Patent Application: US-201415320065-A

Abstract:
the present disclosure relates to a circuit and method for cancelling the acoustic feedback in public address systems , sound reinforcement systems , hearing aids , teleconference systems or hands - free communication systems , comprising providing a filter for tracking the acoustic feedback path between the radiator device broadcasting and the receiver device , the input of said filter being the signal applied to the radiator device ; updating the filter for tracking the acoustic feedback path based on time - domain information contained in the cepstrum of the receiver device signal , or updating the filter for tracking the acoustic feedback path based on time - domain information contained in the cepstrum of the signal applied to the radiator device , or updating the filter for tracking the acoustic feedback path based on time - domain information contained in the cepstrum of the difference between the receiver device signal and the signal applied to the radiator device filtered by the filter .

Description:
as any afc method , the present disclosure identifies and tracks the feedback path using an adaptive filter . but , instead of the traditional adaptive filter algorithms based on wiener theory or least squares , the present disclosure updates the adaptive filter based on time - domain information contained in the cepstrum of the microphone signal and such a scheme is illustrated in fig3 . the system depicted in fig2 and 3 is described by the following time - domain equations from ( 7 ), the frequency - domain realationship between the system input signal u ( n ) and the microphone signal y ( n ) is obtained as ln └( e jω )┘= ln └ u ( e jω )┘+ ln └ 1 + g ( e jω , n ) h ( e jω , n )┘− ln { 1 − g ( e jω , n )[ f ( e jω , n )− h ( e jω , n )]} ( 9 ) if | g ( e jω , n ) h ( e jω , n )|& gt ; 1 , the middle term in ( 9 ) can be expanded in taylor &# 39 ; s series according to and if | g ( e jω )[ f ( e jω )− h ( e jω , n ),]& lt ; 1 , which is the necessary and sufficient condition to ensure the system stability , the rightmost term can also be expanded in taylor &# 39 ; s series according to replacing ( 10 ) and ( 11 ) in ( 9 ), and applying the inverse fourier transform as follows the cepstral - domain relationship between the input signal u ( n ) and the microphone signal y ( n ) is obtained as where τ is the quefrency index and {. }* k denotes the k th convolution power . in the system depicted in fig3 , the cepstrum c y ( τ , n ) of the microphone signal is the cepstrum c u ( τ ) of the input signal added to a time - domain series in function of g ( m , n ), f ( m , n ) and h ( m , n ). the presence of this time - domain series is due to the disappearance of the logarithm operator in the last two terms of ( 12 ). so , for these series in ( 13 ), the sample index m is equivalent to the quefrency index τ , i . e ., f ( m , n )= f ( τ , n ). but , in order to emphasize that it is a time - domain series , it is represented in ( 19 ) by the sample index in . this series is formed by k - fold convolutions of g ( m , n )* h ( m , n ) and g ( m , n )*[ f ( m , n ) h ( m , n )] therefore , it is crucial to understand that the cepstrum c y ( τ , n ) of the microhpne signal contains time - domain information about the afc system of fig3 through g ( z , n ), f ( z , n ) and h ( z , n ). however , the practical existence of these time - domain impulse responses in c y ( τ , n ) depends on the number of points wherewith c y ( τ , n ) is calculated and also if the size of the time - domain observation window is large enough to include their effects . morever , it is crucial to realize that , regardless of the value of h ( m , n ), the open - loop impulse response g ( m , n )* f ( m , n ) is always present in c y ( τ , n ). the functional scheme of the present disclosure is depicted in fig4 . an observation window of the microphone signal y ( n ) has its spectrum y ( e jω ) and cepstrum c y ( τ , n ) calculated using a n fft - points fast fourier transform ( fft ). then , the present disclosure calculates a time - domain signal p y ( m , n ) from c y ( τ , n ). in fact , the time - domain signal p y ( m , n ) is calculated from the time - domain series present in c y ( τ , n ) according to ( 13 ). finally , the time - domain signal p y ( m , n ) is used to update the filter h ( z , n ). the contents of the time - domain signal p y ( m , n ) may be varied as well as the way it is calculated from c y ( τ , n ). a possible solution is depicted in fig5 , in which p y ( m , n ) is an estimate { circumflex over ( f )} y ( m , n ) of the impulse response of the acoustic feedback path . for that purpose , the present disclosure may calculate { g ( m , n )* f ( m , n )}{ circumflex over ( y )}, an estimate of the system open - loop impulse response g ( m , n * f ( m , n ), from c y ( τ , n ). this calculation can be performed by selecting the first l g + l h samples from c y ( τ , n ) and making their first l d − 1 samples equal to zero . alternatively , this calculation can be performed by selecting the samples of c y ( τ , n ) that has a magnitude value above a threshold and also making their first l d − 1 samples equal to zero . the forward path g ( z , n ) can be accurately calculated from its input ( e ( n )) and output ( x ( n )) signals by any open - loop system identification method . then , assuming the existence of an estimate ĝ ( m , n ) of the forward path impulse response , the present disclosure may calculate { circumflex over ( f )} y ( m , n ), an instantaneous estimate of the impulse response f ( m , n ) of the feedback path , according to f y ( m , n )={ g ( m , n )* f ( m , n )}{ circumflex over ( y )}* ĝ − 1 ( m , n ). ( 14 ) finally , the present disclosure may use { circumflex over ( f )} y ( m , n ) to update the filter h ( z , n ). the update of h ( z , n ) may be performed according to h ( m , n )= λ h ( m , n − 1 )+( 1 − λ ) { circumflex over ( f )} y ( m , n ), ( 15 ) where 0 ≦ λ & lt ; 1 is a factor that controls the trade - off between robustness and tracking rate . to assess the performance of the proposed method in a pa system , an experiment was made to measure the accuracy of its estimate of the impulse response of the feedback path in a simulated environment . for this purpose , the following configuration was used . in order to simulate a pa environment , a measured room impulse response , from [ 6 ], was used as the impulse response f ( m , n ) of the acoustic feedback path . the impulse response was downsamples to f x = 16 khz and then truncated to length l f = 4000 samples , and is illustrated in fig3 the impulse response of the forward path was defined as simply defined as a delay and a gain according to g ( m , n )=[ 0 0 . . . 0 g ( 402 , n )]. ( 16 ) the gain g ( 402 , n ) was chosen such that the system had a stable gain margin of 3 db . as sugested in [ 1 , 3 ], the delay is equivalent to 25 ms . the performance of the adaptive filter was evaluated by the normalized misalignment defined as that measures how near the estimate h ( m , n ) is of the real f ( m , n ). the signal database used in the following simulations is formed by 10 speech signals . each speech signal is formed by several basic signals from a speech database . each basic signal consists of one short sentence with duration of 4 s and original sampling rate of 48 khz but downsampled to f s = 16 khz . all basic signals were recorded in the talkers &# 39 ; native language , and their nationalities and genders follow : 4 americans ( 2 males and 2 females ), 2 british ( 1 male and 1 female ), 2 french ( 1 male and 1 female ) and 2 germans ( 1 male and 1 female ). but since the performance assessment of adaptive filters needs longer signals , several basic signals from the same talker were concatenated and had their silence parts removed by a voice activity detector ( vad ), resulting in 10 speech signals ( 1 signal by talker ) with duration of t s = 20 s . the values of λ and l h were chosen empirically , within a pre - defined range , in order to minimize the average misalignment and n fft = 2 15 samples . the method started only after 12 . 5 ms of simulation to avoid inaccurate initial estimates for performance comparison using speech as source signal , the state - of - art pem - afrow method was used . all its parameters had the same values as originally proposed in [ 1 ], but adjusted to f s = 16 khz . the stepsize and length of the adaptive filter were also obtained empirically in order to minimize the average misalignment . fig7 compares the average misalignments obtained by both methods using speech signal as source and a source - signal - to - noise ( snr ) of 30 db . as can be seen , the present disclosure obtained a lower misalignment , what means that it achieved an improvement in the estimation of the impulse response of the feedback path when compared to the state - of - art pem - afrow method . the small advantage of the pem - afrow