones incrementally over time, or by allowing K=2 for an interval of time and computing the overall output as the time-varying weighted sum of both (one filter having the old set of coefficients and the other having the new set). Provided the cross-fade time is reasonably short (less than 100 msec, for example), in practice it is still possible to achieve reasonably correct system identification during such crossfading. In this case, when crossfading G' from a first set of coefficients to a nearby second set of filter coefficients, the corresponding coefficients for W may either be read from memory if the coefficients were computed offline, or computed directly as the inverse of G'. FIG. 10 shows an example of an Adaptive Analysis 12 in which the device or process selects a linear combination of multiple filters. Generally, the Adaptive Analysis 12 chooses N filters. From these N filters, a smaller set of K filters and their relative weights may be identified so that K filter parameters and K weighting parameters may be provided as the Analysis output. Each filter, of the set of N filters, is implemented in a parallel configuration in a bank of filters or filtering functions ("N Parallel Filters") 24, in which each filter operates on the same bandpassed version of the Input Signal. In variations of the FIG. 10 example, described below, limits are placed upon N and K. In all such variations, the range of frequencies over which the Analysis performs its error analysis may be limited, for example, to the range of frequencies with the largest differences across all observations. Adaptive Analysis 12 receives an Input Signal (corresponding to the left-hand input to Analysis 12 in FIGS. 1-3 and 5-8) and a Microphone Signal (corresponding to the right-hand input to Analysis 12 in FIGS. 1-3 and 5-8). The Input Signal and Microphone Signal, respectively, are applied via substantially identical bandpass filters 24 and 30. Their passbands may include the largest variation across the different observations M. Both the Input Signal and the Microphone Signal are digital audio samples. In response to those bandpass-filtered input signals, Control 26 selects N out of M candidate filters and, as its outputs, provides K sets of filter coefficients and K weighting parameters in order to provide information for providing a linear combination of K filters (K <N ≤M), the case of K=I being handled by an Analysis such as described above in connection with FIG. 9. Thus, M is the set of all possible filters,

DQ7044WO01 21 N is the subset of filters to test m parallel to determine the K filters, and K is the bank of parallel filters for which K sets of filter coefficients and K weighting parameters are passed to Plant Estimate Filtering and, after inversion, to Control Filtering (or Inverse Plant Filtering), as descπbed above in connection with the examples of FIGS 1-3 and 5-8 The inputs to Control 26 are the outputs of subtractive combiners 32-0 through 32-(N-I ) that subtract the bandpass-filtered Microphone Signal from each of the N-filtered bandpass-filtered Input Signal, each producing an error signal, Control 26 selects weightings of the filteis having the closest appi oximation to the plant G and outputs the filter parameters of that filter Various ways of choosing a plurality of weighted filters are described below When K> 1 , the Plant Estimate Filtering in the various exemplary embodiments may be implemented by a bank of K parallel filters or filtering functions, each having a weighting coefficient In accordance with aspects of the piesent invention, the filters or filtering functions controlled by the K filter parameters and K weighting parameters provided by the Analysis 12 may be HR, FIR, or a combination of HR and FIR filters One possible application of multiple filters K is to enhance crossfading from one filter to an adjacent filter (m terms of poles and zeros) As mentioned above, outputs of the K filters are mixed together using weighting coefficients produced by the Control 26 During the time interval of a crossfade, K=2, otherwise, K=I This method may reduce audible artifacts caused by switching between two different filters in the method described earlier (when K=I)

A computationally-efficient variation on the multiple-filter method is to restrict the search to a subset of the total number of filters M This is accomplished by assigning filter indices so that filters with similar transfer functions have indices that are adjacent to each other, and then restπcting the search to the N filters neighboring the current filter having minimum mean-square error Tracking is enabled in the Control 26 by monitoring the averaged relative mean-square error of the filter with the middle index compared to its neighbors If, over time, the minimum error begins to move toward one of the endpomts of the set of N filters until finally a new minimum is detected, the indices of all N filters are adjusted so that the filter with the middle index continues to have the minimum mean-square error out of the set of N filters.

