Patent Application: US-201314109556-A

Abstract:
an improved scheme for identifying and removing musical noise in an audio processing device can be achieved in an audio processing device that includes an analysis path with a perceptive model of the human auditory system and provides an audibility measure . the device may also identify an artifact introduced into the processed signal by the processing algorithm and provide an artifact identification measure , and control a gain applied to a signal of the forward path by the processing algorithm based on the perceptive model . an advantage is to dynamically optimize noise reduction with a view to audibility of artifacts and is applicable to hearing aids , headsets , ear phones , active ear protection systems , hands - free telephone systems , mobile telephones , teleconferencing systems , public address systems , karaoke systems , and classroom amplification systems .

Description:
fig1 shows a prior art noise reduction system , e . g . for forming part of an audio processing device , e . g . a hearing instrument . fig1 schematically illustrates components of a noise reduction system for reducing noise in an input audio signal x ( n ) and to provide an enhanced output signal z ( n ). index n is a time index implying the time variance of the signals . the noise reduction system is configured to compare characteristics of the noisy ( unprocessed ) input signal x ( n ) with signal characteristics of the noise - reduced signal z ( n ) to determine to which extent musical noise is present in the noise - reduced signal . it is found that the change of the signal kurtosis is a robust predictor of musical noise . based on this measure , it has been proposed in ep 2 144 233 a2 to adjust the parameters of the noise reduction algorithm ( e . g ., the maximum attenuation ) to reduce the amount of musical noise ( at the price of reduced noise reduction ). time variant signals x ( n ) and z ( n ) are e . g . signals of a forward path of an audio processing device . a noise reduction algorithm ( cf . signal processing unit noise reduction ( i . e . gain application ) in fig1 ) is applied to signal x resulting in enhanced signal z . the algorithm may be configured to work on an input signal x in the time domain and provide a resulting signal z in the time domain . preferably , however , the noise reduction algorithm works on signals in the frequency domain , e . g . in that the noisy input signal x ( n ) is provided as a band split signal ( e . g . as a map of time - frequency ( tf ) bins ( k , m ), each defining the signal at a particular frequency k and time m ). alternatively , the time to time - frequency conversion may be performed in the noise reduction unit . the resulting signal z ( n ) may be further processed in the time or frequency domain , e . g . by a gain unit for applying a frequency dependent gain to compensate for a user &# 39 ; s hearing loss . an analysis path is formed by a ) an snr estimation unit for dynamically estimating a signal to noise ratio of a tf - bin , b ) a computation of kurtosis ratio unit for determining a kurtosis ratio k ( x )/ k ( z )) by comparing respective kurtosis values for a given tf - bin ( k , m ) based on signals x ( k , m ) and z ( k , m ), and c ) a computation of noise reduction gain control unit for controlling a gain applied to a signal of the forward path by the noise reduction algorithm ( noise reduction ( i . e . gain application ) unit ) based on the snr value and the artifact identification measure for the tf - bin ( k , m ) in question . fig2 shows four embodiments of an audio processing device according to the present disclosure . fig2 simply illustrates basic components of an audio processing device , e . g . a listening device ld , comprising a forward path for receiving an input audio signal ( input ) and delivering an enhanced output audio signal ( output ). the forward path comprises ( as shown in to fig2 a in its simplest form ) an input unit ( iu ) ( e . g . an input transducer or an electrical connection point ) for providing an electric input signal representing the audio signal , a signal processing unit ( spu ) for applying a processing algorithm to a signal of the forward path and providing a processed output signal , and an output unit ( ou ) ( e . g . an output transducer or an electrical connection point ) for delivering the processed output signal , either for presentation to a user as a an audible stimulus ( output ) and / or to another unit or device for further processing . in the embodiment shown in fig2 b , the signal processing unit ( spu ) is shown to comprise a processing unit ( alg ) in the forward path and to implement an analysis path comprising a control unit ( cnt ) for controlling an algorithm of processing unit ( alg ). the control unit ( cnt ) receives input signals from the forward path before and after the processing unit ( alg ), respectively . in the embodiment shown in fig2 c , the part of the forward path implemented by processing unit ( spu ) is shown to further comprise analysis filter bank ( a - fb ) for providing input signals to the processing unit ( alg ) and to the control unit ( cnt ) in the time - frequency domain . alternatively , such time to time frequency conversion may be performed in the input unit ( iu ) or elsewhere ( e . g . prior to the input unit ( iu )) to provide that signals of the forward path as well as the analysis path are represented in the ( time -) frequency domain . in the embodiment of fig2 c the forward path — prior to the output unit ( ou )— further comprises a synthesis filter bank ( s - fb ) allowing a presentation of a signal to output unit ou in the time domain . the control unit ( cnt ) of the embodiment of fig2 c comprises a gain control unit ( gct ) for determining a gain ( e . g . an attenuation , or an amplification ) or another parameter and applying the gain ( or another parameter ) to an algorithm of the processing unit ( alg ). the gain control unit ( gct ) determines the relevant gain based on inputs from an artifact detector ( aid ) and a perceptual model ( pm ). a further embodiment of an audio processing device ( comprising the same functional elements as shown in fig2 c ) is illustrated in fig2 d , wherein the algorithm of the processing unit is a noise reduction algorithm ( indicated by denoting the processing unit nr ). the control unit ( cnt )— in addition to gain control unit ( gct ), artifact identification unit ( aid ), and model unit ( pm ) comprising a perceptual model — further comprises a voice activity detector ( vad ), and a unit ( snr ) for estimating a signal to noise ratio . the gain control unit ( gct ) is configured to base its determination of gain for a particular tf - unit ( k , m ) on inputs related to that unit from the artifact identification unit ( aid ), the model unit ( pm ), the voice activity detector ( vad ), and the snr unit ( snr ). fig3 shows in fig3 a an embodiment of an audio processing device ( comprising a noise reduction system ), and in fig3 b an embodiment of a noise reduction system according to the present disclosure . the audio processing device of fig3 a is embodied in a listening device ld having the same basic components as illustrated in fig2 , i . e . a ) an input unit ( here comprising a number of input transducers ( here microphones ) m 1 , . . . , mp , each for picking up a specific part of an input sound field , and each being connected to an analysis filter bank ( a - fb ) for providing a time - frequency representation inf 1 , . . . , infp of a respective microphone signal in 1 , . . . , inp ), b ) a signal processing unit ( spu ) ( here shown to comprise the analysis filter banks ( a - fb ) and a synthesis filter bank ( s - fb ) for providing a time - domain output signal out ), and c ) an output unit comprising and output transducer , here a loudspeaker , for presenting the output signal to one or more users as a sound . the audio processing device of fig3 a is shown to have a single loudspeaker , which is e . g . relevant for a hearing aid application , but may alternatively comprise a larger number of loudspeakers , e . g . two or three or more , depending on the application . a number of loudspeakers may e . g . be relevant in a public address system . in the following , the functional units of the signal processing unit ( spu ) are described . the analysis filter banks ( a - fb ) of signal processing unit ( spu ) receives time domain microphone signals in 1 , . . . , inp and provides time - frequency representations inf 1 , . . . , infp of the p microphone input signals . the p tf - representations of the input signals are fed to a directional ( or beamforming ) unit ( dir ) for providing a single resulting directional or omni - directional signal . the resulting output signal bfs of the dir unit is a weighted combination ( e . g . a weighted sum ) of the input signals inf 1 , . . . , infp . the processing algorithm , here a noise reduction algorithm ( nr ), is applied to the resulting ( directional or omni - directional ) signal bfs . the noise reduced signal nrs is fed to a further processing algorithm ( hag ) for applying a gain to signal nrs , e . g . a