Patent Description:
Suppressing, eliminating, or compensating for echo or reverberation effects while simultaneously transmitting and receiving acoustic signals, e.g., sound including music, speech, etc., are commonly referred to as acoustic echo cancellation (AEC). For example, during a call on hands-free telephones, the voice of a caller reaches from the loudspeaker to the microphone at a remote subscriber and is transmitted back to the caller who experiences echoes of his own voice while speaking. Echo cancellation is employed to reduce such undesired effects. Publication <CIT> discloses a threshold control system for controlling a non-linear processor in an echo canceler, the non-linear processor being configured to remove any signal energy below a threshold that remains in a microphone signal after the echo canceler has subtracted an echo estimate from it. The threshold control system comprises a convergence unit configured to determine an indication of the stability of an adaptive filter, the adaptive filter being configured to continuously model an echo path so as to generate the echo estimate, and a threshold tuner configured to adjust the threshold of the non-linear processor in dependence on the indication. Publication <CIT> discloses an acoustic echo canceler, in which an estimate of an echo signal is determined by an adaptive filter and is subtracted from the input signal by a subtractor. The spectrum estimator determines the frequency spectrum of the estimate of the echo signal, and the filter filters the output signal of the subtractor with a filter having a transfer function dependent on the spectrum determined by the estimator. There is still a desire to further improve the echo cancelling performance of acoustic echo cancelers.

The present invention is defined by the attached independent claims. Other preferred embodiments may be found in the dependent claims. A system for canceling acoustic echoes includes a multiplicity of microphones configured to pick up sound generated by a sound source and transferred from the sound source to the multiplicity of microphones via a multiplicity of unknown transfer paths having a multiplicity of unknown transfer functions, and to provide a multiplicity of electrical microphone signals therefrom. The system further includes a multiplicity of adaptive filters configured to approximate the multiplicity of unknown transfer functions with a multiplicity of estimated transfer functions and to filter one or more electrical signals representative of the sound generated by the sound source with the multiplicity of estimated transfer functions to provide a multiplicity of estimated signals therefrom. The system further includes a microphone calibration block configured to individually, frequency dependently or independently attenuate or amplify the multiplicity of electrical microphone signals dependent on a multiplicity of reference signals derived from the multiplicity of estimated transfer functions, the multiplicity of reference signals representing a multiplicity of predetermined gains or desired transfer functions.

A method for canceling acoustic echoes including picking up with a multiplicity of microphones sound generated by a sound source and transferred from the sound source to the multiplicity of microphones via a multiplicity of unknown transfer paths having a multiplicity of unknown transfer functions, and providing a multiplicity of electrical microphone signals therefrom. The method further includes approximating the multiplicity of unknown transfer functions with a multiplicity of estimated transfer functions of the multiplicity of adaptive filters and filtering a multiplicity of electrical signals representative of the sound generated by the sound source with the multiplicity of estimated transfer functions to provide a multiplicity of estimated signals therefrom. The method further includes individually, frequency dependently or independently attenuate or amplify the multiplicity of electrical microphone signals dependent on a multiplicity of reference signals derived from the multiplicity of estimated transfer functions, the multiplicity of reference signals representing a multiplicity of predetermined gains or desired transfer functions.

The system may be better understood with reference to the following drawings and description. In the figures, like referenced numerals designate corresponding parts throughout the different views.

<FIG> is a signal flow diagram illustrating the principle of adaptive system identification applicable in acoustic echo cancellation (AEC). For simplicity reasons, <FIG> refers to an acoustic echo canceler operating in the time domain but other acoustic echo cancelers, e.g., operating in sub-bands or in the frequency domain, are applicable as well. Acoustic echo cancellation can be attained, e.g., by subtracting from a total sound signal containing echoes an estimated echo signal representing an estimate of these echoes. To provide an estimate of the actual echo signal, algorithms have been developed that operate in the time domain and that may employ adaptive digital filters for processing time-discrete signals. The employed adaptive digital filters may operate in such a way that the network parameters which define the transmission characteristics of the filter are optimized with reference to a predetermined quality function. A quality function is implemented, for example, by minimizing the average square errors of the output signal of the adaptive network with reference to a reference signal.