Another alternative of the Adaptive Analysis 12 is for it to operate in the frequency domain rather than the time domain as m the example of FIG 5 In that case, a mean-square error analysis may be applied to the power spectral density (PSD) coefficients of both inputs to the Adaptive Analysis 12 Any time-to-frequency transform or subband filterbank may be

DQ7Q44WO01 22 used to perform the transformation. This would allow a large number of spectral estimation techniques to be used to improve separation of the signal (the music or speech signal played through the transducer) from the noise (the disturbance). One useful technique is to smooth the PSD coefficients over time, in the manner of a standard periodogram analysis, to assure that any bias in the power approaches zero over time. Alternatively, other spectrum estimation techniques such as the "multitaper" method may be used. This approach would also result in no significant increase in computational complexity because time-domain FIR bandpass filters (described below) in the Adaptive Analysis 12 are eliminated. Instead, the same result may be obtained by limiting the range over which the least-squares calculation is performed on the PSD coefficients. The actual forward transform has complexity on the order of Mlog(M) (where M is the number of frequency-domain coefficients) operations but this is still less than the order (N²) complexity of the time-domain bandlimiting filters. Once the best filter or filters is (are) selected in the frequency-domain, its (their) time-domain equivalent filter or filters is (are) conveyed to the time-domain filter or filters. Thus, there is no online inverse-transformation of filter coefficients nor need there be an audio signal outputted by the Adaptive Analysis 12. Filter coefficients may be selected from a table of precomputed filter coefficients. The selection of time-domain coefficients is conducted through the analysis of frequency-domain coefficients.

Another variation on the multiple-filter linear-combination method, is for K=N and to select the N out of M filters according to an eigenvector method such that a linear combination of the N filters forms an optimal energy-minimizing filter. According to such an eigenvector filter method, the N selected filters are computed offline for a given set of M observations. The N-of-M Selection is not implemented in real-time because the N filters have already been computed off-line. The N selected filters are the eigenvectors of the autocorrelation matrix of the M observations. Alternatively, the M observations form the rows of a rectangular matrix and a Singular Value Decomposition of this rectangular matrix yield the eigenvector filters. The Control 26 then computes weighting coefficients for each of the N eigenvector filters, for example, using a gradient-descent minimization process, such as an LMS algorithm. Because all N filters are used to compute the optimal filtered output, K=N. Thus for any given electroacoustic channel impulse response, the response may be mapped to nearest principal components constructed from the N eigenvectors. Such an eigenvector filter method has the advantage that for a large value of M, (i.e., a large number of observations), a smaller number of fixed filters N may be linearly combined to form an optimal energy-minimizing filter. A derivation of the method for generating the eigenvector

DQ7044WO01 23 filters is presented below under the heading "Derivation of the Eigenvector Filter Design Process "

The Inversion device or function 16 in the examples of FIGS 1-3 and 5-8 aims to derive a spectral inverse filter that, when applied to the control filter and analyzed m seπes with the plant response, results in a flat frequency response with no spectial components greater than 0 dB For the Switched Minimum Error method, if the filter selected m the Adaptive Analysis 12 is minimum phase (excluding any delay) then there is a 1 -to-l mapping of each filter m M to a corresponding spectral inverse filter, which may be read from a table, or computed directly as the inverse of G' For any Adaptive Analysis methods where K > 1 , the inverse filter coefficients is computed other than by filter inversion For instance, the out- of-cncuit network of FIG 11 may be employed as the Inversion 16 A disadvantage of this method is that adaptation may only occur when there is signal present at the speech/music input source In the absence of a speech/music source, the adaptation should be frozen An alternate method that injects an inaudible probe signal during periods of no speech or music is discussed above in connection with the example of FIG 4

Referring to the example of FIG 11, a feedback LMS arrangement is provided for deriving the inverted response W based on the plant estimate response G' A noise signal d(n) is applied to the input A first path sums the input at a subtractive combiner 60 with the output of a feedback arrangement The feedback arrangement compares the overall output from combiner 36 with a G' Copy filtered version of the noise signal d(n), and applies a suitable gradient-descent type algorithm, such as an LMS algorithm, in order to control filtering W such that it is an inversion of G' Copy When optimized, a delayed version of W convolved with G' Copy is unity, which results in the error output e(n) of combiner 60 being zero FIG 12 presents an example of aspects of the invention based on analog technology

