# Patent

## Title
Method and acoustic signal processing system for interference and noise suppression in binaural microphone configurations

## Abstract

A method determines a bias reduced noise and interference estimation in a binaural microphone configuration with a right and a left microphone signal at a time-frame with a target speaker active. The method includes a determination of the auto power spectral density estimate of the common noise formed of noise and interference components of the right and left microphone signals and a modification of the auto power spectral density estimate of the common noise by using an estimate of the magnitude squared coherence of the noise and interference components contained in the right and left microphone signals determined at a time frame without a target speaker active. An acoustic signal processing system and a hearing aid implement the method for determining the bias reduced noise and interference estimation. The noise reduction performance of speech enhancement algorithms is improved by the invention. Further, distortions of the target speech signal and residual noise and interference components are reduced.

## Background
CROSS-REFERENCE TO RELATED APPLICATION
This application claims the priority, under 35 U.S.C. §119, of European patent application EP 10005957, filed Jun. 9, 2010; the prior application is herewith incorporated by reference in its entirety.
BACKGROUND OF THE INVENTION
Field of the Invention
The present invention relates to a method and an acoustic signal processing system for noise and interference estimation in a binaural microphone configuration with reduced bias. Moreover, the present invention relates to a speech enhancement method and hearing aids.
Until recently, only bilateral speech enhancement techniques were used for hearing aids, i.e., the signals were processed independently for each ear and thereby the binaural human auditory system could not be matched. Bilateral configurations may distort crucial binaural information as needed to localize sound sources correctly and to improve speech perception in noise. Due to the availability of wireless technologies for connecting both ears, several binaural processing strategies are currently under investigation. Binaural multi-channel Wiener filtering approaches preserving binaural cues for the speech and noise components are state of the art. For multi-channel techniques determining the noise components in each individual microphone is desirable. Since, in practice, it is almost impossible to obtain these separate noise estimates, the combination of a common noise estimate with single-channel Wiener filtering techniques to obtain binaural output signals is investigated.
FIG. 1 depicts a well known system for blind binaural signal extraction and a two microphone setup (M1, M2). Hearing aid devices with a single microphone at each ear are considered. The mixing of the original sources sq[k] is modeled by a filter of length M denoted by an acoustic mixing system AMS.
This leads to the microphone signals xp[k]




  
    
      
        
          
            
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where hqp[k], k=0, . . . , M−1 denote the coefficients of the filter model from the q-th source sq[k], q=1, . . . , Q to the p-th sensor xp[k], pε{1, 2}. The filter model captures reverberation and scattering at the user's head. The source s1[k] is seen as the target source to be separated from the remaining Q−1 interfering point sources sq[k], q=2, . . . , Q and babble noise denoted by nbp[k], pε{1, 2}. In order to extract desired components from the noisy microphone signals xp[k], a reliable estimate for all noise and interference components is necessary. A blocking matrix BM forces a spatial null to a certain direction φtar which is assumed to be the target speaker location to assure that the source signal s1[k] arriving from that direction can be suppressed well. Thus, an estimate for all noise and interference components is obtained which is then used to drive speech enhancement filters wi[k], iε{1, 2}. The enhanced binaural output signals are denoted by yi[k], iε{1, 2}.

For all speech enhancement algorithms a good noise estimate is the key for the best possible noise reduction. For binaural hearing aids and a two-microphone setup, the easiest way to obtain a noise estimate is to subtract both channels x1[k], x2[k] assuming that the desired signal component is the same in both channels. There are also more sophisticated solutions that can also deal with reverberation. Generally, the noise estimate ñ[v,n] is given in the time-frequency domain by




  
    
      
        
          
            
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where v and n denote the frequency band and the block index, respectively. bp[v,n], pε{1, 2} denoteS the spectral weights of the blocking matrix BM. Since with such blocking matrices only a common noise estimate ñ[v,n] is available it is essential to compute a single speech enhancement filter applied to both microphone signals x1[k], x2[k]. A well-known single Wiener filter approach is given in the time-frequency domain by





  
    
      
        
          
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where μ is a real number and can be chosen to achieve a trade-off between noise reduction and speech distortion. Ŝññ[v,n] and Ŝvpvp[v,n], pε{1, 2} denote auto power spectral density (PSD) estimates from the estimated noise signal ñ[v,n] and the filtered microphone signals. The microphone signals are filtered with the coefficients of the blocking matrix according to equation 2.