In the exemplary arrangement shown in <FIG>, an unknown system <NUM> and an adaptive filter <NUM> operate in parallel. The unknown system <NUM> converts, according to its transfer function w(n), an input signal x(n) from a signal source <NUM> into a signal y(n). The signal source <NUM> may be a loudspeaker and input signal x(n) may be a signal supplied to and reproduced by the loudspeaker. Signal y(n) may be an output signal of a microphone <NUM> that picks up sound reproduced by the loudspeaker and transferred via the unknown system <NUM> to the microphone <NUM>. The adaptive filter <NUM> converts, according to its transfer function w̃(n), the input signal x(n) into a signal d̂(n). The signal y(n), which is the input signal x(n) distorted by the unknown system <NUM> having the unknown transfer function w(n), serves as a desired signal. The output of the adaptive filter <NUM>, i.e., the signal d̂(n), is deducted from the input signal x(n) by the adaptive filter <NUM> under control of a filter controller <NUM> dependent on the input signal x(n) and an error signal e(n). Employing, e.g., the known Least Mean Square (LMS) algorithm, the filter controller <NUM> adjusts filter coefficients of the adaptive filter <NUM> in an iteration loop such that the error signal e(n), which is the difference between the signal y(n) and signal d̂(n) as represented by a subtractor <NUM> in <FIG>, is minimized. Thus the signal d̂(n) approaches signal y(n) and the unknown transfer function w(n) is approximated by transfer function w̃(n) so that, in terms of cancellation, maximum elimination of the signal y(n), e. g,, an echo signal, is attained by the signal d̂(n).

The LMS algorithm is based on the so-called method of the steepest descent (gradient descent method) and approximates the gradient in a simple manner. The algorithm operates in a time-recursive manner, that is, the algorithm runs over and over again with each new data set and the solution is updated correspondingly. Due to its small complexity, its numerical stability and the low memory requirement, the LMS algorithm is eminently suitable for adaptive filters and for adaptive controls. Alternatively, the adaptation method may employ, for example, recursive least squares, QR decomposition least squares, least squares lattice, QR decomposition lattice or gradient adaptive lattice, zero forcing, stochastic gradient algorithms etc. Infinite Impulse Response (IIR) filters or Finite Impulse Response (FIR) filters may be used as adaptive filters in connection with the above adaptation algorithms.

Despite the fact that a time domain acoustic echo canceler has some advantages, such as a minimum consumption of memory and a low latency, there are also some drawbacks involved with the time domain operation, such as a low cancelling performance, a high processor load and limited control of the algorithm. Other types of acoustic echo cancelers, e.g., those operating in sub-bands or in the frequency domain, may be more suited in some applications, and may dispose of a higher degree of flexibility, e.g., in order to control the adaptation step size or to employ an adaptive post filter (APF) that implements a residual echo suppression (RES) functionality. A very efficient acoustic echo canceler is based on the known frequency domain adaptive filter (FDAF) structure which may use an overlap-save (OLS) method, acting as signal processing frame and may utilize an overlap of at least <NUM>%.

An acoustic echo canceler with an overlap-save based, frequency domain adaptive filter is shown in detail in <FIG>. It should be noted that the latency between the input signal and the output signal can be controlled by the size of the overlap. High efficiency can be achieved if a minimum overlap of <NUM>%, which is N/<NUM> with N denoting the length of a fast Fourier transformation (FFT), is used. At the same time this also reflects the maximum possible latency of N/<NUM> [Samples]. One can now freely choose the size of the overlap and hence adjust the system to the least acceptable latency and/or adjust to the available processing power, to find a good compromise between processing power and latency.

A Frequency Domain Block Least Mean Square (FBLMS) algorithm is a very efficient approach to implementing an adaptive filter in the frequency domain. The FBLMS algorithm may be implemented as an overlap-save algorithm or an overlap-add algorithm. The overlap-save algorithm can be implemented more efficiently than the overlap-add algorithm and is thus used in the acoustic echo cancelers described below. The acoustic echo canceler using the overlap-save FBLMS algorithm may include, according to <FIG>, a functional block <NUM> for providing a new input block signal, a functional block <NUM> for using (in case of <NUM>% overlap) the last half of the output block signal, a functional block <NUM> for filling with zeros, a functional block <NUM> for erasing the second half of the block, a functional block <NUM> for adding zeros, and a functional block <NUM> for forming a conjugated complex spectrum. Further, in the acoustic echo canceler, signal multipliers <NUM>, <NUM> and <NUM>, functional blocks for FFT <NUM>, <NUM> and <NUM>, two functional blocks for Inverse Fast Fourier Transformation (IFFT) <NUM> and <NUM>, a delay unit <NUM>, and two signal adders <NUM> and <NUM> are included.