An advantage of an analog over a digital implementation is that system latencies are shorter because A/D and D/A converters are unnecessary A microphone 4 gives a single-frequency estimate of the low- frequency response of the electroacoustic channel G, and a filter is selected from a filter bank 38 that gives the closest response to a desired response The output of microphone 4 is applied to a bandpass filter 30, followed, in series, by an averager or averaging function ("Mic Avg") 40 The Mic Avg 24 output is applied to an input of each of three comparators or comparator functions Cl, C2 and C3 The speech/music input audio signal is applied to a static filter or filtering function ("Static Filter") 42, followed, in seπes, by a bandpass filter 24 and an averager or averaging function

DQ7Q44WO01 24 ("Audio Avg") 44. The Audio Avg 44 output is applied to an input of each of three comparators or comparator functions Cl, C2 and C3. The Bandpass Filters 24 and 30 isolate a narrow band of frequencies at which the average reproduced level at low frequencies is compared with the average level in the audio program. Comparators Cl, C2, and C3 have different offsets in order to give different thresholds for the decision as to which filter (1, 2, 3, 4) should be selected. The comparators may be implemented with hysteresis in order to eliminate jittering between the outputs of the various filters. Control 26 selects the filter 20 having the least squared error.

Other than employing an analog or partially analog implementation, another way to reduce latency is to implement the feedback path in the example of FIG. 3 with a 1-bit delta- sigma-sampled digital signal processing arrangement. Such 1-bit delta-sigma-modulated sampling system may sample audio at a sampling frequency as high as 64 times the base audio sampling rate. Doing so provides an updating of the anti-phase signal at a very high rate, which reduces system latency incurred by sampling the signal using traditional multi-bit sampling methods, sampled at the standard audio sample rate. A 1 -bit delta-sigma A/D converter at combiner 6 in FIG. 3 and a 1 -bit delta-sigma D/A converter at the loudspeaker 2 in FIG. 3 would be required. In addition, the control filter W and secondary path filter G' would apply multi-bit filter coefficients to the 1-bit intermediate-filter-state values, which would result in a multi-bit output at the filter outputs. The multi-bit output values from each filter would then be transformed back to 1 -bit values through the incorporation of a delta- sigma modulator. Other combinations of filters and delta-sigma modulators are possible, such as performing a single multi-bit to delta-sigma modulator conversion immediately before the 1-bit delta-sigma D/A converter. Depending on the specific implementation, the speech and/or music audio signal may need to be modulated from a multi-bit to a 1-bit delta- sigma representation at the summation 10.

In the analog example of FIG. 12, including digital variations thereof, measuring the change in electroacoustic channel response at a single frequency has a problem in that the variation in the range of sensitivities of an earspeaker and of a microphone is each almost as great as the variation in response associated with changes in the acoustical loading conditions. The assumption is that the gain in the middle of the band defined by the bandpass filters should be substantially equal in both the 'mic AVG' and 'audio AVG' signal paths. Thus, a way to compensate variations in the sensitivities of the microphone and earspeaker should be provided.

D07044WO01 25 Another alternative example that embodies aspects of the present invention is a hybrid digital/analog exemplary embodiment in which the Adaptive Analysis 12 operates on digital samples of both the speech/music signal and the microphone signal, but then applies analog filter parameters (shown as Filter 1 through Filter 4 in the example of FIG. 12) to analog implementations of the control filtering W and the plant estimate filtering G'.

Derivation of the Eigenvector Filter Design Process

In order to derive a set of eigenvector filters for use in the eigenvector alternative mentioned above, one needs to compute K (or N, K=N) eigenvector filters based on a set of M observations. Calculation of eigenvector filters C may occur off-line. The eigenvector filter coefficients may be stored in a suitable non-volatile computer memory.

Selection of N Base Filters One may start from a general case in which the filter to be modeled is characterized i-l by a random filter P{z) — 2 ,P₁Z ' having random real coefficients p = (p₀,.. - ,p_L._\ ) • The

L-I objective is to find a set of Nbase filters C₁ (Z) = ^c_{1 /}z^{' 1} , i = \,... , N , N < L , with real

coefficients C₁ = (c_{; 0},. .. , c_{; i}_, ) , such that

J(C)

= 2?{]|p-C'w|} is minimized. In equation 8, Zs {Dj is the statistical expectation with respect to the distribution of the random coefficients of p ,

U

and w D (w_i , ..., w_N ) is a real vector that minimizes p — C^rw for given p and C . Without lost of generality one may further assume c, are orthonormal vectors, i.e.,

^{1 J} I O else

Because

||p - C^rw| = P⁷P + W⁷CC^{7^}w - 2P⁷C^7-W .