The noise estimation procedures (e.g. subtracting the signals from both channels x1[k], x2[k] or more sophisticated approaches based on blind source separation) lead to an unavoidable systematic error (=bias).
SUMMARY OF THE INVENTION
It is accordingly an object of the invention to provide a method and acoustic signal processing system for interference and noise suppression in binaural microphone configurations which overcome the above-mentioned disadvantages of the heretofore-known devices and methods of this general type and which provide for noise and interference estimation in a binaural microphone configuration with reduced bias. It is a further object to provide a related speech enhancement method and a related hearing aid.
With the foregoing and other objects in view there is provided, in accordance with the invention, a method for a bias reduced noise and interference estimation in a binaural microphone configuration with a right and a left microphone signal at a timeframe with a target speaker active. The method comprises the following method steps:
determining the auto power spectral density estimate of a common noise estimate comprising noise and interference components of the right and left microphone signals and
modifying the auto power spectral density estimate of the common noise estimate by using an estimate of the magnitude squared coherence of the noise and interference components contained in the right and left microphone signals determined at a time frame without a target speaker active.
The method uses a target voice activity detection and exploits the magnitude squared coherence of the noise components contained in the individual microphones. The magnitude squared coherence is used as criterion to decide if the estimated noise signal obtains a large or a weak bias.
According to a further preferred embodiment of the method, the magnitude squared coherence (MSC) is calculated as




  
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where Ŝv,n1n2 is the cross power spectral density of the by a blocking matrix filtered noise and interference components contained in the right and left microphone signals, Ŝv,n1v,n1 is the auto power spectral density of the by said blocking matrix filtered noise and interference components contained in the right microphone signal and Ŝv,n2V,n2 is the auto power spectral density of the by said blocking matrix filtered noise and interference components contained in the left microphone signal.
In accordance with an additional feature of the invention, the bias reduced auto power spectral density estimate Ŝ{circumflex over (n)}{circumflex over (n)} of the common noise is calculated as

Ŝ{circumflex over (n)}{circumflex over (n)}=MSC·(Ŝv,n1v,n1+Ŝv,n2v,n2)+(1−MSC)·Ŝññ,

where Ŝññ is the auto power spectral density estimate of the common noise estimate.
In accordance with an additional feature of the invention, the above object is solved by a further method for a bias reduced noise and interference estimation in a binaural microphone configuration with a right and a left microphone signal. At timeframes during which a target speaker is active, the bias reduced auto power spectral density estimate is determined according to the method for a bias reduced noise and interference estimation according to the invention and at time frames during which the target speaker is inactive, the bias reduced auto power spectral density estimate is calculated as Ŝññ=Ŝv,n1v,n1+Ŝv,n2v,n2.
In accordance with a preferred embodiment of the invention, the bias reduced auto power spectral density estimate is determined in different frequency bands.
According to the present invention, the above object is further solved by a method for speech enhancement with a method described above, wherein the bias reduced auto power spectral density estimate is used for calculating filter weights of a speech enhancement filter.
With the above and other objects in view there is also provided, in accordance with the invention, an acoustic signal processing system for a bias reduced noise and interference estimation at a timeframe in which a target speaker is active with a binaural microphone configuration comprising a right and left microphone with a right and a left microphone signal. The system comprises:
a power spectral density estimation unit determining the auto power spectral density estimate of the common noise estimate comprising noise and interference components of the right and left microphone signals; and
a bias reduction unit modifying the auto power spectral density estimate of the common noise estimate by using an estimate of the magnitude squared coherence of the noise and interference components contained in the right and left microphone signals determined at a time frame without a target speaker active.
According to a further preferred embodiment of the acoustic signal processing system, the bias reduced auto power spectral density estimate Ŝññ of the common noise is calculated as

Ŝññ=MSC·(Ŝv,n1v,n1+Ŝv,n2v,n2)+(1−MSC)·Ŝññ.

where Ŝññ is the auto power spectral density estimate of the common noise.

In accordance with again an added feature of the invention, the acoustic signal processing system further comprises a speech enhancement filter with filter weights which are calculated by using the bias reduced auto power spectral density estimate.
With the above and other objects in view there is also provided, in accordance with the invention, a hearing aid with an acoustic signal processing system as outlined above.
Finally, there is provided a computer program product with a computer program which comprises software means for executing a method for bias reduced noise and interference estimation according to the invention, if the computer program is executed in a processing unit.
The invention offers the advantage over existing methods that no assumption about the properties of noise and interference components is made. Moreover, instead of introducing heuristic parameters to constrain the speech enhancement algorithm to compensate for noise estimation errors, the invention directly focuses on reducing the bias of the estimated noise and interference components and thus improves the noise reduction performance of speech enhancement algorithms. Moreover, the invention helps to reduce distortions for both, the target speech components and the residual noise and interference components.
The above described methods and systems are preferably employed for the speech enhancement in hearing aids. However, the present application is not limited to such use only. The described methods can rather be utilized in connection with other binaural/dual-channel audio devices.
Other features which are considered as characteristic for the invention are set forth in the appended claims.
Although the invention is illustrated and described herein as embodied in a method and acoustic signal processing system for interference and noise suppression in binaural microphone configurations, it is nevertheless not intended to be limited to the details shown, since various modifications and structural changes may be made therein without departing from the spirit of the invention and within the scope and range of equivalents of the claims.
The construction and method of operation of the invention, however, together with additional objects and advantages thereof will be best understood from the following description of specific embodiments when read in connection with the accompanying drawings.



BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING
FIG. 1 a block diagram of an acoustic signal processing system for binaural noise reduction without bias correction according to prior art,
FIG. 2 a block diagram of an acoustic signal processing system for binaural noise reduction with bias correction,
FIG. 3 an overview about four test scenarios and
FIG. 4 a diagram of SIR improvement for the invented system depicted in FIG. 2.