The output of the functional block <NUM> provides a new input block signal that is supplied to the functional block <NUM> to execute an FFT. An output of the functional block <NUM> is supplied to the signal multiplier <NUM> and to an input of the functional block <NUM> to form a conjugated complex spectrum. An output of the signal multiplier <NUM> is supplied to the functional block <NUM> for IFFT, the output of which is supplied to the functional block <NUM> to use (in case of <NUM>% overlap) the last half of the output block signal. The output of the functional block <NUM> is supplied to the signal adder <NUM>, the output of which is supplied to the functional block <NUM> to fill (in case of <NUM>% overlap) the first half of the block with zeros.

The output of the functional block <NUM> for filling (in case of <NUM>% overlap) the first half of the block with zeros is supplied to the functional block <NUM> for FFT, the output of which is supplied to the signal multiplier <NUM>. The output of the signal multiplier <NUM> is supplied to the signal multiplier <NUM>. In turn, the output of the signal multiplier <NUM> is supplied to the signal adder <NUM>. The output of the delay unit <NUM> is supplied to the other input of the signal adder <NUM> and is input to the functional block <NUM> for IFFT.

The output of the functional block <NUM> for IFFT is supplied to the functional block <NUM> to erase the last half of the block, the output of which is supplied to the functional block <NUM> to add zeros. The output of the functional block <NUM> for adding zeros to the last half of the block is supplied to the functional block <NUM> for FFT, the output of which is supplied to the other input of the signal multiplier <NUM>. The output of the functional block <NUM> for FFT is also supplied to the functional block <NUM> to form a conjugated complex spectrum, the output of which is supplied to the other input of the signal multiplier <NUM>.

The input of the functional block <NUM> for building a new input block signal receives the input signal x(n) and forms an input block signal, which, according to a chosen overlap consists of the signal portion of a previously processed signal block "old" and a signal portion of the currently received input signal x(n). This input block signal is supplied to the functional block <NUM> for FFT, at the output of which a signal X(ejΩ, n), which is transformed into the frequency domain, is provided accordingly. This output signal X(ejΩ, n) is subsequently supplied to the signal multiplier <NUM> as well as to the functional block <NUM> to form a conjugated complex spectrum.

The signal D̂ (ejΩ, n) is supplied to the functional block <NUM> for IFFT by way of multiplying the signal X(ejΩ, n) with an output signal W̃(ejΩ, n) of the functional block <NUM> in the signal multiplier <NUM>, whereby a corresponding output signal transformed into the time domain is formed at the output of the functional block <NUM>. This output signal is subsequently supplied to the functional block <NUM> to use (in case of <NUM>% overlap) the last half of the output block signal for further processing. In this functional block, the last half of the block signal (overlap is <NUM>%) is used to build the signal d̂(n).

The output signal d̂(n) is supplied to the signal adder <NUM>, the other input of which receives the signal y(n). The signal d̂(n) is subtracted from signal y(n) in the signal adder <NUM>, whereby the error signal e(n) is formed at the output of the signal adder <NUM>. The error signal e(n) is supplied to the functional block <NUM> to fill with zeros, so that the first half of this error block signal is filled with zeros (overlap is <NUM>%, see functional block <NUM> for building the new input block signal).

The signal embodied in this manner at the output of the functional block <NUM> for filling with zeros is routed to the input of the functional block <NUM> for FFT, at the output of which the signal E(ejΩ, n), which is transformed into the frequency range, is provided. In the subsequent signal multiplier <NUM>, this signal E(ejΩ, n) is multiplied with the signal X*(ejΩ, n) which emerges from the output signal X(ejΩ, n) of the functional block <NUM> for FFT by processing in the functional block <NUM> to form the conjugated complex spectrum. The signal emerging therefrom at the output of the signal multiplier <NUM> is subsequently supplied to the signal multiplier <NUM>.

In the signal multiplier <NUM>, this output signal is multiplied with <NUM>·µ(ejΩ, n) wherein µ(ejΩ, n) corresponds to the time-and frequency dependent step size of the adaptive filter. The output signal of the signal multiplier <NUM> formed in such a manner is subsequently added in the signal adder <NUM> to the signal W̃(ejΩ, n) which emerges from the output signal W̃(ejΩ, n+<NUM>) of the functional block <NUM> for FFT by means of a corresponding delay via the delay unit <NUM>. The resulting output signal W(ejΩ, n+<NUM>) of the signal adder <NUM> is subsequently supplied to the functional block <NUM> for IFFT, which accordingly provides an output signal and which is transformed back into the time domain.