D07Q44WO01 26 Recognizing that CC^r = I , partially differentiating the above expression with respect to w , and setting the derivative to zero, one has w = Cp . Replace the above into (1 ) one has

J(C) = £{p^rp-p^rC^rCp}

where

RDE{pp^T}.

Clearly, the coefficient vectors c, , i = l,...,N that minimizes J also maximizes

N

^c,^rRc_; , which turn out to be the N eigenvectors corresponding to the N largest eigenvalues

1=1 of the covariance matrix R . That is: Rc, = λ,c,, i = l,...,N, and X₁, i-\,...,N are the N largest scalars that satisfy the above equations.

A more generalized solution can be obtained by adding a frequency weighting function W(ύ)) to the cost function J(C), which can be quite useful in practical applications.

J(C) = E U P(e^J'°)-∑w,C,(eη W(ω)dω)

Consider a more specific case in which the filter to be modeled is from M observed

plant filters G₁ (z) = ^g₁ (j) z^~' , i = 1,2,...,M . Noting that in this case one is trying to

model a random filter of M equally probable filters G₁ (z) for which the covariance matrix is given by:

where g₍ =(g₍ (0),g, (l),...,g₍ (Z-I)) , the coefficients of the N base filters C₁(Z),...,C_N (Z) are thus given by the eigenvector c_; corresponding to the TVlargest eigenvalues X₁ of the covariance matrix R.

DQ7044WO01 27 The actual number of the base filter N can be decided either by complexity constraints, or quality constraints, e.g., the sum of the remaining eigenvalues

satisfies ∑ λ, < ε where ε is a pre-determined maximum design tolerance

In practice, it is also possible to use HR filters that have frequency responses that approximate those of the Eigenvector filters as the N base filters for further complexity reduction. The HR base filters can be designed from C₁ (z) , .. . , C_N (Z) by using, e.g., a suitable error minimizing process such as a least-square-fit algorithm.

LMS Adaptation of Weighting Coefficients

Once the N base filters have been computed, the optimal weighting W that provides the least square fit for a given unknown electroacoustic channel may be obtained by using a gradient-descent minimization process such as an LMS algorithm. An example is shown in FIG. 13. In the FIG. 13 example, the error signal e{n) is given by e («) = x (ft) - w^7" (ft) u(n) ,

where u («) D (ω, [n),...,u_N («)) are the respective outputs of the N base filters. The filter

weightings W( nj are updated as: w (» + l) = yv {n) + μw(n) e(n) .

Implementation

The invention may be implemented in hardware or software, or a combination of both (e g , programmable logic arrays). Unless otherwise specified, algorithms and processes included as part of the invention are not inherently related to any particular computer or other apparatus. In particular, various general-purpose machines may be used with programs written in accordance with the teachings herein, or it may be more convenient to construct more specialized apparatus (e g , integrated circuits) to perform the required method steps. Thus, the invention may be implemented m one or more computer programs executing on one or more programmable computer systems each comprising at least one processor, at least one data storage system (including volatile and non-volatile memory and/or storage elements), at least one input device or port, and at least one output device or port. Program code is applied to input data to perform the functions described herein and generate output information. The output information is applied to one or more output devices, in known fashion. Each such program may be implemented in any desired computer language (including machine, assembly, or high level procedural, logical, or object oriented programming

D07044WO01 28 languages) to communicate with a computer system. In any case, the language may be a compiled or interpreted language.

Each such computer program may be stored on or downloaded to a storage media or device (e.g., solid state memory or media, or magnetic or optical media) readable by a general or special purpose programmable computer, for configuring and operating the computer when the storage media or device is read by the computer system to perform the procedures described herein. The inventive system may also be considered to be implemented as a computer-readable storage medium, configured with a computer program, where the storage medium so configured causes a computer system to operate in a specific and predefined manner to perform the functions described herein.