DETAILED DESCRIPTION OF THE INVENTION
The core of the invention is a method to obtain a noise PSD estimate with reduced bias.
In the following, for the sake of clarity, the block index n as well as the subband index v are omitted. Assuming that the necessary noise estimate ñ is obtained by equation 2, equation 3 can be written in the time-frequency domain as




  
    
      
        
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where hqp denotes the spectral weight from source q=1, . . . , Q to microphone p, pε{1, 2} for the frequency band v. s1 is assumed to be the desired source and sq, q=2, . . . , Q denote interfering point sources. By equation (4), an optimum noise suppression can only be achieved if the noise components in the numerator are the same as in the denominator. Assuming an optimum desired speech suppression by the blocking matrix BM and defining s1 as desired speech signal to be extracted from the noisy signal xp, pε{1, 2}, we derive a noise PSD estimation bias ΔŜññ. The common noise PSD estimate Ŝññ is identified from equations 2, 3, and 4 as





  
    
      
        
          
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Applying the well-known standard Wiener filter theory to equation (4), the optimum noise estimate Ŝnono that would be necessary to achieve a best noise suppression reads however





  
    
      
        
          
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The estimated bias ΔŜññ is then given as the difference between the obtained common noise PSD estimate Ŝññ and the optimum noise PSD estimate Ŝnono and reads




  
    
      
        
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From equation (7) it can be seen that the noise PSD estimation bias ΔŜññ is described by the correlation of the noise components in the individual microphone signals x1, x2. As long as the correlation of the noise components in the individual channels x1, x2 is high, this bias ΔŜññ is also high. Only for ideally uncorrelated noise components, the bias ΔŜññ will be zero. As the noise PSD estimation bias ΔŜññ is signal-dependent (equation (7) depends on the PSD estimates of the source signals Ŝsqsq) and the signals are highly non-stationary as we consider speech signals, equation (7) can hardly be estimated at all times and all frequencies. Only if the target speaker s1 is inactive, the noise PSD estimation bias ΔŜññ can be obtained as the microphone signals x1, x2 contain only noise and interference components and thus the bias of the noise PSD estimate Ŝññ can be reduced.
In order to obtain a bias reduced noise PSD estimate Ŝññ even if the target speaker s1 is active, reliable parameters related to the noise PSD estimation bias ΔŜññ that can be applied even if the target speaker is active, need to be estimated. This is important as speech signals are considered as interference which are highly non-stationary signals. Thus it is not sufficient to estimate the noise PSD estimation error ΔŜññ during target speech pauses only.
According to the invention, a valuable quantity is the well-known Magnitude Squared Coherence (MSC) of the noise components. On the one hand, if the MSC is low (close to zero), then ΔŜññ (equation 7) is low, since the cross-correlation between the noise components in the right and left channels x1, x2 is weak. On the other hand, if the MSC is close to one, the noise PSD estimation bias |ΔŜññ| (equation 7) becomes quite high as the noise components contained in the microphone signals x1, x2 are strongly correlated. Using the MSC it is possible to decide whether the common noise estimate exhibits a strong or a low bias ΔŜññ.
In summary, a noise PSD estimate Ŝññ with reduced bias can be obtained by:

    
    
        using the microphone signals x2 as noise and interference estimate during target speech pauses; and
        applying the MSC of the noise and interference components of the microphone signals estimated during target speech pauses to decide whether the common noise estimate exhibits a strong or a low bias.
    
    


We now describe the way how to reduce the bias ΔŜññ if the target speaker is active and the MSC is close to one will be discussed next. First of all, a target Voice Activity Detector VAD for each time-frequency bin is necessary (just as in standard single-channel noise suppression) to have access to the quantities described previously. If the target speaker is inactive (s1≡0), the by BM filtered microphone signals x1, x2 can directly be used as noise estimate. The PSD estimate Ŝvpvp of the filtered microphone signals is then given by




  
    
      
        
          
            
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where Ŝv,npv,np describes the by the blocking matrix BM filtered noise components of the right and left channel x1, x2, respectively. Thus, the noise PSD estimate with reduced bias Ŝññ is given by

Ŝññ=Ŝv,n1v,n1+Ŝv,n2v,n2  (9)

Moreover, during target speech pauses, the MSC of the noise components in the right and left channel x1, x2 is estimated. The estimated MSC is applied to decide whether the common noise PSD estimate Ŝññ (equation 5) exhibits a strong or a low bias. The MSC of the filtered noise components in the right and left channel x1, x2 is given by




  
    
      
        MSC
        =
        
          
            |
            
              
                S
                ^
              
              
                v
                ,
                
                  
                    n
                    1
                  
                  ⁢
                  v
                
                ,
                
                  n
                  2
                
              
            
            ⁢
            
              |
              2
            
          
          
            
              
                S
                ^
              
              
                v
                ,
                
                  
                    n
                    1
                  
                  ⁢
                  v
                
                ,
                
                  n
                  1
                
              
            