frequency and / or level dependent gain to compensate for a user &# 39 ; s hearing loss and / or to compensate for un - wanted sound sources in the sound field of the environment . the output ams of the further processing algorithm ( hag ) is fed to synthesis filter bank ( s - fb ) for conversion to time - domain signal out . the signal processing unit ( spu ) further comprises an analysis path comprising a control unit ( cnt ) for controlling the noise reduction algorithm ( nr ). the control unit ( cnt ) comprises the same functional elements shown in fig2 d and described in connection therewith . the control unit comprises a voice activity detector ( vad ) configured to indicate ( signal noi ) whether or not a human voice is present in the input audio signal in a given frequency region ( k ) at a given point in time ( m ). the control unit ( cnt ) is configured to only perform the analysis of kurtosis ( performed by artifact identification unit ( aid in fig2 d = kur , kum , kur in fig3 a ) comprising kurtosis calculation units ( kur ) and kurtosis comparison unit ( kum )) during time spans where no voice is present in a given tf - bin of the input audio signal , as indicated by a voice activity detector ( vad ). in other words , units kur , kum and mod may be held at standby during time segments identified ( e . g . by the vad ) as comprising speech . in case a voice is present in the signal bfs of the forward path subject to the noise reduction algorithm ( nr ), the influence of possible musical noise is considered negligible ( ignored ). thereby processing power is saved . in an embodiment , the voice activity detector ( vad ) analyses the full band signal ( full frequency range considered by the device ld ) and indicates whether or not a voice is present in the signal at a given point in time . preferably , however , the voice activity detector ( vad ) analysis the signal in a time - frequency representation and is configured to indicate the presence of a voice component ( e . g . speech ) in each time frequency bin ( k , m ), as schematically illustrated in fig7 . in the example of fig7 , showing the presence of speech ( and noise ) or noise only ( no speech )— in a magnitude |▪| vs . time plot — for a specific frequency band ( k = kp ) and a number of time units m 1 , m 1 + 1 , . . . , m 5 , the kurtosis analysis ( and thus the search for artifacts due to the applied noise reduction algorithm ) is only performed in time units ( m 1 + 1 )− m 2 , and ( m 3 + 1 )− m 4 , where only noise is present ( no speech ). the model unit ( mod ) comprising a perceptive model of the human auditory system receives output signal ams from the further processing algorithm ( hag , e . g . after an applied gain ) to decide whether an artifact identified in a given tf - bin ( k , m ) is audible or not ( signal and to gain control unit gnr ). this is illustrated in fig6 in the form of an exemplary noise signal spectrum ( solid line ) and corresponding masking thresholds ( dashed line ). the two kurtosis calculation units ( kur ) for determining kurtosis values based on signals bfs ( before noise reduction ) and nrs ( after noise reduction ), respectively , provide inputs k 1 and k 2 , respectively , to the kurtosis comparison unit ( kum ) determining a kurtosis ratio kr . units kum and kur are operatively connected with the gain control unit ( gnr ) ( indicated by double arrows on signals kr , k 1 and k 2 ) allowing the latter to control the calculation of respective kurtosis values and kurtosis rations , e . g . to only calculate kurtosis parameters for tf - units comprising a noise - only signal component ( as indicated by control signal not from the voice activity detector ( vad ) to the gain control unit ( gnr )). in case the kurtosis comparison unit ( kum ) indicates that an artifact is present in tf - bin ( k , m ) as communicated by control signal kr to the gain control unit ( nrg ), and the model unit ( mod ) indicates that such artifact is audible as communicated to the gain control unit ( gnr ) via control signal aud , an appropriately reduced attenuation ( increased gain ) g nr ( k , m ) is applied to signal bfs by the algorithm unit ( nr ). a schematic example of a relation between ( minimum ) noise reduction gain g nr , min ( k , m ) and the identification of audible and inaudible artifacts is shown in fig9 c . the noise reduction system as described in the listening device of fig3 a is illustrated in fig3 b and comprises a forward path comprising a noise reduction algorithm ( denoted nr and apply nrg in fig3 a and 3b , respectively ) for enhancing a noisy