An embodiment of the present invention may relate to one or more of the example embodiments enumerated below.

1. A method for altering the soundfield in an electroacoustic channel in which a first audio signal is applied by a first electromechanical transducer to an acoustic space, causing changes in air pressure in the acoustic space, and a second audio signal is obtained by a second electromechanical transducer in response to changes in air pressure in the acoustic space, comprising: establishing, in response to the second audio signal and at least a portion of the first audio signal, a transfer function estimate of the electroacoustic channel, said transfer function estimate being derived from one or a combination of transfer functions selected from a group of transfer functions, said transfer function estimate being adaptive in response to temporal variations in the transfer function of the electroacoustic channel, and obtaining one or more filters whose transfer function is based on the transfer function estimate and filtering with the one or more filters at least a portion of the first audio signal, which portion of the first audio signal may or may not be the same portion as said first recited portion of the first audio signal.

2. A method according to enumerated example embodiment 1 further comprising implementing said transfer function estimate with one or more of a plurality of time-invariant filters.

3. A method according to enumerated example embodiment 1 or enumerated example embodiment 2 wherein said one or more filters whose transfer function is based on the transfer function estimate have a transfer function that is an inverted version of the transfer function estimate.

D07044WO01 29 4. A method according to any one of enumerated example embodiments 1-3 wherein the transfer function estimate is adaptive in response to a time average of temporal variations in the transfer function of the electroacoustic channel.

5. A method according to enumerated example embodiment 3 or enumerated example embodiment 4 as dependent on enumerated example embodiment 2 wherein said one or more of a plurality of time-invariant filters are HR filters.

6 A method according to enumerated example embodiment 3 or enumerated example embodiment 4 as dependent on enumerated example embodiment 2 wherein said one or more of a plurality of time-invariant filters are two filters in cascade, the first filter being an HR filter and the second filter being an FIR filter

7. A method according to any one of enumerated example embodiments 1-6 wherein said one or more filters whose transfer function is based on the transfer function estimate are IIR filters

8. A method according to any of enumerated example embodiments 1-6 wherein said one or more filters whose transfer function is based on the transfer function estimate are two filters in cascade, the first filter being an IIR filter and the second filter being an FIR filter.

9. A method according to any one of enumerated example embodiments 1-8 wherein said transfer function estimate is derived from one or a combination of transfer functions selected from a group of transfer functions by employing an error minimization technique. 10. A method according to any one of enumerated example embodiments 1-8 wherein said transfer function estimate is established by cross fading from one to another of said one or combination transfer functions selected from a group of transfer functions by employing an error minimization technique

11. A method according to any one of enumerated example embodiments 1-8 wherein said transfer function is established by selecting two or more of said transfer functions from said group of transfer functions and forming a weighted linear combination of them based on an error minimization technique.

12. A method according to any one of enumerated example embodiments 1-11 wherein the characteristics of one or more of the group of transfer functions includes the impulse responses of the electroacoustic channel across a range of variations in impulse responses with time.

13. A method according to enumerated example embodiment 12 wherein the impulse responses are measured impulse responses of real and/or simulated transmission channels.

D07044WO01 30 14 A method according to enumerated example embodiment 12 wherein the characteπstics of said group of transfer functions are obtained according to an eigenvector method

15 A method according to enumerated example embodiment 14 wherein the group of transfer functions are obtained by deriving the eigenvectors of the autocorrelation matrix of the time invariant filter characteπstics

16 A method according to enumerated example embodiment 14 wherein the defined group of time-invaπant filter characteristics are obtained by deriving the eigenvectors resulting from pei forming a singular value decomposition of a rectangulai matnx in which the rows of the matrix are a larger group of time-invariant filter characteristics

17 A method according to any one of enumerated example embodiments 1-16 wheiein said first electromechanical transducer is one of a loudspeaker, an earspeaker, a headphone ear piece, and an ear bud

18 A method according to any one of enumerated example embodiments 1-17 wherein said second electromechanical transducer is a microphone

19 A method according to any one of enumerated example embodiments 1-18 wherein said acoustic space is a small acoustic space at least partially bounded by an over- the-ear or an around-the-ear cup, the degree to which the small acoustic space is enclosed being dependant on the closeness and centering of the ear cup with respect to the ear 20 A method according to enumerated example embodiment 19 wherein said variations in the transfer function of the electroacoustic channel result from changes in the location of the small acoustical space with respect to said ear