            ⁢
            
              
                S
                ^
              
              
                v
                ,
                
                  
                    n
                    1
                  
                  ⁢
                  v
                
                ,
                
                  n
                  2
                
              
            
          
        
      
    
    
      
        (
        10
        )
      
    
  




and is always in the range of 0≦MSC≦1. MSC=1 indicates ideally correlated signals whereas MSC=0 means ideally de-correlated signals. If the MSC is low, the common noise PSD estimate Ŝññ given by equation 5 is already an estimate with low bias and thus we can use:

Ŝññ=Ŝññ.  (11)

If the MSC is close to one, Ŝññ (equation 5) represents an estimate with strong bias, since |ΔŜññ| (equation 7) becomes quite high. In this case, the following combination is proposed to obtain the bias reduced noise PSD estimate Ŝññ:

Ŝ{circumflex over (n)}{circumflex over (n)}=MSC·(Ŝv,n1v,n1+Ŝv,n2v,n2)+(1−MSC)·Ŝññ,  (12)

where Ŝv,n1v,n1+Ŝv,n2v,n2 is an estimate taken from the most recent data frame with s1=0. In general, the noise PSD estimate with reduced bias Ŝññ is given by

Ŝññ=α·(Ŝv,n1v,n1+Ŝv,n2v,n2)+(1−α)·Ŝññ  (13)

where α=1 if the target speaker is inactive, otherwise α=MSC. For obtaining Ŝññ obviously it is needed to estimate three different quantities, namely the MSC, a target VAD for each time-frequency bin, and an estimate of Ŝv,n1v,n1+Ŝv,n2v,n2.

FIG. 2 shows a block diagram of an acoustic signal processing system for binaural noise reduction with bias correction according to the invention described above. The system for blind binaural signal extraction comprises a two microphone setup, a right microphone M1 and a left microphone M2. For example, the system can be part of binaural hearing aid devices with a single microphone at each ear. The mixing of the original sources sq is modeled by a filter denoted by an acoustic mixing system AMS. The acoustic mixing system AMS captures reverberation and scattering at the user's head. The source s1 is seen as the target source to be separated from the remaining Q−1 interfering point sources sq, q=2, . . . , Q and babble noise denoted by nbp, pε{1, 2}. In order to extract desired components from the noisy microphone signals xp, a reliable estimate for all noise and interference components is necessary. A blocking matrix BM forces a spatial null to a certain direction Φtar which is assumed to be the target speaker location assuring that the source signal s1 arriving from this direction can be suppressed well. The output of the blocking matrix BM is an estimated common noise signal ñ, an estimate for all noise and interference components.
The microphone signals x1, x2, the common noise signal ñ, and a voice activity detection signal VAD are used as input for a noise power density estimation unit PU. In the unit PU, the noise and interference PSD Ŝv,npv,np, pε{1, 2} as well as the common noise PSD Ŝññ and the MSC are calculated. These calculated values are inputted to a bias reduction unit BU. In the bias reduction unit the common noise PSD Ŝññ is modified according to equation 13 in order to get a desired bias reduced common noise PSD Ŝññ.
The bias reduced common noise PSD Ŝññ is then used to drive speech enhancement filters w1, w2 which transfer the microphone signals x1, x2 to enhanced binaural output signals y1, y2.
Estimation of the MSC
The estimate of the MSC of the noise components is considered to be based on an ideal VAD. The MSC of the noise components is in the time-frequency domain given by




  
    
      
        
          
            MSC
            ⁡
            
              [
              
                v
                ,
                n
              
              ]
            
          
          =
          
            
              |
              
                
                  
                    S
                    ^
                  
                  
                    
                      n
                      1
                    
                    ⁢
                    
                      n
                      2
                    
                  
                
                ⁡
                
                  [
                  
                    v
                    ,
                    n
                  
                  ]
                
              
              ⁢
              
                |
                2
              
            
            
              
                
                  
                    S
                    ^
                  
                  
                    
                      n
                      1
                    
                    ⁢
                    
                      n
                      1
                    
                  
                
                ⁡
                
                  [
                  
                    v
                    ,
                    n
                  
                  ]
                
              
              ⁢
              
                
                  
                    S
                    ^
                  
                  
                    
                      n
                      2
                    
                    ⁢
                    
                      n
                      2
                    
                  
                
                ⁡
                
                  [
                  
                    v
                    ,
                    n
                  
                  ]
                
              
            
          
        
        ,
      
    
    
      
        (
        14
        )
      
    
  




where v denotes the frequency bin and n is the frame index. Ŝn1n2[v,n] represents the cross PSD of the noise components n1[v,n] and n2[v,n]. Ŝnpnp[v,n], pε{1, 2} denotes the auto PSD of np[v,n], pε{1, 2}. The noise components np[v,n], pε{1, 2} are only accessible during the absence of the target source, consequently, the MSC can only be estimated at these time-frequency points and is calculated by:





  
    
      
        
          
            MSC
            _
          
          ⁡
          
            [
            
              
                v
                I
              
              ,
              n
            
            ]
          
        
        =
        
          
            |
            
              
                