input signal x ( n ) of the forward path and providing an enhanced output signal z ( n ), and an analysis path comprising a control part cnt for controlling the noise reduction algorithm . kurtosis values k 1 ( k , m ) ( k 1 = k ( x )) and k 2 ( k , m ) ( k 2 = k ( z )) of signals of the forward path before and after , respectively , the application of the noise reduction algorithm are determined in units kurtosis ( x ) and kurtosis ( z ), respectively , for the tf - bins in question . according to the present disclosure , a kurtosis value k 1 ( k , m ) or k 2 ( k , m ) is determined for a probability density function p of the energy ( magnitude squared , |▪| 2 ) at a given frequency ( k ) and time ( m ) of the signal ( k 1 ( k , m ) and k 2 ( k , m )) in question . a kurtosis parameter k ( k , m ) at a particular frequency k and time instance m is based on a probability density function p (|▪| 2 ) of the energy for a number of previous time frames , e . g . corresponding to a sliding window ( e . g . the n f previous time frames relative to a given ( e . g . present ) time frame , cf . e . g . fig6 ). an artifact identification measure aidm ( k , m ), e . g . comprising a kurtosis ratio kr ( k , m )= k 2 ( k , m )/ k 1 ( k , m ), is determined in unit kurtosis ratio based on the determined kurtosis values k 1 ( k , m ) and k 2 ( k , m ). a predetermined criterion regarding the value of the artifact identification measure is defined , e . g . k 2 ( k , m )/ k 1 ( k , m )≧ aidm th . in an embodiment , aidm th ≧ 1 . 2 , e . g . ≧ 1 . 5 . if the predefined criterion is fulfilled by the artifact identification measure of a given tf - bin , an artifact at that frequency and time is identified . compared to the noise reduction system described in connection with fig1 , the system of fig3 b additionally comprises a model unit ( perceptual model unit in fig2 ) comprising a perceptual model ( e . g . a simple masking model ), which is used to identify to which extent a given time - frequency unit ( k , m ) of the output signal z ( n ) ( or a further processed version of z ( n )) is masked ( cf . e . g . fig6 ), and , consequently , to which extent the kurtosis - ratio k ( z ( k , m ))/ k ( x ( z , m )) ( cf . unit kurtosis ratio [ kr ( k , m )])— in case an artifact is identified in the tf - unit ( k , m ) in question — should influence the gain g nr ( k , m ) applied to the signal x ( n ) (= x ( k , m )) by the processing algorithm ( cf . unit apply nrg [ g nr ( k , m )]). the gain control unit compute nrg determines such resulting noise reduction gain ( attenuation ) g nr ( k , m ). the resulting noise reduction gain ( attenuation ) g nr ( k , m ) of a given tf - unit ( k , m ) is determined on the basis of the estimated signal to noise ratio snr ( k , m ) of the signal x ( n ), a voice activity indication noi ( k , m ), the determined kurtosis ratio kr ( k , m ), and an audibility parameter aud ( k , m ). this improved musical noise predictor can e . g . be used in an online noise - reduction system in a hearing instrument or other audio processing device , where parameters of the noise reduction system is continuously updated based on a musical noise predictor , such that the amount of noise reduction is always at a level where the noise reduction is maximum subject to the constraint that no musical noise is introduced ( or that musical noise is minimized ). a noise reduction system applying a band specific scheme is e . g . described in wo 2005 / 086536 a1 . fig4 shows an embodiment of a binaural audio processing system according to the present disclosure . the binaural audio processing system is here embodied in a binaural hearing aid system comprising first and second hearing instruments ( hi - 1 , hi - 2 ) adapted for being located at or in left and right ears of a user , respectively . the hearing instruments hi - 1 , hi - 2 of the binaural hearing aid system of fig4 are further adapted for exchanging information between them via a wireless communication link , e . g . a specific inter - aural ( ia ) wireless link ( ia - wls ). the two hearing instruments hi - 1 , hi - 2 are adapted to allow the exchange of status signals , e . g . including the transmission of characteristics of the input signal received by a device at a particular ear to the device at the other ear . to establish the inter - aural link , each hearing instrument comprises antenna and transceiver circuitry ( here indicated by block ia - rx / tx ). each hearing instrument hi - 1 and hi - 2 is an embodiment of an audio processing devise as described in the present application ( e . g . shown