Patent Application: US-201615040042-A

Abstract:
the application relates to a binaural hearing system comprising left and right hearing devices , e . g . hearing aids , each comprising a ) a multitude of input units , each providing a time - variant electric input signal x i representing sound received at an i th input unit , t representing time , the electric input signal x i comprising a target signal component s i and a noise signal component v i , the target signal component originating from a target signal source ; b ) a configurable signal processing unit for processing the electric input signals and providing a processed signal y ; c ) an output unit for creating output stimuli to the user , d ) transceiver circuitry allowing information to be exchanged between the hearing devices , and e ) a binaural speech intelligibility prediction unit for providing a binaural si - measure of the predicted speech intelligibility of the user when exposed to said output stimuli , based on processed signals y l , y r from the signal processing units of the respective left and right hearing devices . this allows the hearing devices to control the processing of the respective electric input signals based on said binaural si - measure .

Description:
the detailed description set forth below in connection with the appended drawings is intended as a description of various configurations . the detailed description includes specific details for the purpose of providing a thorough understanding of various concepts . however , it will be apparent to those skilled in the art that these concepts may be practised without these specific details . several aspects of the apparatus and methods are described by various blocks , functional units , modules , components , circuits , steps , processes , algorithms , etc . ( collectively referred to as “ elements ”). depending upon particular application , design constraints or other reasons , these elements may be implemented using electronic hardware , computer program , or any combination thereof . the electronic hardware may include microprocessors , microcontrollers , digital signal processors ( dsps ), field programmable gate arrays ( fpgas ), programmable logic devices ( plds ), gated logic , discrete hardware circuits , and other suitable hardware configured to perform the various functionality described throughout this disclosure . computer program shall be construed broadly to mean instructions , instruction sets , code , code segments , program code , programs , subprograms , software modules , applications , software applications , software packages , routines , subroutines , objects , executables , threads of execution , procedures , functions , etc ., whether referred to as software , firmware , middleware , microcode , hardware description language , or otherwise . fig1 shows a first embodiment of a binaural hearing system according to the present disclosure . the signal processing of each of the left and right hearing devices is guided by an estimate of the speech intelligibility experienced by the hearing aid user ( cf . control signals pcnt l , pcnt r from the binaural speech intelligibility predictor ( bin - si ) to the respective signal processing units ( spu ) of the left and right hearing devices ). in this example , the si estimation / prediction takes place in the left - ear hearing device ( left ear :) using the output signals of both has ( the output signal of the right - ear hearing device ( right ear :) is wirelessly transmitted to the left - ear hearing device ( left ear :)). dashed lines indicate wired or wireless signal transmission via a communication link ( link ). the general idea of the present disclosure is illustrated in fig1 . in this figure , each hearing device is schematically depicted comprising of two microphones , a signal processing block ( spu and potentially a binaural si prediction module bin - si ), and a loudspeaker . the microphones pick up a — potentially noisy ( time varying ) signal x ( t )— which generally consists of a target signal component s ( t ) and an undesired signal component v ( t ) ( in the figure , the subscripts 1 , 2 indicate a first and second ( e . g . front and rear ) microphone , respectively , while the subscripts l , r indicate whether it is the left or right ear hearing device ( hd l , hd r , respectively )). the hearing devices are wirelessly connected . in the depicted situation , it is assumed that the binaural si - processing ( cf . unit bin - si ) takes place in the left hearing device . this requires access to the output signal y l ( t ) of the loudspeaker of the left - ear hearing device ( hd l ), which is easily available , and to the output signal y r ( t ) of the loudspeaker of the right - ear hearing device ( hd r ), which we assume is ( e . g . wirelessly ) transmitted ( dashed line ) via communications link ( link ) between the two hearing devices . based on the predicted si , the signal processing of each hearing device may be ( individually ) adapted ( cf . signals pcnt l , pcnt r ). since the si predicted is performed in the left - ear hearing device ( hd r ), adaptation of the processing in the right - ear