                  S
                  ^
                
                
                  v
                  ,
                  
                    
                      n
                      1
                    
                    ⁢
                    v
                  
                  ,
                  
                    n
                    2
                  
                
              
              ⁡
              
                [
                
                  
                    v
                    I
                  
                  ,
                  n
                
                ]
              
            
            ⁢
            
              |
              2
            
          
          
            
              
                
                  S
                  ^
                
                
                  v
                  ,
                  
                    
                      n
                      1
                    
                    ⁢
                    v
                  
                  ,
                  
                    n
                    1
                  
                
              
              ⁡
              
                [
                
                  
                    v
                    I
                  
                  ,
                  n
                
                ]
              
            
            ⁢
            
              
                
                  S
                  ^
                
                
                  v
                  ,
                  
                    
                      n
                      2
                    
                    ⁢
                    v
                  
                  ,
                  
                    n
                    2
                  
                
              
              ⁡
              
                [
                
                  
                    v
                    I
                  
                  ,
                  n
                
                ]
              
            
          
        
      
    
    
      
        (
        15
        )
      
    
  
  
    
      
        
          
        
        ⁢
        
          
            =
            
              
                |
                
                  
                    
                      S
                      ^
                    
                    
                      
                        v
                        1
                      
                      ⁢
                      
                        v
                        2
                      
                    
                  
                  ⁡
                  
                    [
                    
                      
                        v
                        I
                      
                      ,
                      n
                    
                    ]
                  
                
                ⁢
                
                  |
                  2
                
              
              
                
                  
                    
                      S
                      ^
                    
                    
                      
                        v
                        1
                      
                      ⁢
                      
                        v
                        1
                      
                    
                  
                  ⁡
                  
                    [
                    
                      
                        v
                        I
                      
                      ,
                      n
                    
                    ]
                  
                
                ⁢
                
                  
                    
                      S
                      ^
                    
                    
                      
                        v
                        2
                      
                      ⁢
                      
                        v
                        2
                      
                    
                  
                  ⁡
                  
                    [
                    
                      
                        v
                        I
                      
                      ,
                      n
                    
                    ]
                  
                
              
            
          
          ,
        
      
    
    
      
        (
        16
        )
      
    
  




where v,np[vI,n], pε{1, 2} are the filtered noise components and vp[vI,n], pε{1, 2} are the filtered microphone signals x1, x2. The time-frequency points [vI,n] represent the set of those time-frequency points where the target source is inactive, and, correspondingly, [vA,n] denote those time-frequency points dominated by the active target source. Note that here we use v,np[vI,n] instead of np[vI,n], since in equation 13 the coherence of the filtered noise components is considered. Besides, in order to have reliable estimates, the obtained MSC is recursively averaged with a time constant 0<β<1:





  
    
      
        
          
            MSC
            _
          
          ⁡
          
            [
            
              
                v
                I
              
              ,
              n
            
            ]
          
        
        =
        
          
            β
            ·
            
              
                MSC
                _
              
              ⁡
              
                [
                
                  
                    v
                    I
                  
                  ,
                  
                    n
                    -
                    1
                  
                
                ]
              
            
          
          +
          
            
              (
              
                1
                -
                β
              
              )
            
            ·
            
              
                
                  |
                  
                    
                      
                        S
                        ^
                      
                      
                        
                          v
                          1
                        
                        ⁢
                        
                          v
                          2
                        
                      
                    
                    ⁡
                    
                      [
                      
                        
                          v
                          I
                        
                        ,
                        n
                      
                      ]
                    
                  
                  ⁢
                  
                    |
                    2
                  
                
                
                  
                    
                      
                        S
                        ^
                      
                      
                        
                          v
                          1
                        
                        ⁢
                        
                          v
                          1
                        
                      
                    
                    ⁡
                    
                      [
                      
                        
                          v
                          I
                        
                        ,
                        n
                      
                      ]
                    
                  
                  ⁢
                  
                    
                      
                        S
                        ^
                      
                      
                        
                          v
                          2
                        
                        ⁢
                        
                          v
                          2
                        
                      
                    
                    ⁡
                    
                      [
                      
                        
                          v
                          I
                        
                        ,
                        n
                      
                      ]
                    
                  
                
              
              .
            
          
        
      
    
    
      
        (
        17
        )
      
    
  




Since the noise components are not accessible at the time-frequency point of the active target source, MSC cannot be updated but keeps the value estimated at the same frequency bin of the previous frame:

 MSC[vA,n]= MSC[vA,n−1].  (18)

Estimation of the Separated Noise PSD
The second term to be estimated for equation 13 is the sum of the power of the noise components contained in the individual microphone signals. During target speech pauses, due to the absence of the target speech signal, there is access to these components getting

Ŝv1v1[vI,n]+Ŝv2v2[vI,n]=Ŝv,n1v,n1[vI,n]+Ŝv,n2v,n2[vI,n]. 