in and discussed in connection with fig2 or 3 ). in the binaural hearing aid system of fig4 , a signal iax generated by the processing unit ( spu ) of one of the hearing instruments ( e . g . hi - 1 ) is transmitted to the other hearing instrument ( e . g . hi - 2 ) and / or vice versa . signals iax may ( at a given point in time ) comprise audio signals only , control signals only , or a combination of audio and control signals . the control signals from the local and the opposite device are e . g . used together to influence a decision or a parameter setting in the local device . the control signals may e . g . comprise information that enhances system quality to a user , e . g . improve signal processing , e . g . the execution of a processing algorithm . the control signals may e . g . comprise directional information or information relating to a classification of the current acoustic environment of the user wearing the hearing instruments , audibility of artifacts , etc . in an embodiment , the audio processing system further comprises an audio gateway device for receiving a number of audio signals and for transmitting at least one of the received audio signals to the audio processing devices ( e . g . hearing instruments ). in an embodiment , the audio processing system is adapted to provide that a telephone input signal can be received in the audio processing device ( s ) via the audio gateway . the hearing instruments hi - 1 , hi - 2 — in addition to a microphone ( mic ) for picking up a sound signal in the environment — each comprise antenna ( ant ) and transceiver circuitry ( block rx / tx ) to implement a wireless interface to an audio gateway or other audio delivery device , e . g . a telephone . the input unit ( iu ) is configured to select one of the input signals inw ( from the wireless interface ) or inm ( from the microphone ) or to provide a mixture of the two signals , and present the resulting signal to the signal processing unit ( spu ) as a band - split ( time - frequency ) signal ifb 1 - ifb ni . in an embodiment , the system is configured to control the gain of a noise reduction algorithm independently in each of the first and second hearing instruments . it may be a problem , however , if artifacts are ‘ detected ’ and thus attenuation reduced at one ear , but not at the other ear . thus ( at that frequency and time ) gain will increase ( because of a less aggressive noise reduction , e . g . by reducing attenuation from 10 db to 4 db ) at the one ear relative to the other ear , which — in some instances — may erroneously be interpreted as spatial cues and thus cause confusion for the user . in a preferred embodiment , information about the control of the noise reduction is exchanged between the first and second hearing instruments , e . g . via the inter - aural wireless link ( ia - wls ), thus allowing a harmonized control of the noise reduction algorithms of the respective hearing instruments . specifically , information about the control of gains of time - frequency regions for which gains should be increased ( attenuation reduced ) to reduce the risk of producing audible artifacts is exchanged between the first and second hearing instruments . preferably , the same attenuation strategy is applied in first and second hearing instruments ( at least regarding attenuation in time - frequency regions at risk of producing audible artifacts ). fig5 shows schematic illustrations of the steps of determining a kurtosis parameter . signals of the forward path before and after the processing algorithm ( e . g . signals x and z , respectively , in fig3 b ) are provided in a time - frequency representation , e . g . x ( k , m ), k being a frequency index and m being a time index . such time - frequency representation is schematically illustrated in the top graph of fig5 . a specific time - frequency ( tf ) bin is defined by a specific combination of indices ( k , m ). the two middle graphs schematically illustrate a possible time variation ( for a number n f of time frames ) of values of magnitude squared of a noise signal before and after the application of processing algorithm ( e . g . signals x and z , respectively , of fig3 b ) at a particular frequency k p . in a normal mode of operation of a noise reduction algorithm , a value of the magnitude (|▪|) or ( as indicated here ) magnitude squared (|▪| 2 ) of the input signal x in a particular time - frequency bin ( k , m ) below a predefined threshold value n th ( during a noise - only time period ) may result in a predetermined attenuation ( e . g . 