hearing device ( hd r ) requires a wireless control processing signal ( pcnt r ) to be transmitted from left to right - ear hearing device to the right ear hearing device ( dashed line ). in fig1 , each of the left and right hearing devices comprise two microphones . in other embodiments , each ( or one ) of the hearing devices may comprises three or more microphones . likewise , in fig1 , the binaural speech intelligibility predictor ( bin - si ) is located in the left hearing device ( hd l ). alternatively , the binaural speech intelligibility predictor ( bin - si ) may be located in the right hearing device ( hd r ), or alternatively in both , preferably performing the same function in each hearing device . the latter embodiment consumes more power and requires a two - way exchange of output audio signals ( y l , y r ), whereas the exchange of processing control signals ( pcnt r in fig1 ) can be omitted . in still another embodiment , the binaural speech intelligibility predictor ( bin - si ) is located in a separate auxiliary device , e . g . a remote control ( e . g . embodied in a smartphone ) requiring that an audio link can be established between the hearing devices and the auxiliary device for receiving output signals ( y l , y r ) from , and transmitting processing control signals ( pcnt l , pcnt r ) to , the respective hearing devices ( hd l , hd l ). the processing performed in the signal processing units ( spu ) and controlled or influenced by the control signals ( pcnt l , pcnt r ) of the respective left and right hearing devices ( hd l , hd l ) from the binaural speech intelligibility predictor ( bin - si ) may in principle include any processing algorithm influencing speech intelligibility , e . g . spatial filtering ( beamforming ) and noise reduction , compression , feedback cancellation , etc . ( cf . e . g . fig6 ). the adaptation of the signal processing of a hearing device based on the estimated binaural si include ( but are not limited to ): 1 . adapting the aggressiveness of beamformers of the hearing system . specifically , for binaural beamformers , it is well - known that the beamformer configuration involves a trade - off between noise reduction and spatial correctness of the noise cues . in one extreme setting , the noise is maximally reduced , but all noise signals sound as if originating from the direction of the target signal source . the trade - off that leads to maximum si is generally time - varying and generally unknown . with the proposed approach , however , it is possible to adapt the beamformer stage of a given hearing device to produce maximum si at all times . 2 . adapting the aggressiveness of a ( single - channel ( sc )) noise reduction system . often a beamformer stage is followed by an sc noise reduction stage ( cf . e . g . fig6 ). the aggressiveness of the sc noise reduction filter is adaptable ( e . g . by changing the maximum attenuation allowed by the sc noise reduction filter ). the proposed approach allows to choose the si optimal tradeoff , i . e ., a system that suppresses an appropriate amount of noise without introducing si - disturbing artefacts in the target speech signal . 3 . for systems with adaptable analysis / synthesis filterbanks , the analysis / synthesis filter bank leading to maximum si may be chosen . this implies to change the time - frequency tiling , i . e ., the bandwidths and / or sampling rate used in individual subbands to deliver maximum si in accordance with the target signal and acoustic situation ( e . g ., noise type , level , spatial distribution , etc .). 4 . if the binaural si prediction unit estimates the maximum si of the binaural hearing system to be so low that it is of no use for the user , then an indication may be given to the user ( e . g . via a sound signal ), that the ha system is unable to operate in the given acoustical conditions . it may then adapt its processing , e . g . to at least not introduce sound quality degradations , or to go to a “ power - saving ” mode , where the signal processing is limited to save power . the proposed method relies on the ability to — given a binaural signal ( y l ( t ) and y r ( t )) in the embodiment in fig1 — predict the si experienced by the user of the hearing system . to this end , a binaural si prediction algorithm is needed . while such algorithms are known from literature , e . g . [ 1 - 6 ], these methods cannot be used in the situation at hand , since they generally require access to the target signal component and the undesired signal component , impinging on the left and right ear drum , each in isolation . in the current situation , these signal components are unavailable in separation : only the noisy signals ( i . e ., the combined target and undesired signal components ) picked up by the microphones of the hearing devices along with the processed output signals are available . however , as described in the following , a scheme is proposed , which can provide a binaural si estimate , even though the target speech signal and the disturbing noise component are unavailable in separation . specifically , the method proposed in [ 1 , 2 ]— which cannot be used in the current