Now, a correction function is introduced given by




  
    
      
        
          
            f
            Corr
          
          ⁡
          
            [
            
              
                v
                I
              
              ,
              n
            
            ]
          
        
        =
        
          
            
              
                
                  
                    S
                    ^
                  
                  
                    
                      v
                      1
                    
                    ⁢
                    
                      v
                      1
                    
                  
                
                ⁡
                
                  [
                  
                    
                      v
                      I
                    
                    ,
                    n
                  
                  ]
                
              
              +
              
                
                  
                    S
                    ^
                  
                  
                    
                      v
                      2
                    
                    ⁢
                    
                      v
                      2
                    
                  
                
                ⁡
                
                  [
                  
                    
                      v
                      I
                    
                    ,
                    n
                  
                  ]
                
              
            
            
              
                
                  S
                  ^
                
                
                  
                    n
                    ~
                  
                  ⁢
                  
                    n
                    ~
                  
                
              
              ⁡
              
                [
                
                  
                    v
                    I
                  
                  ,
                  n
                
                ]
              
            
          
          .
        
      
    
    
      
        (
        19
        )
      
    
  




This correction function ƒCorr[vIn] is then used to correct the original noise PSD estimate Ŝññ[vI,n] to obtain an estimate of the separated noise PSD Ŝv,n1v,n1[vI,n]+Ŝv,n2v,n2[vI,n] that is necessary for equation 13. Again, in order to obtain a reliable estimate of the correction function, the estimates are recursively averaged with a time constant 0<γ<1:




  
    
      
        
          
            f
            Corr
          
          ⁡
          
            [
            
              
                v
                I
              
              ,
              n
            
            ]
          
        
        =
        
          
            γ
            ·
            
              
                f
                Corr
              
              ⁡
              
                [
                
                  
                    v
                    I
                  
                  ,
                  
                    n
                    -
                    1
                  
                
                ]
              
            
          
          +
          
            
              (
              
                1
                -
                γ
              
              )
            
            ·
            
              
                
                  
                    
                      S
                      ^
                    
                    
                      
                        v
                        1
                      
                      ⁢
                      
                        v
                        1
                      
                    
                  
                  ⁡
                  
                    [
                    
                      
                        v
                        I
                      
                      ,
                      n
                    
                    ]
                  
                
                +
                
                  
                    
                      S
                      ^
                    
                    
                      
                        v
                        2
                      
                      ⁢
                      
                        v
                        2
                      
                    
                  
                  ⁡
                  
                    [
                    
                      
                        v
                        I
                      
                      ,
                      n
                    
                    ]
                  
                
              
              
                
                  
                    S
                    ^
                  
                  
                    
                      n
                      ~
                    
                    ⁢
                    
                      n
                      ~
                    
                  
                
                ⁡
                
                  [
                  
                    
                      v
                      I
                    
                    ,
                    n
                  
                  ]
                
              
            
          
        
      
    
    
      
        (
        20
        )
      
    
  




An estimate of Ŝv,n1v,n1[vI,n]+Ŝv,n2v,n2[vI,n] can now be obtained by

Ŝv,n1v,n1[vI,n]+Ŝv,n2v,n2[vI,n]=Ŝv1v1[vI,n]+Ŝv2v2[vI,n]=ƒCorr[vI,n]·Ŝññ[vI,n].  (21)

However, at the time-frequency points of active target speech Ŝv1v1[vA,n]+Ŝv2v2[vA,n]+Ŝv,n1v,n1[vA,n]+Ŝv,n2v,n2[vA,n] is not true and the correction function (equation 19) cannot be updated. But, since the PSD estimates are obtained by time-averaging, the spectra of the signals are supposed to be similar for neighboring frames. Therefore, at the time-frequency points of active target speech, one can take the correction function estimated at the same frequency bin for the previous frame:

ƒCorr[vA,n]=ƒCorr[vA,n−1],  (22)

such that Ŝv,n1v,n1[vA,n]+Ŝv,n2v,n2[vA,n] can be estimated by:

Ŝv,n1v,n1[vA,n]+Ŝv,n2,v,n2[vA,n]=ƒCorr[vA,n]·Ŝññ[vA,n].  (23)

Now, based on the estimated MSC and the estimated noise PSD, the improved common noise estimate can be calculated by:

Ŝ{circumflex over (n)}{circumflex over (n)}[v,n]= MSC[v,n]·(Ŝv,n1v,n1[v,n]+Ŝv,n2v,n2[v,n])+(1− MSC[v,n])·Ŝññ[v,n].  (24)

Then, the original speech enhancement filter given by equation 3 can now be recalculated with a noise PSD estimate that obtains a reduced bias:




  
    
      
        
          
            
              w
              
                Im
                ⁢
                
                  
                
                ⁢
                p
              
            
            ⁡
            
              [
              
                v
                ,
                n
              
              ]
            
          
          =
          
            1
            -
            
              μ
              ⁢
              
                
                  
                    
                      S
                      ^
                    
                    
                      
                        n
                        ~
                      
                      ⁢
                      
                        n
                        ~
                      
                    
                  
                  ⁡
                  
                    [
                    
                      v
                      ,
                      n
                    
                    ]
                  
                
                
                  
                    
                      
                        S
                        ^
                      
                      
                        
                          v
                          1
                        
                        ⁢
                        
                          v
                          1
                        
                      
                    
                    ⁡
                    
                      [
                      
                        v
                        ,
                        n
                      
                      ]
                    
                  
                  +
                  
                    
                      
                        S
                        ^
                      
                      
                        
                          v
                          2
                        
                        ⁢
                        
                          v
                          2
                        
                      
                    
                    ⁡
                    
                      [
                      
                        v
                        ,
                        n
                      
                      ]
                    
                  
                
              
            
          
        
        ,
      
    
    
      
        (
        25
        )
      
    
  




where Ŝññ[v,n] is obtained by equation (24).