6 db ) of the signal of that tf - bin . correspondingly , a value larger than the threshold value n th may result in no attenuation being applied to the contents of that tf - bin . this is illustrated in the two middle graphs , where three ( high magnitude tf - bins at frequency k p ) are not attenuated resulting in ‘ musical noise ’. according to the present disclosure , a kurtosis parameter k ( k p , m ) is determined for a probability density function of the energy ( magnitude squared , |▪| 2 ) at a given frequency ( k p ) and time ( m ) of a signal of the forward path of the audio processing device before ( k 1 ( k p , m )) and after ( k 2 ( k p , m )) the processing algorithm in question , e . g . a noise reduction algorithm . the bottom graphs of fig6 illustrate schematic probability density functions p (|▪| 2 ) for signals x and z extracted from the middle graphs of the time dependent signals . a kurtosis parameter k ( k p , m ) at a particular frequency k p and time instance m is based on a number of previous time frames , e . g . corresponding to a sliding window ( e . g . the n f previous time frames relative to a given ( e . g . present ) time frame # m ) as illustrated by the solid enclosure in the top graph of fig6 denoted analysis window . a kurtosis value ( indicating a degree of peakedness ) based on the respective bottom graphs will show an increase for the noise reduced signal ( z , right graph ) compared to the unprocessed signal ( x , left graph ). an artifact identification measure will thus be relatively large , and can be used as an indicator of artifacts ( and thus an indicator of a risk of musical noise ). a masking model or an audibility model applied to an output signal ( e . g . the noise reduced signal , or a further processed signal ) is , however , preferably used to qualify the artifacts in audible and in - audible artifacts . fig6 shows a schematic perceptual model ( here a masking model ) for a noise signal at a given point in time , and an artefact identification measure aidm implying a number of exemplary occurrences of artifacts ( at the given point in time ). fig6 illustrates masking thresholds versus frequency k ( dashed line ) according to a masking model for a specific frequency dependence of the magnitude |▪| of a noise signal picked up by an audio processing device according the present disclosure ( solid line ). frequency ranges where the curve representing the masking thresholds is below the assumed noise level indicates frequencies where an artifact would be audible ( here k & lt ; k x ), whereas frequency ranges where the curve representing the masking model is above the assumed noise level indicates frequencies where an artifact would be in audible ( here k & gt ; k x ). fig7 shows a schematic example of magnitude |▪| of a time variant input audio signal in a specific frequency band ( kp ) comprising time segments of noise - only and time segments of speech in noise the resulting analysis by a voice activity detector . fig8 shows a schematic example of the gain gnr applied by a noise reduction algorithm to a given tf - unit as a function of an estimated signal to noise ratio snr of the tf - unit . fig8 illustrates a resulting gain g nr ( snr ( k , m )) applied to a particular tf - bin ( k , m ) of an audio signal of the forward path of an audio processing device by a noise reduction algorithm . the audio signal typically comprises a mixture of a target signal ( e . g . a speech signal ) and other sound elements , termed noise . the noise reduction algorithm has the purpose of attenuating noise parts of the audio signal ( typically to thereby let the target signal ‘ stand out more conspicuously ’, and thereby increasing intelligibility ). typically an estimate of the signal to noise ratio ( snr ) of the audio signal ( e . g . in each frequency band of the signal ) is determined at successive time instances ( e . g . in every time frame , e . g . at time intervals of the order of ms , e . g . 3 . 