Subsequently, the second half of the block of filter coefficients of the FIR filter is discarded in functional block <NUM> and is substituted with coefficient values of zeros in functional block <NUM>. By means of the functional block <NUM> the signal is, in turn, transformed into a signal in the frequency domain and is supplied to the signal multiplier <NUM> for multiplication with signal X(ejΩ, n). The signal processing block embodied in the signal flowchart according to <FIG> by the functional block <NUM> for the IFFT, the functional block <NUM> for erasing the coefficients of the last half of the block, the functional block <NUM> for adding zeros, and functional block <NUM> for FFT are identified as "constraints" in response to the overlap save FBLMS algorithm.

In the present examples, the FBLMS algorithm comprises a standardized, frequency-selective, time variant adaptation step size µ(ejΩ, n). This adaptation step size µ(ejΩ, n) is normalized to the power density spectrum of the input signal X(ejΩ, n). The normalization has the effect of compensating fluctuations of the amplitude of the input signal, which allows for adaptive filters to converge with a higher speed. This normalization has a positive effect, in particular due to the FBLMS algorithm in an acoustic echo canceler system, because a speech signal, which encompasses a distinct fluctuation in amplitude, is used as input signal and conventional adaptive filters thus always encompass a slow convergence speed. This disadvantage of conventional adaptive filters can be avoided in a simple manner by means of the normalization in the frequency domain.

Referring to <FIG>, as the acoustic echo canceler shown in <FIG> is only capable of controlling the linear part of the unknown system, an additional APF <NUM> may be connected downstream of subtractor <NUM> in order to further reduce echoes originating, e.g., from non-linear parts of the unknown system <NUM>. In situations when one wants to reduce echoes, such as signals radiated from a loudspeaker such as source <NUM> and picked-up with a microphone such as microphone <NUM>, as is common e.g. in handsfree systems, handhelds or mobile devices, a problem occurs once the loudspeaker is driven beyond a certain upper level. In such a situation, non-linearities will inevitably be generated, mostly due to the loudspeaker, especially if it is a miniature one. Since non-linearities cannot be handled by a common acoustic echo canceler, so-called residual echoes will undesirably remain in the output signal. For this reason a residual echo suppressor may be employed. The degree of non-linearities generated by the loudspeaker depends on the volume as well as on the content of the input signal.

In the exemplary acoustic echo cancelers described below, the residual echo suppressor is automatically adjusted to the current situation, e.g., to the energy content of the input signal and as such to the potential degree of created nonlinearities. Setting the residual echo suppressor to a fix, aggressive state would negatively influence the acoustic quality of the output signal, e.g., of the speech signal, especially in cases when no or only very little residual echos are present. The systems and methods described below are designed to keep the degree to which the residual echo suppressor will be used as low as possible, while at the same time adjusting its performance dependent on the current input signal energy. The systems and methods do not require much processing power and memory.

An acoustic echo canceler may be operated in diverse undesired situations such as double talk or abrupt changes of the room impulse response (RIR), also referred to as secondary path. An adaptive control of the adaptation step size µ(ejΩ, n) can be described as follows: <MAT> wherein.

In connection with a FDAF as shown in <FIG>, a system and method for calculating an adaptive, adaptation step size µ(ejΩ, n) and an adaptive post filter based on a statistical approach similar to <NPL>, is used which can be described as follows:.

The frequency and time dependent APF filter transfer function H (e jΩ, n) can be calculated, once the adaptive adaptation step size µ(ejΩ, n) is known, simply by subtracting the latter from one. Further, the system distance G(ejΩ, n) may be estimated utilizing a purely statistical approach. With the tuning parameter C, one can control the adaptation step size µ(ejΩ, n) to better perform in double talk situations - the smaller C, the better the double talk detection (DTD) performance - or to enable the acoustic echo canceler to quickly re-adapt in the case of a rapid secondary path change. All these calculation steps may take place in a filter control block <NUM>, which substitutes controller <NUM> used in the acoustic echo canceler shown in <FIG> and which controls filters <NUM> and <NUM>.