Evaluation
In the sequel, the proposed scheme (FIG. 2) with the enhanced noise estimate (equation 24) and the improved Wiener filter (equation 25) is evaluated in various different scenarios with a hearing aid as illustrated in FIG. 3. The desired target speaker is denoted by s and is located in front of the hearing aid user. The interfering point sources are denoted by ni, iε{1, 2, 3} and background babble noise is denoted by nbp, pε{1, 2}. From Scenario 1 to Scenario 3, the number of interfering point sources ni is increased. In Scenario 4, additional background babble noise nbp is added (in comparison to Scenario 3).
Corresponding to the scenarios 1 to 4, the SIR (signal-to-interference-ratio) of the input signal decreases from −0.3 dB to −4 dB. The signals were recorded in a living-room-like environment with a reverberation time of about T60≈300 ms. In order to record these signals, an artificial head was equipped with Siemens Life BTE hearing aids without processors. Only the signals of the frontal microphones of the hearing aids were recorded. The sampling frequency was 16 kHz and the distance between the sources and the center of the artificial head was approximately 1.1 m.
FIG. 4 illustrates the SIR improvement for a living-room-like environment (T60≈300 ms) and 256 subbands. The SIR improvement is defined by




  
    
      
        
          SIR
          gain
        
        =
        
          
            1
            2
          
          ⁢
          
            
              ∑
              
                p
                =
                1
              
              2
            
            ⁢
            
              
            
            ⁢
            
              
                (
                
                  
                    SIR
                    
                      out
                      p
                    
                  
                  -
                  
                    SIR
                    
                      in
                      p
                    
                  
                
                )
              
              ⁢
              
                d
                B
              
            
          
        
      
    
    
      
        (
        26
        )
      
    
  
  
    
      
        
          
        
        ⁢
        
          =
          
            
              1
              2
            
            ⁢
            
              
                ∑
                
                  p
                  =
                  1
                
                2
              
              ⁢
              
                
              
              ⁢
              
                
                  (
                  
                    
                      
                        σ
                        
                          s
                          
                            out
                            p
                          
                        
                        2
                      
                      
                        σ
                        
                          n
                          
                            out
                            p
                          
                        
                        2
                      
                    
                    -
                    
                      
                        σ
                        
                          s
                          
                            in
                            p
                          
                        
                        2
                      
                      
                        σ
                        
                          n
                          
                            in
                            p
                          
                        
                        2
                      
                    
                  
                  )
                
                ⁢
                
                  
                    d
                    B
                  
                  .
                
              
            
          
        
      
    
    
      
        (
        27
        )
      
    
  








  σ
  
    s
    
      out
      p
    
  
  2




and





  σ
  
    n
    
      out
      p
    
  
  2




represent the (long-time) signal power of the speech components and the residual noise and interference components at the output of the proposed scheme (FIG. 2), respectively.





  σ
  
    s
    
      i
      ⁢
      
        
      
      ⁢
      
        n
        p
      
    
  
  2




and





  σ
  
    n
    
      i
      ⁢
      
        
      
      ⁢
      
        n
        p
      
    
  
  2




represent the (long-time) signal power of the speech components and the noise and interference components at the input.

The first column in FIG. 4 for each scenario shows the SIR improvement obtained for the scheme depicted in FIG. 1 without the proposed method for bias reduction. The noise estimate is obtained by equation 2 and the spectral weights bp[v,n], pε{1, 2} are obtained by using a BSS-based algorithm. The spectral weights for the speech enhancement filter are obtained by equation 3. The second column in FIG. 4 represents the maximum performance achieved by the invented method to reduce the bias of the common noise estimate (equations 13 and 25). Here, it is assumed that all terms that in reality need to be estimated are known. The last column depicts the SIR improvement achieved by the invented approach with the estimated MSC (equations 17 and 18), the estimated noise PSD (equation 24), and the improved speech enhancement filter given by equation 25. It should be noted that the target VAD for each time-frequency bin is still assumed to be ideal. It can be seen that the proposed method can achieve about 2 to 2.5 dB maximum improvement compared to the original system, where the bias of the common noise PSD is not reduced. Even with the estimated terms (last column), the proposed approach can still achieve an SIR improvement close to the maximum performance.
These results show that the novel method for reducing the noise bias of the common noise estimate according to the invention works well in practical applications and achieves a high improvement compared to an approach in which the noise PSD estimation bias is not taken into account.