in the low time is explained by the fact that , unlike the present disclosure , the pem - afrow is applied since the beginning of the simulation . further , the same cepstral analysis , that was applied to the microphone signal y ( n ), is also extended to the error e ( n ) and loudspeaker x ( n ) signals . as a result , the present disclosure discloses a circuit and method wherein the acoustic feedback cancellation is performed in an alternative fashion . more specifically , the method disclosed in the present disclosure calculates , from the cepstra of the system signals , time - domain signals that can be , for instance , estimates of the environment impulse response . these time - domain signals can be used separately , as in fig3 and 9 , or combined , as in fig1 , to update a filter that is responsible for cancelling the acoustic feedback . the method is capable to outperform existing methods . the main difference with prior art schemes is twofold . first , there is no assumption on the nature of the system input signal u ( n ). second , in addition to the feedback removal , the present disclosure does not modify the signals that circulate in the system and thus does not affect the main system fidelity . furthermore , the method can be implemented in real - time because of its low computacional complexity . from ( 7 ), the frequency - domain relationships between the system input signal u ( n ) and the error e ( n ) and loudspeaker x ( n ) signals are , respectively , obtained as ln └ e ( e jω )= ln └ u ( e jω )┘− ln { 1 − g ( e jω , n )└ f ( e jω , n )− h ( e jω , n )┘} ( 20 ) ln └ x ( e jω )┘= ln └ u ( e jω )┘+ ln └ g ( e jω , n )┘− ln { 1 − g ( e jω , n )[ f ( e jω , n )− h ( e jω , n )]} ( 21 ) if | g ( e jω )[ f ( e jω )− h ( e jω , n ),]& gt ; 1 , which is the necessary and sufficient condition to ensure the system stability , the rightmost term in ( 20 ) and ( 21 ) can be expanded in taylor &# 39 ; s series according to ( 11 ). replacing ( 11 ) in ( 20 ) and ( 21 ), and applying the inverse fourier transform as follows the cepstral - domain relationships between the input signal u ( n ) and error e ( n ) and loudspeaker x ( n ) signals are , respectively , obtained as the cepstrum c e ( τ , n ) of the signal e ( n ) is the cepstrum c u ( τ ) of the signal u ( n ) added to a time - domain series in function of g ( m , n ), f ( m , n ) and h ( m , n ). the cepstrum c x ( τ , n ) of the signal x ( n ) also includes the cepstrum c g ( τ ) of the forward path g ( z , n ). in c e ( τ , n ) and c x ( τ , n ), the presence of the time - domain series are due to the disappearance of the logarithm operators in the rightmost term of ( 22 ) and ( 23 ), respectively . so , for these series in ( 24 ) and ( 25 ), the sample index m is equivalent to the quefrency index τ , i . e ., f ( m , n )= f ( τ , n ). but , in order to emphasize that they are time - domain series , they are represented in ( 24 ) and ( 25 ) by the sample index m . these series are formed by k - fold convolutions g ( m , n )*[ f ( m , n )− h ( m , n )]. therefore , the cepstra c e ( τ , n ) and c x ( τ , n ) contain time - domain information about the afc system through g ( z , n ), f ( z , n ) and h ( z , n ). however , the practical existence of these time - domain impulse responses in c e ( τ , n ) and c x ( τ , n ) depends on the number of points wherewith c e ( τ , n ) and c x ( τ , n ) are calculated and also if the size of the time - domain observation windows is large enough to include their effects . the functional scheme of the present disclosure is depicted in fig1 . from fig1 ( a ) , an observation window of the error signal e ( n ) has its spectrum e ( e jω ) and cepstrum c e ( τ , n ) calculated using a n fft - points fast fourier transform ( fft ). then , the present disclosure calculates the time - domain signal p e ( m , n ) from c e ( τ , n ). in fact , the time - domain signal p e ( m , n ) may be calculated from the time - domain series present in c e ( τ , n ) according to ( 24 ). finally , the time - domain signal p e ( m , n ) is used to update the filter h ( z , n ). from fig1 ( b ) , an observation window of the loudspeaker signal x ( n ) has its spectrum x ( e jω ) and cepstrum c x ( τ , n ) calculated using a n fft - points fast fourier transform ( fft ). then , the present disclosure calculates the time - domain signal p x ( m , n ) from c x ( τ , n ). in fact , the time - domain signal p x ( m , n ) is calculated from the time - domain series present