The purpose of the adaptive post filter <NUM> is to suppress potential, residual echos, remaining within the output signal e(n) of the (linear) acoustic echo canceler. The functional principle of the acoustic echo canceler is comparable to that of a single channel noise reduction method, e.g., in terms of frequency subtraction. Thus, the adaptive post filter <NUM> represents a non-linear signal processing stage which may create unwanted acoustical artifacts, for example, musical tones. A way to avoid artefacts is to limit the (frequency depndent) damping of the adaptive post filter <NUM> to a minimum threshold Hmin, as shown below: <MAT> If Hmin is set to values of approximately Hmin ≥ -<NUM> [dB] , fewer or even no acoustically disturbing artifacts will be generated, while at values of about Hmin ≤ -<NUM> [dB] more acoustic artifacts may be perceived so that the minimum threshold Hmin that resides within this value range may be selected.

As already mentioned, the purpose of the adaptive post filter <NUM> is to suppress residual echoes which could otherwise not be reduced by the linear adaptive echo canceler, for example, due to nonlinearities of the unknown system. The most relevant, non-linear part within the unknown loudspeaker-enclosure-microphone (LEM) system is dependent on the utilized loudspeaker. Thereby it holds true that the higher the volume, i.e. the higher the excursion of the voice coil, the more probable it is that the loudspeaker will generate nonlinearities. This leads to the decision to use a volume setting (representing the amplitude of the input signal), or more generally, an input power (i.e., the power of the input signal) controlled, minimum threshold Hmin(px(n)), where px(n) designates the estimated, time varying power of the input signal x(n): <MAT> wherein α is a smoothing parameter (α∈[<NUM>,. Thereby, the input power controlled, minimum threshold Hmin(px(n)) may be realized as follows: <MAT> wherein.

This means, if the current input power pxdB(n) (in [dB]) remains below a certain input power threshold pxdBTH , a fix minimum threshold HMinInit will be used for Hmin(px(n)). Otherwise, the momentary threshold Hmin(px(n)) will be calculated based on the momentary input power pxdB(n), the minimum threshold HMinInit and the input power threshold pxdBTH in such a way that it will linearly rise (in the logarithmic domain), the higher the input power.

<FIG> shows a multiple single-channel acoustic echo canceler utilizing a single channel reference signal (single loudspeaker playing back the mono input signal x(n) from signal source <NUM>) and M><NUM> error microphones <NUM><NUM>. <NUM>M acoustically coupled to source <NUM> via transfer functions w<NUM>(z). wM(z) of unknown systems (paths) <NUM><NUM>. As can be seen, in this case it is sufficient to employ the previously disclosed, single channel acoustic echo canceler as shown in <FIG>, M times. The transfer functions w<NUM>(z). wM(z) of the unknown systems (paths) <NUM><NUM>. <NUM>M are approximated by adaptive filters <NUM><NUM>. <NUM>M with transfer functions w̃<NUM>(z). w̃M(z) under control of filter controllers <NUM><NUM>. <NUM>M based on the input signal x(n) and error signals e<NUM>(n). The adaptive filters <NUM><NUM>. <NUM>M provide signals d̂<NUM>(n). d̂M(n) which are subtracted from output signals y<NUM>(n). yM(n) of the unknown systems (paths) <NUM><NUM>. <NUM>M by subtractors <NUM><NUM>. <NUM>M to generate the error signals e<NUM>(n).

A situation as depicted in <FIG> occurs, for example, in mobile or handheld devices, which have small dimensions and are able to fill a whole horizontal plane, or ideally a whole three-dimensional room with sound, i.e. to mimic an ideal, isotropic radiator. In this case, it is not important whether the device is able to playback stereo or multi-channel signals, such as <NUM> surround sound channels or the like, since the main focus is to create an ideal (mono) isotropic wave-field. In this special case, all speakers involved can be regarded as one virtual speaker (source <NUM>) having a somewhat omnidirectional, cylindrical or spherical, radiation pattern. Microphones in the proximity of this virtual, omnidirectional source that have a similar distance to it would then, ideally, pick-up the same sound pressure level (SPL).

A device <NUM> approximately fulfilling the requirements outlined above is shown in <FIG>. It includes, for example, five regularly distributed broadband speakers <NUM> (two not visible in <FIG>) mounted at a cylindrical body <NUM> and a down-firing subwoofer <NUM> mounted at the bottom of the device (not visible in <FIG>), and eight equiangularly distributed, omnidirectional microphones <NUM> (five not visible in <FIG>), each located in the center of a cavity <NUM> mounted at the body <NUM> of the device <NUM>. As can be seen, the distance of the layer where the speakers <NUM> are mounted at the cylindrical body <NUM>, and the layer at which the microphones <NUM> are mounted are in parallel, i.e. the distances of the microphones <NUM> to the virtual, cylindrical radiating speaker <NUM>, are the same.