situation — use the target signal and the noise signal components ( available in isolation ) to establish an snr - optimal binaural beamformer . in other words , they find the coefficients of the linear combination of microphone signals ( individual frequency subbands ), which lead to maximum snr in the beamformer output . in [ 1 , 2 ] it is realized , however , that the optimal beamformer weights lead to si predictions , which are superior to human si performance . to account for this fact , jitter ( i . e . noise ) is added to the optimal beamformer weights , to reduce the beamformer performance to be in accordance with human performance . finally , in [ 1 , 2 ] the target and noise signal components are passed through this jittered beamformer ; then the resulting beamformed target and noise signal components are passed through a monaural si predictor ( esii , [ 7 , 8 ]), to produce an si estimate . in the situation at hand [ 1 , 2 ] cannot be used because target and noise signals cannot be observed in separation . to propose an approach that can be used in this situation , let us assume that noise v ( n ) is additive and uncorrelated with the target signal s ( n ). this assumption is traditionally made in the area of speech enhancement because it is a reasonable assumption in many practical situations : it is obviously valid in situations where the noise generation process is unrelated to the target speech generation process , e . g . a conversation in a car cabin environment while driving ; furthermore , it is an operational assumption even in situations where the undesired signal component is not obviously uncorrelated from a target speech signal , e . g ., in reverberant environments , cf . e . g . [ 12 ]. furthermore , let us assume that the signal processing of the hearing devices to be linear across sufficiently short time durations . the assumption is approximately valid for many of the standard signal processing algorithms of a hearing device , e . g ., beamforming , which are generally time - varying , linear operations . other algorithms , e . g ., amplification and dynamic range compression [ 13 ], are inherently non - linear operations : however , since these algorithms tend to change relatively slowly across time , they may be assumed roughly linear ( constant ), across time - durations of several 10 s of ms , and often across several 100 s of ms . with these assumptions in mind , we propose to estimate the si based on the users ‘ eardrum signals ’ ( y l ( t ) and y r ( t )) in the example in fig1 ), as outlined in fig2 . fig2 shows a flow diagram for a method of providing a binaural speech intelligibility predictor based on the output signals yl ( t ) and yr ( t ) of left and right hearing devices , respectively of a binaural hearing system . it is assumed that these operations are performed in the frequency domain . specifically , we assume that the operations are applied ( in parallel ) to frequency sub - bands with bandwidths which may resemble the critical band filters of the human auditory system . first , a potential hearing loss is modelled ( block model hearing loss in fig2 ). this can be done by simply adding uncorrelated noise , spectrally shaped according to the audiogram of the user , as proposed in [ 1 , 2 ]. while it is difficult to estimate reliably the target and noise components based on signals y l ( t ) and y r ( t ) or signals x 1 , l ( t ) and x 1 , r ( t )), it is possible to estimate the inter - aural target and noise covariance matrices ( for each frequency sub - band of the signals involved ), cf . block estimate interaural covariance matrices in fig2 , and also fig3 . fig3 shows an example of estimation of covariance matrices of target and an undesired ( noise ) component ( none of which can be observed directly ), based on covariance matrix of signal y ( k , m ) which can be observed . these covariance matrices are accurately defined in the following . let us , to be closer to a practical implementation , make the description in the time - frequency plane . so , let y l ( k , m ) denote the output signal y l ( n ) of the left - ear hearing aid at frequency index k and time index m . similarly , let y r ( k , m ) denote the output signal y r ( n ) of the right - ear hearing aid at frequency index k and time index m . using the assumption that the signal processing of the hearing devices is linear , and that the noise is additive , the output signals of the left - ear and right - ear hearing aids , y l ( k , m ) and y r ( k , m ), respectively , can be written as and where the function ƒ (•) represents the hearing aid signal processing ( which is assumed to be linear in the equations above ). furthermore , let denote the ( 2 × 1 in this case ) vector with the output signal of the left - and right - ear hearing devices ( for a particular time frequency index ), and similarly define vectors v ( k , m )=[ v l ( k , m ) v r ( k , m )] t , the cross - covariance matrix c y ( k , m ) of the output signals ( that is , the inter - aural covariance matrix ) is then defined as where e [•] denotes the statistical expectation operator , and the superscript h denotes hermitian ( complex - conjugate ) transposition . similar definitions hold for the inter - aural target signal covariance matrix c s ( k , m ) and the undesired signal covariance matrix c v ( k , m ). estimation of these target and noise covariance matrices c s ( k , m ) and c v ( k , m ) is possible using ( the assumption ) that target and noise processed are uncorrelated , and possibly using prior knowledge that the target source is located frontally to the hearing aid user . as an example ( which may be applied in the present situation with a few modifications ) fig3 outlines the maximum likelihood approach described in [ 9 , 10 ], for estimating the matrices c s ( k , m ) and c v ( k , m ) based on the assumption that the direction to the target signal source is known , and knowledge about the structure of c v ( k , m ) ( these assumptions are practically relevant in a typical hearing aid situation ). in fig2 , the vector d ( k , m ) ( termed the look vector ) denotes the transfer function from the target source to each of the sensor in the system , or alternatively the relative transfer functions ( defined as the transfer function from any microphone to a reference microphone , see [ 9 , 10 ] for details ). based on these estimated matrices , an estimate of snr - optimal beamformers can be produced ( cf . block estimate snr optimal beamformer in fig2 ), one pair of — generally complex - valued — beamformer weights w ( k , m )=[ w l ( k , m ) w r ( k , m )] for each frequency band . for example , for the situation at hand , the snr - optimal beamformer weights are given by analogously to [ 1 , 2 ], these optimal beamformer weights are jittered ( cf . block compute jittered beamformer weights in fig2 ). this may be written as { tilde over ( w )} l ( k , m )= w l ( k , m ) g ( w ( k , m )), { tilde over ( w )} r ( k , m )= w l ( k , m ) g ( w ( k , m )), where in [ 1 , 2 ], the function g ( w ( k , m )) introduces random and statistically independent gain errors and delay errors to the optimal beamformer weights ; in [ 1 , 2 ] the gain errors and delay errors are gaussian distributed on the logarithmic and linear scale , respectively , and the standard deviation of these errors is a function of the optimal beamformer weights w ( k , m ) themselves , hence the notation g ( w ( k , m )). then , the binaural signal ( y l ( t ) and y r ( t )) is passed through the jittered beamformer { tilde over ( w )}( k , m )=[{ tilde over ( w )} l ( k , m ){ tilde over ( w )} r ( k , m )] ( cf . block apply jittered beamformer in fig2 ), and using the estimated inter - aural target and noise covariance matrices , an apparent signal - to - noise ratio is computed as a function of time and frequency . finally , these snr values are used in a standard monaural si prediction , e . g . the extended speech intelligibility index ( esii ) [ 7 , 8 ] or the short - term objective intelligibility ( stoi ) measure [ 11 ] to produce a final estimate of the intelligibility experienced by the hearing aid user ( cf . block evaluate monaural si predictor and signal si estimate in fig2 ). in practice , the absolute si ( i . e ., the percentage of words understood ) is difficult to estimate , since it is dependent on e . g ., the speaking rate , the speech signal redundancy , etc .,— quantities which are hardly available in practice ( and difficult to estimate in a hearing aid system ). however , the relative si , i . e ., whether the si is improved or degraded can be estimated without detailed knowledge of the target speech signal . fig4 shows an embodiment of a binaural speech intelligibility prediction unit according to the present disclosure . the embodiment of fig4 basically illustrates the flow diagram of fig2 as functional blocks with a few additional features described in the following . the hearing loss model unit ( hlm ) corresponds to the step of applying a model of a user &# 39 ; s hearing loss to the output signals y l , y r of the left and right hearing devices hd l , hd r ( model hearing loss in fig2 ). the hearing loss model unit ( hlm ) provides resulting modified output signals y ′ l , y ′ r e . g . by adding ( to the original output signals y l , y r ) uncorrelated noise , spectrally shaped according to an audiogram of the respective ears of the user . the interaural covariance estimation unit ( iacov ) corresponds to the step of estimating the inter - aural target signal covariance matrix c s ( k , m ) and the undesired signal covariance matrix c v ( k , m ). ( cf . estimate inter - aural covariance matrices in fig2 ). the interaural covariance estimation unit ( iacov ) comprises respective analysis filter banks ( units tf in fig4 ) to provide the time domain signals y ′ l , y ′ r in a time frequency domain representation in a number of frequency bands ( k ) and at a number of time instances ( m ), e . g . of the order of a time - frame . the interaural covariance estimation unit ( iacov ) may e . g . comprise a maximum likelihood estimation unit of the target and noise covariance matrices as illustrated in fig3 . the input look vector d ( k , m ) in fig3 is shown as an input d ( k , m ) to the iacov unit of fig4 ( dashed arrow ). the beamformer