21 A method according to any one of enumerated example embodiments 1-20 wherein each estimate of the transfer function of the electroacoustic channel is an estimate of the channel's magnitude response within a range of frequencies

22 A method according to any one of enumerated example embodiments 1-21 wherein said acoustic space also receives an audio disturbance signal

23 A method according to any one of enumerated example embodiments 1-21 wherein said acoustic space also receives an audio disturbance and said first audio signal includes (1) an error feedback signal deπved from the difference between the second audio signal and an audio signal obtained by applying said first audio signal to the filter based on the estimate of the transfer function of the electroacoustic channel, said difference being filtered by said one or more filters whose transfer function is an inverted version of the transfer function estimate, and (2) a speech and/or music audio signal

D07044WO01 31 24. A method according to enumerated example embodiment 23 wherein the method provides an active noise canceller in which the perceived audio response of the electroacoustic channel reduces or cancels the audio disturbance.

25. A method according to any one of enumerated example embodiments 1-21 wherein said first audio signal includes an audio input signal filtered by a target response filter and by said one or more filters.

26. A method according to enumerated example embodiment 25 wherein the method provides an equalizer in which the perceived audio response of the electroacoustic channel emulates the response of the target response filter. 27. A method according to any one of enumerated example embodiments 1-21 wherein said acoustic space also receives an audio disturbance and said first audio signal includes (1) an error feedback signal derived from the difference between the second audio signal and an audio signal obtained by applying said first audio signal to the estimate of the transfer function of the electroacoustic channel, said difference being filtered by said one or more filters whose transfer function is an inverted version of the transfer function estimate, and (2) a speech and/or music audio signal filtered by a target response filter and also filtered by said one or more filters whose transfer function is an inverted version of the transfer function estimate.

28. A method according to enumerated example embodiment 27 wherein the method provides an active noise canceller in which the perceived audio response of the electroacoustic channel reduces or cancels the audio disturbance and also provides an equalizer in which the perceived audio response of the electroacoustic channel emulates the response of the target response filter.

29. A method according to enumerated example embodiment 26 or enumerated example embodiment 28 in which the target response filter has a flat response, whereby the filter may be omitted.

30. A method according to enumerated example embodiment 26 or enumerated example embodiment 28 in which the target response filter has a diffuse field response.

31. A method according to enumerated example embodiment 26 or enumerated example embodiment 28 in which the target response filter characteristic is user-specified.

32. A method according to enumerated example embodiment 23 or enumerated example embodiment 27 wherein said one or more filters whose transfer function is an inverted version of the transfer function estimate comprise a lower- frequency HR filter and an upper- frequency FIR filter in cascade.

DQ7Q44WO01 . 32 33 A method according to any one of enumerated example embodiments 1-21 wherein said first audio signal comprises an artificial signal selected to be inaudible

34 A method according to any one of enumerated example embodiments 1 -32 wherein said establishing responds to the second audio signal and at least a portion of the second audio signal as digital audio signals in the frequency domain

35 A method for altering the soundfield in an electroacoustic channel m which a first audio signal is applied by a first electromechanical transducei to an acoustic space, causing changes in air pressure in the acoustic space, and a second audio signal is obtained by a second electromechanical transducer in response to changes in air pressure in the acoustic space, comprising establishing, in response to the second audio signal and at least a portion of the fust audio signal, a transfer function estimate of the electroacoustic channel for a range of audio frequencies lower than an upper iange of audio frequencies, said transfer function estimate being derived from one or a combination of transfer functions selected from a group of transfer functions, said transfer function estimate being adaptive in response to temporal variations in the transfer function of the electroacoustic channel, obtaining one or more filters whose transfer function for said range of audio frequencies lower than an upper range of audio frequencies is based on the transfer function estimate and filtering with the one or more filters at least a portion of the first audio signal, which portion of the first audio signal may or may not be the same portion as said first recited portion of the first audio signal, and obtaining one or more filters whose transfer function for a range of frequencies higher than said lower range of frequencies is vaπably controlled by a gradient descent minimization process 36 A method according to enumerated example embodiment 35 furthei comprising implementing said transfer function estimate for said range of audio frequencies lower than an upper range of audio frequencies with one or more of a plurality of time-mvaπant filters