in c x ( τ , n ) according to ( 25 ). finally , the time - domain signal p x ( m , n ) is used to update the filter h ( z , n ). alternatively , as depicted in fig1 ( c ) , the time - domain signals p y ( m , n ), p e ( m , n ) and p x ( m , n ) can be combined to update the filter h ( z , n ). this can be performed through , for instance , a linear combination . the contents of the time - domain signal p e ( m , n ) may be varied as well as the way it is calculated from c e ( τ , n ). a possible solution is depicted in fig1 ( a ) , in which p e ( m , n ) is an estimate { circumflex over ( f )} e ( m , n ) of the impulse response of the acoustic feedback path . for that purpose , the present disclosure may calculate { g ( m , n )*[ f ( m , n )− h ( m , n )]}{ circumflex over ( e )}, an estimate of the estimation error g ( m , n )*[ f ( m , n )− h ( m , n )] of the open - loop impulse response provided by the filter h ( z , n ), from c e ( τ , n ). this calculation can be performed by selecting the first l g + l h samples from c e ( τ , n ) and making their first l d − 1 samples equal to zero . alternatively , this calculation can be performed by selecting the samples of c e ( τ , n ) that has a magnitude value above a threshold and also making their first l d − 1 samples equal to zero . the forward path g ( z , n ) can be accurately estimated from its input ( e ( n )) and output ( x ( n )) signals by any open - loop system identification method . then , assuming the existence of an estimate ĝ ( m , n ) of the forward path impulse response , the present disclosure may calculate [ f ( m , n )− h ( m , n )]{ circumflex over ( e )}, an estimate of the estimation error f ( m , n )− h ( m , n ) of the feedback path provided by the adaptive filter h ( z , n ), according to [ f ( m , n )− h ( m , n )] e ={ g ( m , n )*[ f ( m , n )− h ( m , n )]}{ circumflex over ( e )}* ĝ − 1 ( m , n ). ( 34 ) thereafter , the present disclosure may calculate { circumflex over ( f )} e ( m , n ), an instantaneous estimate of the impulse response f ( m , n ) of the feedback path , from ( 34 ) according to { circumflex over ( f )} e ( m , n )=[ f ( m , n )− h ( m , n )]{ circumflex over ( e )} + h ( m , n − 1 ). ( 35 ) finally , the present disclosure may use { circumflex over ( f )} e ( m , n ) to update the filter h ( z , n ). the update of h ( z , n ) may be performed according to h ( m , n )= λ h ( m , n − 1 )+( 1 − λ ) f e ( m , n ), ( 23 ) where 0 ≦ λ & lt ; 1 is a factor that controls the trade - off between robustness and tracking rate . similarly , the contents of the time - domain signal p x ( m , n ) may be varied as well as the way it is calculated from c x ( τ , n ). a possible solution is depicted in fig1 ( b ) , in which p s ( m , n ) is an estimate { circumflex over ( f )} x ( m , n ) of the impulse response of the acoustic feedback path . for that purpose , the present disclosure may calculate { g ( m , n )*[ f ( m , n )− h ( m , n )]}{ circumflex over ( x )}, an estimate of the estimation error g ( m , n )*[ f ( m , n )− h ( m , n )] of the open - loop impulse response provided by the filter h ( z , n ), from c x ( τ , n ). this calculation can be performed by selecting the first l g + l h samples from c x ( τ , n ) and making their first l d − 1 samples equal to zero . alternatively , this calculation can be performed by selecting the samples of c ( z , n ) that has a magnitude value above a threshold and also making their first l d − 1 samples equal to zero . assuming the existence of an estimate ĝ ( m , n ) of the forward path impulse response , the present disclosure may calculate [ f ( m , n )− h ( m , n )]{ circumflex over ( x )}, an estimate of the estimation error f ( m , n )− h ( m , n ) of the feedback path provided by the adaptive filter h ( z , n ), according to [ f ( m , n )− h ( m , n )]{ circumflex over ( x )}={ g ( m , n )*[ f ( m , n )− h ( m , n )]}{ circumflex over ( x )} * g − 1 ( m , n ). ( 34 ) thereafter , the present disclosure may calculate { circumflex over ( f )} x ( m , n ), an instantaneous estimate of the impulse response f ( m , n ) of the feedback path , from ( 34 ) according to { circumflex over ( f )} x ( m , n )=[ f ( m , n )− h ( m , n )]{ circumflex over ( x )} + h ( m , n − 1 ). ( 35 ) finally , the present disclosure may use { circumflex over ( f )} x ( m , n ) to update the filter h ( z , n ). the update of h ( z , n ) may be performed according to h ( m , n )= λ h ( m , n − 1 )+( 1 − λ ) { circumflex over ( f )} x ( m , n ), ( 36 ) where 0 ≦ λ & lt ; 1 is a factor that controls the trade - off between robustness and tracking rate . the present disclosure was evaluated through the misalignment ( mis ) and the maximum stable gain ( msg ). the mis ( n ) measures the distance between the impulse responses of the adaptive filter and of the feedback path according to ( 25 ). in order to measure the maximum stable gain of the pa system , a broadband gain k ( n ) was defined , similarly to [ 3 ], as the average magnitude of the forward path frequency response g ( e jω , n ) considering that j ( z , n ) is known , the maximum stable gain ( msg ) of the afc system was defined as where p h denotes the set of frequencies that fulfill the phase condition of the system with the insertion of the adaptive filter , also called critical frequencies of the afc system , so that p h ( n )={ ω |∠ g ( e jω , n )└ f ( e jω , n )┘= 2 kπ , k ∈ z } ( 41 ) the increase in msg ( n ) achieved by the afc methods was denoted as δmsg ( n ). the msg of the system with no afc method was defined as msg 0 = 20 log 10 k 0 . k ( n ) was initialized to a value k 1 such that 20 log 10 k 1 = msg 0 − 3 , i . e ., a 3 db initial gain margin as suggested in [ 3 ], in order to allow the afc method to operate in a stable condition and thus the adaptive filter to converge . in a first configuration , k ( n ) remained with the same value , k ( n )= k 1 , during all the simulation time t = 20 s in order to verify the methods &# 39 ; performance for a time - invariant forward path g ( z , n ). in a more practical configuration , k ( n )= k 1 until 5 s and then 20 log 10 k ( n ) was increased at the rate of 1 dbs up to 20 log 10 k 2 such that 20 log 10 k 2 = 20 log 10 k 1 + δk . finally , k ( n )= k 2 during 10 s totaling a simulation time t = 15 + δks . the maximum increase in the broadband gain δk that can be allowed while maintaining a stable operation ( which should not be confused with the msg ) differs depending on which method is being used . the performance of the present disclosure is demonstrated considering 10 speech signals as the source signal v ( n ) and a sampling rate f x = 16 khz . the feedback path f ( z , n ) was a measured room impulse response , from [ 6 ], with l f = 4000 samples . the forward path g ( z , n ) was defined as ( 24 ). for performance comparison , the pem - afrow method was used . the parameters of the pem - afrow , except those of the adaptive filter , had the values originally proposed in [ 1 ] adjusted to f s = 16 khz . for both methods , the adaptive filter &# 39 ; s parameters were chosen empirically in order to optimize the msg ( n ) in terms of minimum area of instability and , secondarily , of maximum mean value . the evaluation was done in real - world conditions where the source - signal - to - noise ratio ( snr ) was 30 db . in the first configuration , the broadband gain k ( n ) remained constant , i . e ., δk = 0 . fig1 shows the results obtained by the present disclosure ( using only the microphone signal y ( n ) or combining y ( n ), e ( n ) and x ( n )) and the pem - afrow method for δk = 0 . as can be observed , both configuration of the present disclosure outperformed the state - of - art pem - afrow method in the second configuration , k ( n ) was increased in order to determine the maximum stable broadband gain ( msbg ) of each method , that is the maximum value of k 2 with which an afc method achieves a msg ( n ) completely stable . such situation occurred firstly for the present disclosure using only the microphone signal y ( n ) with δk = 14 db . fig1 shows the results obtained by the present disclosure and the pem - afrow method for δk = 14 db . as can be observed , the present invention using only the microphone signal y ( n ) performed better than the pem - afrow until 10 s . again , the present disclosure combining y ( n ), e ( n ) and x ( n ) outperformed the pem - afrow . hereupon , k ( n ) continued to be increased to determine the msbg of the other methods . the second method to show a limited stability was the pem - afrow with δk = 16 db . fig1 shows the results obtained by the present disclosure combining y ( n ), e ( n ) and x ( n ) and the pem - afrow method for δk = 16 db . once again , as can be observed , the present disclosure combining y ( n ), e ( n ) and x ( n ) outperformed the pem - afrow . finally , k ( n ) was increased further to determine the msbg of the present disclosure combining y ( n ), e ( n ) and x ( n ). this situation occurred only with an impressive δk = 30 db , outscoring by 14 db the msbg of the pem - afrow method . fig1 shows the results obtained by the present disclosure combining y ( n ), e ( n ) and x ( n ) for δk = 30 . the present disclosure increased in 30 db the msg of the pa system and estimated the impulse response f ( m , n ) of the feedback with a mis of − 25 db . the term “ comprising ” whenever used in this document is intended to indicate the presence of stated features , integers , steps , components , but not to preclude the presence or addition of one or more other features , integers , steps , components or groups thereof . flow diagrams of particular embodiments of the presently disclosed methods are depicted in figures . the flow diagrams do not depict any particular means , rather the flow diagrams illustrate the functional information one of ordinary skill in the art requires to perform said methods required in accordance with the present disclosure . it will be appreciated by those of ordinary skill in the art that unless otherwise indicated herein , the particular sequence of steps described is illustrative only and can be varied without departing from the disclosure . thus , unless otherwise stated the steps described are so unordered meaning that , when possible , the steps can be performed in any convenient or desirable order . it is to be appreciated that certain embodiments of the disclosure as described herein may be incorporated as code ( e . g ., a software algorithm or program ) residing in firmware and / or on computer useable medium having control logic for enabling execution on a computer system having a computer processor , such as any of the servers described herein . such a computer system typically includes memory storage configured to provide output from execution of the code which configures a processor in accordance with the execution . the code can be arranged as firmware or software , and can be organized as a set of modules , including the various modules and algorithms described herein , such as discrete code modules , function calls , procedure calls or objects in an object - oriented programming environment . if implemented using modules , the code can comprise a single module or a plurality of modules that operate in cooperation with one another to configure the machine in which it is executed to perform the associated functions , as described herein . the disclosure is of course not in any way restricted to the embodiments described and a person with ordinary skill in the art will foresee many possibilities to modifications thereof . the following claims further set out particular embodiments of the disclosure . 1 . g . rombouts , t . van waterschoot , k . struyve , and m . moonen , “ acoustic feedback cancellation for long acoustic paths using a nonstationary source model ,” ieee transactions on signal processing , vol . 54 , no . 9 , pp . 3426 - 3434 , september 2006 . 2 . spriet , i . proudler , m . moonen , and j . wouters , “ adaptive feedback cancellation in hearing aids with linear prediction of the desired signal ,” ieee transactions on signal processing , vol . 53 , no . 10 , pp . 3749 - 3763 , october 2005 . 3 . t . van waterschoot and m . moonen , “ fifty years of acoustic feedback control : state of the art and future challenges ,” proceedings of the ieee , vol . 99 , no . 2 , pp . 288 - 327 , february 2011 . 4 . m . guo , s . h . jensen , j . jensen , and s . l . grant , “ on the use of a phase modulation method for decorrelation in acoustic feedback cancellation ,” in proceedings of the 20 th european signal processing conference , bucharest , romania , august 2012 , pp . 2000 - 2004 . 5 . j . jensen , m . guo , control of an adaptive feedback cancellation system based on probe signal injection , google patents , u . s . patent application ser . no . 13 / 622 , 880 ( marche 21 2013 ). url http :// www . google . com / patents / us20130070936 6 . m . jeub , m . schafer , and p . vary , “ a binaural room impulse response database for the evaluation of dereverberation algorithms ,” in proc . international conference on digital signal processing , santorini , greece , july 2009 . 7 . j . benesty , d . r . morgan , and m . m . sondhi , “ a better understanding an a improved solution to the specific problems of stereophonic acoustic echo cancellation ,” ieee trans . on speech and audio processing , vol . 6 , no . 2 , pp . 156 - 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