weight estimation unit ( bfwgt ) corresponds to the step of estimating snr - optimal beamformers in the form beamformer weights w ( k , m )=[ w l ( k , m ) w r ( k , m )] for each frequency band . ( cf . block estimate snr optimal beamformer in fig2 ). the jittered beamformer weight estimation unit ( j - bfwgt ) corresponds to the step of applying jitter to the snr optimal beamformer weights w ( k , m )=[ w l ( k , m ) w r ( k , m )] ( cf . block compute jittered beamformer weights in fig2 ) providing jittered beamformer weights { tilde over ( w )}( k , m )=[{ tilde over ( w )} l ( k , m ){ tilde over ( w )} r ( k , m )] ( denoted wj l ( k , m ) and wj r ( k , m ), respectively , in fig4 ). the beamformer filter (( apply ) bf ) corresponds to the step of applying jittered beamformer weights { tilde over ( w )}( k , m )=[{ tilde over ( w )} l ( k , m ){ tilde over ( w )} r ( k , m )] to the output signals y l , y r of the left and right hearing devices hd l , hd r ( cf . block apply jittered beamformer in fig2 ). in the embodiment of fig4 , it is assumed that a time to time - frequency transformation of the output signals y l , y r is performed in the beamformer filter (( apply ) bf ), to provide the output signals y l , y r in a time frequency domain representation ( k , m ). alternatively , the output signals y l , y r might be provided to the hlm and ( apply ) bf units in a time frequency domain representation ( k , m ). in that case separate conversions in the iacov and ( apply ) bf units can be dispensed with . the beamformer filter (( apply ) bf ) provide as an output an apparent signal - to - noise ratio snr ( k , m ) as a function of time and frequency . the speech intelligibility estimation unit ( si - p ) for producing a final estimate of the intelligibility si - m experienced by the hearing aid user corresponds to block evaluate monaural si predictor and signal si estimate in fig2 . the speech intelligibility estimation unit ( si - p ) may further benefit from other inputs , e . g . as shown by dashed line arrows target and noise interaural covariance matrices c s , c v . in the block diagram of fig4 a further processing control unit ( p - cnt ) is shown to provide separates control signals pcnt l and pcnt r for controlling or influencing the processing of the electric input signals x 1 , l . . . , x m , l and x 1 , r , . . . , x m , r , respectively , to the signal processing units ( spu ) of the left and right hearing devices hd l , hd r ( as also illustrated in fig1 and 6 ). fig5 shows a second embodiment of a binaural hearing system according to the present disclosure . the embodiment of fig5 is similar to the embodiment of fig1 apart from extra input signals ( shown in dashed or dotted line in fig5 ) provided to the binaural speech intelligibility prediction unit ( bin - si ) as described in the following . the signal processing of each of the left and right hearing devices is guided by an estimate of the binaural speech intelligibility experienced by the hearing aid user . to help estimate inter - aural covariance matrices , the binaural speech intelligibility prediction block ( bin - si , running in the left - ear hearing device hd l ) uses microphone signals x 1 , l , s 2 , l from the left hearing device hd l , and microphone signals x 1 , r , x 2 , r , from the right hearing device hd r ( wirelessly transmitted from left to right ), all four signals shown in dashed line in fig5 . furthermore , it uses knowledge of the signal processing applied to the microphone signals for the left ( dotted arrow denoted pr l from the signal processing unit ( spu ) of the left hearing device hd l to binaural speech intelligibility prediction unit bin - si ) as well as wirelessly transmitted knowledge of the signal processing applied to the microphone signals in the right hearing device hd r ( dotted arrow from the signal processing unit ( spu ) of the right hearing device hd r to binaural speech intelligibility prediction block ( bin - si ). an important step in the proposed scheme for providing a binaural speech intelligibility predictor is the estimation of the inter - aural target and noise covariance matrices c s , c v of the hearing aid output signals y l , y r . this estimation may be difficult to perform reliably based only on the output signals ( y l ( t ) and y r ( t )) of the hearing devices ( as shown in in fig1 ). instead or additionally , these covariance matrices may be estimated using a ) the noisy microphone signals x 1 , l , x 2 , l and x 1 , r , x 2 , r and b ) the signal processing pr l , pr r applied to them to arrive at y , l ( t ) and y , r ( t ) ( these optional extra inputs . are also shown in fig4 as inputs to the iacov - unit ( dotted arrows ). therefore , in extended versions of the idea , the binaural intelligibility prediction block uses as inputs some or all of the noisy microphone signals along with information about the signal processing applied to these signals in each ha . the information ( represented by signals pr l , pr r ) may for example be the filter weights of a beamformer ( as a function of frequency ), the gain / suppression applied by a single - channel noise reduction