37 A method according to enumerated example embodiment 35 or 36 wherein said one or more filters whose transfer function for said range of audio frequencies lower than an upper range of audio frequencies is based on the transfer function estimate have a transfer function that is an inverted version of the transfer function estimate for said range of frequencies

38 A method according to enumerated example embodiment 35 wherein the gradient descent minimization process is responsive to the difference between said second audio

D07044WO01 33 signal and an audio signal obtained by applying at least a portion of said first audio signal to the seπes arrangement of (a) a filter or filters estimating the electroacoustic channel transfer function for said range of audio frequencies lower than an upper range of audio frequencies and (b) a filter or filters having a time-mvaπant transfer response for a range of frequencies higher than said lower range of frequencies

39 A method according to enumerated example embodiment 38 wheiein the filter or filters estimating the electroacoustic channel transfer function for said range of audio frequencies lower than an upper range of audio frequencies is or are HR filters and the filter or filters having a time-mvaπant transfer response for a range of frequencies higher than said lower range of frequencies is or aie FIR filteis

40 A method according to any one of enumerated example embodiments 1-3 wherein said acoustic space also receives an audio disturbance and said first audio signal includes (1) an error feedback signal derived from the difference between the second audio signal and an audio signal obtained by applying said first audio signal to the series arrangement of (a) a filter or filters estimating the electroacoustic channel transfer function for said range of audio frequencies lower than an upper range of audio frequencies and (b) a filter or filters having a time-invariant transfer response for a range of frequencies higher than said lower range of frequencies, said difference being filtered by a seπes arrangement of (a) said one or more filters whose transfer function for said range of audio frequencies lower than an upper range of audio frequencies is an inverted version of the transfer function estimate and (b) one or more filters whose transfer function for a range of frequencies higher than said lower range of frequencies is variably controlled by a gradient descent minimization process, and (2) a speech and/or music audio signal

41 A method according to any one of enumerated example embodiments 35-39 wherein said acoustic space also receives an audio disturbance and said first audio signal includes (1) an en or feedback signal derived from the difference between the second audio signal and an audio signal obtained by applying said first audio signal to the seπes arrangement of (a) a filter or filters estimating the electroacoustic channel transfer function for said range of audio frequencies lower than an upper range of audio frequencies and (b) a filter or filters having a time-mvaπant transfer response for a range of frequencies higher than said lower range of frequencies, said difference being filtered by a seπes arrangement of (a) said one or more filters whose transfer function for said range of audio frequencies lower than an upper range of audio frequencies is an inverted version of the transfer function estimate and (b) one or more filters whose transfer function for a range of frequencies higher

D07044WO01 34 than said lower range of frequencies is vaπably controlled by a gradient descent minimization process, and (2) a speech and/or music audio signal filtered by a target response filter and also filtered by said series arrangement of filters

42 A method for obtaining a set of filters whose linear combination estimates the impulse response of a time-varying transmission channel, comprising obtaining M filter observations, the observations including the impulse responses of the transmission channel across its range of possible variations with time, selecting N of M filters according to an eigenvector method, determining, in leal-time, a linear combination of the N filters that forms an optimal estimate of the transmission channel 43 The method of enumerated example embodiment 42 wherein the N selected filters are determined by deriving the eigenvectois of the autocorrelation matrix of the M observations

44 The method of enumerated example embodiment 42 wheiein the N selected filters aie determined by deriving the eigenvectors resulting from performing a Singular Value Decomposition of a rectangular matrix in which the rows of the matrix are said M observations

45 The method of any one of enumerated example embodiments 42-44 wherein a scaling factor for each of the N eigenvector filters is obtained using a gradient-descent optimization 46 The method of enumerated example embodiment 45 wherein said gradient- descent optimization employs an LMS algoπthm

47 The method of any one of enumerated example embodiments 42-46 wherein said M observations are measured impulse responses of real or simulated transmission channels 48 Apparatus adapted to perform the methods of any one of enumerated example embodiments 1-47

49 Apparatus comprising means adapted to perform each step of the method of any one of enumerated example embodiments 1 -47