Referring to <FIG>, in connection with devices as shown in <FIG>, the echo cancelling performance of an acoustic echo canceler and of potentially following stages, such as a beamformer, can be further improved by introducing a self-calibrating multi-microphone arrangement which can be integrated into the acoustic echo canceler depicted in <FIG>. The self-calibrating multi-microphone arrangement may include (one or) a multiple of microphones <NUM><NUM>. <NUM>M, a microphone calibration block <NUM> and controllable gain blocks <NUM><NUM>. The controllable gain blocks <NUM><NUM>. <NUM>M are connected upstream of the subtractors <NUM><NUM>. <NUM>M and are controlled by the microphone calibration block <NUM> dependent on signals ŵ<NUM>(z). ŵM(z) from filter control blocks <NUM><NUM>. Thereby, once the multiple adaptive filters <NUM><NUM>. <NUM>M have converged, which may, e.g., be indicated if the maximum of the power of the error signals max{pe(n)} with pe(n) = [pe1(n),. , peM(n)] and pem(n), ∀m∈[<NUM>,. , M], which are calculated in analogy to px(n) = α px(n-<NUM>) + (<NUM>-α) x(n)<NUM>, undercuts an adjustable lower threshold peTH , all current filter coefficient sets Ŵz (z), ∀m∈[<NUM>,. ,M] will be used to calculate scaling values gm(n),∀m∈[<NUM>,. M] for M microphone signals ym(n),∀m ∈[<NUM>,. , M] as follows: <MAT>.

If only a single microphone (not shown) instead of a multiplicity of microphones <NUM><NUM>. <NUM>M is used to pick up sound generated by the sound source <NUM> and transferred from the sound source <NUM> to the single microphone via an unknown transfer path having one or more unknown transfer function, and, thus, to provide only a single electrical microphone signal, the microphone calibration block may individually attenuate or amplify the single electrical microphone signal dependent on a first reference signal that may represent a predetermined reference level or one or more desired (estimated) transfer functions. The first reference signal may be provided by a memory block (not shown) that stores and supplies the first reference signal.

If, however, a multiplicity of microphones <NUM><NUM>. <NUM>M is used to pick up sound generated by the sound source <NUM> and transferred from the sound source <NUM> to the multiplicity of microphones <NUM><NUM>. <NUM>M via a multiplicity of unknown transfer paths having a multiplicity of unknown transfer functions, and to provide a multiplicity of electrical microphone signals, the microphone calibration block <NUM> may individually, frequency dependently or independently attenuate or amplify, e.g., filter, the multiplicity of electrical microphone signals dependent on one or more second reference signals derived from one or more of the multiplicity of estimated transfer functions. The one or more second reference signals may represent one predetermined gain (frequency dependent or independent amplification or attenuation) or a multiplicity of predetermined gains. Alternatively, the one or more second reference signals may represent a mean of some or all of the multiplicity of estimated transfer functions or a selected one of the estimated transfer functions that serves as a reference for further adapting one, some or all of the other transfer functions to be estimated. For example, a single second reference signal may represent one of the estimated transfer functions other than the estimated transfer function corresponding to an electrical microphone signal to be filtered dependent on this single second reference signal.

Claim 1:
A system for canceling acoustic echoes comprising:
a multiplicity of microphones (<NUM><NUM> - <NUM>M) configured to pick up sound generated by a sound source (<NUM>) and transferred from the sound source (<NUM>) to the multiplicity of microphones (<NUM><NUM> - <NUM>M) via a multiplicity of unknown transfer paths (<NUM><NUM> - <NUM>M) having a multiplicity of unknown transfer functions (<NUM><NUM> - <NUM>M), and to provide a multiplicity of electrical microphone signals therefrom;
a multiplicity of adaptive filters (<NUM><NUM> - <NUM>M) configured to approximate the multiplicity of unknown transfer functions with a multiplicity of estimated transfer functions and to filter a multiplicity of electrical signals representative of the sound generated by the sound source (<NUM>) with the multiplicity of estimated transfer functions to provide a multiplicity of estimated signals therefrom; and
a microphone calibration block (<NUM>, <NUM><NUM> - <NUM>M) configured to individually, frequency dependently or independently attenuate or amplify the multiplicity of electrical microphone signals dependent on a multiplicity of reference signals derived from the multiplicity of estimated transfer functions, the multiplicity of reference signals representing a multiplicity of predetermined gains or desired transfer functions.