filter ( as a function of frequency ), the gain applied by an amplification / dynamic range compression system ( as a function of frequency ), etc ., as illustrated in fig5 . compared to the basic system in fig1 , more signals need to be communicated wirelessly ( additional dashed lines in fig5 ). obviously , systems “ between ” the relatively simple system in fig1 and the more complex system in fig5 are possible . fig6 shows an embodiment of a left hearing device of a binaural hearing system according to the present disclosure . the embodiment of a left hearing device ( hd l ) of fig6 is equivalent to the one shown and discussed in connection with fig5 . on differences is a ) that instead of 2 microphones , the left hearing device ( hd l ) of fig6 comprises m input units ( e . g . microphones ), where m ≧ 2 , and each input unit being adapted to pick up a sound ( x 1 , l , . . . , x m , l ) from the environment and convert it to a corresponding electric signal , which are input to the signal processing unit ( spu ) as well as to the binaural speech intelligibility predictor unit ( bin - si ) together with electric input signals ( x 1 , r , . . . , x m , r ) received via communication link ( link ) from the right hearing device ( hd r ) of the binaural hearing system . another difference is b ) that the signal processing unit ( spu ) comprises a multi input noise reduction system ( comprising a beamformer filter ( bf ) and a single - channel noise reduction unit ( sc - nr )) for providing a noise reduced estimate of the target signal , and a further processing unit ( fp ) for applying further processing algorithms to the noise reduced estimate of the target signal , e . g . including the application of a level and frequency dependent gain according to a user &# 39 ; s needs , etc ., to provide a resulting output signal y l . the mentioned algorithms may be influenced by control signal pcnt l from the binaural speech intelligibility predictor unit ( bin - si ) to provide an optimized combined binaural speech intelligibility . likewise , characteristics of the currently applied processing algorithms in the signal processing unit may be transferred to the binaural speech intelligibility predictor unit ( bin - si ) via signal pr l , and used in the generation of processing control signal pcnt l ( and pcnt r ). it is intended that the structural features of the devices described above , either in the detailed description and / or in the claims , may be combined with steps of the method , when appropriately substituted by a corresponding process . as used , the singular forms “ a ,” “ an ,” and “ the ” are intended to include the plural forms as well ( i . e . to have the meaning “ at least one ”), unless expressly stated otherwise . it will be further understood that the terms “ includes ,” “ comprises ,” “ including ,” and / or “ comprising ,” when used in this specification , specify the presence of stated features , integers , steps , operations , elements , and / or components , but do not preclude the presence or addition of one or more other features , integers , steps , operations , elements , components , and / or groups thereof . it will also be understood that when an element is referred to as being “ connected ” or “ coupled ” to another element , it can be directly connected or coupled to the other element but an intervening elements may also be present , unless expressly stated otherwise . furthermore , “ connected ” or “ coupled ” as used herein may include wirelessly connected or coupled . as used herein , the term “ and / or ” includes any and all combinations of one or more of the associated listed items . the steps of any disclosed method is not limited to the exact order stated herein , unless expressly stated otherwise . it should be appreciated that reference throughout this specification to “ one embodiment ” or “ an embodiment ” or “ an aspect ” or features included as “ may ” means that a particular feature , structure or characteristic described in connection with the embodiment is included in at least one embodiment of the disclosure . furthermore , the particular features , structures or characteristics may be combined as suitable in one or more embodiments of the disclosure . the previous description is provided to enable any person skilled in the art to practice the various aspects described herein . various modifications to these aspects will be readily apparent to those skilled in the art , and the generic principles defined herein may be applied to other aspects . the claims are not intended to be limited to the aspects shown herein , but is to be accorded the full scope consistent with the language of the claims , wherein reference to an element in the singular is not intended to mean “ one and only one ” unless specifically so stated , but rather “ one or more .” unless specifically stated otherwise , the term “ some ” refers to one or more . accordingly , the scope should be judged in terms of the claims that follow . r . beutelmann and t . brand , “ prediction of speech intelligibility in spatial noise and reverberation for normal - 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