50 A computer program, stored on a computer-readable medium, for causing a computer to perform the methods of any one of enumerated example embodiments 1-47. A number of example embodiments of the invention have been descπbed in the specifiation Nevertheless, it will be understood that vaπous modifications may be made without departing from the spiπt and scope of the invention For example, some of the steps descπbed herein may be order independent, and thus can be performed in an order different from that descπbed

D07044WO01 35

Claims

ClaimsWe claim:

1. A method for altenng the soundfϊeld m an electroacoustic channel in which a first audio signal is applied by a first electromechanical transducer to an acoustic space, causing changes in air pressure in the acoustic space, and a second audio signal is obtained by a second electromechanical transducer in response to changes in air pressure in the acoustic space, comprising. establishing, in response to the second audio signal and at least a portion of the first audio signal, a transfer function estimate of the electroacoustic channel, said transfer function estimate being derived from one or a combination of transfer functions selected from a group of transfer functions, said transfer function estimate being adaptive in response to temporal variations in the transfer function of the electroacoustic channel; and obtaining one or more filters whose transfer function is based on the transfer function estimate and filtering with the one or more filters at least a portion of the first audio signal, which portion of the first audio signal may or may not be the same portion as said first recited portion of the first audio signal.

2. A method according to claim 1 further comprising implementing said transfer function estimate with one or more of a plurality of time- invariant filters.

3. A method according to any one of claims 1-3 wherein the transfer function estimate is adaptive in response to a time average of temporal vaπations in the transfer function of the electroacoustic channel.

4. A method according to claim 3 wherein said one or more of a plurality of time- mvaπant filters compπse: one or more infinite impulse response (HR) filters; or at least two filters in cascade, the first filter being an HR filter and the second filter being an finite impulse response (FIR) filter.

5. A method according to any one of claims 1-4 wherein: said transfer function estimate is derived from one or a combination of transfer functions selected from a group of transfer functions by employing an error minimization technique;

D07044WO01 36 said transfer function estimate is established by cross fading from one to another of said one or combination transfer functions selected from a group of transfer functions by employing an error minimization technique; or said transfer function is established by selecting two or more of said transfer functions from said group of transfer functions and forming a weighted linear combination of them based on an error minimization technique.

6. A method according to any one of claims 1-5 wherein the characteristics of one or more of the group of transfer functions includes the impulse responses of the electroacoustic channel across a range of variations in impulse responses with time.

7. A method according to claim 6 wherein the characteristics of said group of transfer functions are obtained according to an eigenvector method.

8. A method according to any one of claims 1 -16 wherein: said first electromechanical transducer comprises at least one of a loudspeaker, an earspeaker, a headphone ear piece, or an ear bud; or said second electromechanical transducer comprises a microphone.

9. A method according to any one of claims 1-8 wherein said acoustic space comprises a small acoustic space at least partially bounded by an over-the-ear or an around- the-ear cup, the degree to which the small acoustic space is enclosed being dependant on the closeness and centering of the ear cup with respect to the ear.

10. A method according to claim 9 wherein said variations in the transfer function of the electroacoustic channel result from changes in the location of the small acoustical space with respect to said ear.

11. A method according to any one of claims 1-10 wherein each estimate of the transfer function of the electroacoustic channel comprises an estimate of the channel's magnitude response within a range of frequencies.

12. A method according to any one of claims 1-11 wherein also receives an audio disturbance signal and said first audio signal includes one or more of: an error feedback signal derived from the difference between the second audio signal and an audio signal obtained by applying said first audio signal to the filter based on the

D07044WO01 37 estimate of the transfer function of the electro acoustic channel, said difference being filtered by said one or more filters whose transfer function is an inverted version of the transfer function estimate, or a speech and/or music audio signal

13 A method according to claim 12 wherein the method includes actively cancelling noise, wherein the perceived audio response of the electroacoustic channel reduces or cancels the audio disturbance

14 An apparatus compπsmg means for performing method steps as recited in one or more of Claims 1-13

15 A computer readable storage medium product comprising encoded instructions which, when executing with one or more processors, controls the processors to perfoπn process steps as recited in one or more of Claims 1 -13

16 A use for a pi ocessor based system, compπsmg performing method steps as recited in one or more of Claims 1-13

D07Q44WO01 38