2 ms ). this estimate is e . g . used to determine a gain ( attenuation ) applied to the audio signal ( preferably in a specific frequency bands or bands ) by the noise reduction algorithm . the gain applied by the noise reduction algorithm is typically allowed to vary between a minimum value g nr , min ( maximum attenuation , e . g . − 10 db ) and a maximum value g nr , max ( minimum attenuation , e . g . no gain , 0 db ). in an embodiment , the minimum gain g nr , min is applied to the signal ( or frequency bands ) at relatively low signal to noise ratios ( e . g . below snr 1 in fig8 , indicated as ‘ noisy signal ’), and the maximum gain g nr , max is applied to the signal ( or frequency bands ) at relatively high signal to noise ratios ( e . g . above snr 2 in fig8 , indicated as ‘ good signal ’). in an intermediate range between relatively low and relatively high signal to noise ratios , the gain g nr applied by the noise reduction algorithm is increased from g nr , min to g nr , max , e . g . in steps ( dotted line ), or linearly ( solid line ), or according to any other continuous function , with increasing snr , cf . e . g . fig8 . preferably , a perceptive noise reduction scheme as proposed in the present application is implemented . when an artifact identification measure aidm ( k , m ) ( e . g . a kurtosis ratio ) for the particular tf - unit ( k , m ) is smaller than a threshold value aidm th , no risk of introducing artifacts is identified , and a normal operation of the noise reduction algorithm is applied ( as described above for fig8 , here shown to be the application of a minimum gain g nr , min , i . e . a predefined maximum attenuation ), e . g . attenuating the magnitude of the tf - bin in question with a predefined amount , e . g . 10 db , if the contents of the tf - bin is characterized as noise ( e . g . by a voice activity detector ( cf . e . g . fig9 a ) and / or by an snr - analysis unit and / or by a frequency analysis unit ). if , on the other hand , the measure aidm ( k , m ) is larger than the threshold value aidm th , a risk of introducing artifacts is present , and a modified operation of the noise reduction algorithm is applied ( based on a perceptual model , cf . e . g . fig6 ). the algorithm alg is assumed to have a specific form for determining a gain for a given tf bin , when artifacts are not considered ( normal mode ). according to the present disclosure , where artifacts are identified using an artifact identification measure aidm that is calculated on a tf bin basis , aidm ( k , m ), a modification δg alg of the ‘ normal ’ gain is proposed when artifacts can be identified . in an embodiment , δg alg is identical for all values of k and m . in an embodiment , δg alg is dependent on frequency ( index k ). in an embodiment , δg alg is dependent on the artifact identification measure aidm ( k , m ). in an embodiment , a speech or voice activity detector is configured to determine whether the audio signal ( either the full signal and / or specific time - frequency elements of the signal ) at a given time contain speech elements . for a noise reduction algorithm , a modification δg nr of the ‘ normal ’ gain ( g nr in fig8 ) is proposed , when artifacts can be identified according to the following scheme : g nr ( k , m )= g nr ( k , m − 1 )+ δg nr [ db ], if artifacts are detected during noise only ( effectively , increase g nr , min ); g nr ( k , m )= g nr ( k , m − 1 )− δg nr [ db ], if no artifacts are detected during noise only ( effectively , decrease g nr , min ); and g nr ( k , m )= g nr ( k , m − 1 ) [ db ], if speech is detected ( effectively , keep g nr at the value ‘ arrived at ’ during a noise only period ); under the constraint that g nr0 , min ( k , m )≦ g nr ( k , m )≦ g nr0 , max ( k , m ), where g nr0 , min ( k , m ) and g nr0 , max ( k , m ) are predetermined minimum and maximum values , respectively , of the gain ( g nr ) applied by the noise reduction algorithm ( e . g . − 10 db and 0 db , respectively ). preferably the rate of change of the modification is limited , the rate of change being defined by δg nr and the time interval t f between successive time frames of the signal . in an embodiment , a time frame has a duration of between 0 . 5 ms and 30 ms , depending on the application in question ( and determine by the length in time of one sample ( determined by the sampling rate f s ) and the number of samples per time frame , e . g . 2 n , n being a positive integer , e . g . larger than or equal to 6 ). a relatively short time frame enables a system with a relatively low latency ( e . g . necessary in applications where a transmitted sound signal is intended to be in synchrony with an image , e . g . a live image , such as e . g . in hearing aid system ). relatively longer time frames results in higher system latency , but may be acceptable in other applications , however , e . g . in cell phone systems . in an embodiment , δg nr is adaptively determined in dependence of the size of the artifact identification measure ( aidm ), e . g . so that δg nr is larger the larger aidm ( k , m ) ( e . g . proportional to aidm ). fig9 illustrates in fig9 c a resulting minimum gain g nr , min ( k , m ) applied to a particular frequency band ( k p , m ) of a signal of the forward path of an audio processing device by a noise reduction algorithm implementing a perceptive noise reduction scheme as proposed in the present application , fig9 a schematically showing time segments of the processed audio signal of the forward path ( after noise reduction ) for the frequency band k p in question , and fig9 b showing identified artifacts at particular points in time of the noise - only time segments at the frequency band k p in question , and indicate an estimate of their audibility (‘ a ’) or inaudibility (‘ ia ’). typically , the ‘ noise only ’ periods of time are ( by definition ) periods of time with a low signal to noise ratio ( see indication ‘ noisy signal ’ in fig8 ). hence , in practice ( in an embodiment ), the modification of the noise reduction algorithm provided by the present disclosure is a modification of the minimum gain g nr , min ( cf . e . g . fig8 ) applied to frequency components ( tf bins ) of a signal ( in case an artifact is identified and considered audible ) to make the noise reduction less aggressive ( i . e . increase g nr , min ,=& gt ; less attenuation ), in practice to increase the minimum gain level ( while keeping the maximum gain g nr , max constant ) thereby minimizing the dynamic range of attenuation available to the noise reduction algorithm , as indicated in fig9 : the graph of fig9 c illustrates a modification of g nr , min ( k p , m ) ( when audible artifacts are identified ) within a dynamic range between predetermined minimum and maximum values g nr0 , min ( k , m ) and g nr0 , max ( k , m ), respectively , for a specific time variant input signal of the forward path of a listening device ( at a particular frequency k p ) according to the present disclosure , as illustrated in the graph of fig9 a . the time variant input signal comprises the same alternating time segments of noise only and speech ( in noise ), respectively , at a particular frequency k p , as illustrated and discussed in connection with fig7 . the graph in fig9 b indicates the occurrence in time of ( identified ) artifacts during the noise - only time periods . each artifact is symbolized by a bold vertical line occurring at a particular point in time and denoted ‘ a ’ or ‘ ia ’ in a square enclosure , depending on its estimated audibility and inaudibility , respectively . the artifacts occurring in the first noise - only time segment ( between time indices m 1 and m 2 ) are judged by the perceptual model to be audible (‘ a ’) as also indicated by the small graphical insert ( above the artifacts , in the left part fig9 b ). the insert schematically illustrates the noise signal spectrum , masking thresholds ( as determined by a perceptual model ) and the occurrence of ( identified ) artifacts at the relevant time . the noise spectrum ( solid line ) and masking thresholds ( dashed line ) in the above insert in principle corresponds to one particular time instance , but all three artifacts are assumed to occur at points in time where the masking threshold are so that the artifact in question is audible . conversely , the artifacts occurring in the second noise - only time segment ( between time indices m 3 and m 4 ) are judged by the perceptual model to be inaudible (‘ ia ’) as also indicated by the small graphical insert ( above the artifacts , in the right part of fig9 b ). preferably , the steps δg nr and the frame length in time ( t f determining a time unit from time index m to time index m + 1 ) are configured to provide that an adaptation rate of the noise reduction gain g nr ( k , m )— when artifacts are detected — is a compromise between the risk of creating artifacts in the processed signal of the forward path and the wish to ensure an aggressive noise reduction . in an embodiment , δg nr and t f are selected to provide that the adaptation rate of g nr ( k , m ) is in the range from 0 . 5 db / s to 5 db / s . an exemplary frame length t f of 5 ms and an adaptation rate of 2 . 5 db / s leads for example to a step size per time unit δg nr of 0 . 0125 db ( δg nr / t f = ar ). the invention is defined by the features of the independent claim ( s ). preferred embodiments are defined in the dependent claims . any reference numerals in the claims are intended to be non - limiting for their scope . some preferred embodiments have been shown in the foregoing , but it should be stressed that the invention is not limited to these , but may be embodied in other ways within the subject - matter defined in the following claims and equivalents thereof . 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