## Claims

1. A method for determining a bias reduced noise and interference estimation in a binaural microphone configuration, the method which comprises:
receiving with the binaural microphone configuration a right microphone signal and a left microphone signal during a time-frame with a target speaker active;
determining an auto power spectral density estimate of a common noise containing noise components and interference components of the right and left microphone signals; and
modifying the auto power spectral density estimate of the common noise by using an estimate of a magnitude squared coherence of the noise components and interference components contained in the right and left microphone signals determined during a time frame without a target speaker active.

2. The method according to claim 1, which comprises calculating the magnitude squared coherence estimate MSC as




  
    MSC
    =
    
      
        |
        
          
            S
            ^
          
          
            v
            ,
            
              
                n
                1
              
              ⁢
              v
            
            ,
            
              n
              2
            
          
        
        ⁢
        
          |
          2
        
      
      
        
          
            S
            ^
          
          
            v
            ,
            
              
                n
                1
              
              ⁢
              v
            
            ,
            
              n
              1
            
          
        
        ⁢
        
          
            S
            ^
          
          
            v
            ,
            
              
                n
                2
              
              ⁢
              v
            
            ,
            
              n
              2
            
          
        
      
    
  
  ,



where:
Ŝv,n1v,n2 is a cross power spectral density of the estimated noise and interference components computed by a blocking matrix from filtered noise and interference components contained in the right and left microphone signals;
Ŝv,n1v,n1 is the auto power spectral density of the noise and interference components contained in the right microphone signal filtered by the blocking matrix; and
Ŝv,n2v,n2 is the auto power spectral density of the noise and interference components contained in the left microphone signal filtered by the blocking matrix.

3. The method according to claim 1, which comprises calculating the bias reduced auto power spectral density estimate Ŝññ of the common noise as


Ŝññ=MSC·(Ŝv,n1v,n1+Ŝv,n2v,n2)+(1−MSC)·Ŝññ,

where Ŝññ is the auto power spectral density estimate of the common noise.

4. A method for a bias reduced noise and interference estimation in a binaural microphone configuration with a right microphone signal and a left microphone signal, the method which comprises:
at time frames with a target speaker inactive, calculating the bias reduced auto power spectral density estimate Ŝññ as


Ŝññ=Ŝv,n1v,n1+Ŝv,n2v,n2 


where
Ŝv,n1v,n1 is the auto power spectral density of the noise and interference components contained in the right microphone signal filtered by the blocking matrix; and
Ŝv,n2v,n2 is the auto power spectral density of the noise and interference components contained in the left microphone signal filtered by the blocking matrix; and
at time frames with the target speaker active, carrying out the method according to claim 1 to determine the bias reduced auto power spectral density estimate Ŝññ.

5. The method according to claim 4, which comprises determining the bias reduced auto power spectral density estimate in different frequency bands.

6. The method according to claim 1, which comprises determining the bias reduced auto power spectral density estimate in different frequency bands.

7. A speech enhancement method, which comprises:
providing a speech enhancement filter; and
performing the method according to claim 1 for determining a bias reduced auto power spectral density estimate; and
utilizing the bias reduced auto power spectral density estimate for calculating filter weights of the speech enhancement filter.

8. A speech enhancement method, which comprises:
providing a speech enhancement filter; and
performing the method according to claim 4 for determining a bias reduced auto power spectral density estimate; and
utilizing the bias reduced auto power spectral density estimate for calculating filter weights of the speech enhancement filter.

9. An acoustic signal processing system for a bias reduced noise and interference estimation at a timeframe with a target speaker active, comprising:
a binaural microphone configuration including a right microphone and a left microphone respectively outputting a right microphone signal and a left microphone signal;
a power spectral density estimation unit connected to receive the right and left microphone signals from said binaural microphone configuration and configured for determining an auto power spectral density estimate of a common noise containing noise and interference components of the right and left microphone signals; and
a bias reduction unit connected to said power spectral density estimation unit and configured for modifying the auto power spectral density estimate of the common noise by using an estimate of a magnitude squared coherence of the noise and interference components contained in the right and left microphone signals determined at a time frame without a target speaker active.

10. The acoustic signal processing system according to claim 9, wherein the bias reduced auto power spectral density estimate Ŝññ of the common noise is calculated as


Ŝññ=MSC·(Ŝv,n1v,n1+Ŝv,n2v,n2)+(1−MSC)·Ŝññ,

where
MSC is the magnitude squared coherence of the noise and interference components;
Ŝññ is the auto power spectral density estimate of the common noise estimate;
Ŝv,n1v,n1 is the auto power spectral density of the noise and interference components contained in the right microphone signal filtered by a blocking matrix; and
Ŝv,n2v,n2 is the auto power spectral density of the noise and interference components contained in the left microphone signal filtered by the blocking matrix.

11. The acoustic signal processing system according to claim 10, which comprises a speech enhancement filter with filter weights that are calculated by using the bias reduced auto power spectral density estimate.

12. The acoustic signal processing system according to claim 9, which comprises a speech enhancement filter with filter weights that are calculated by using the bias reduced auto power spectral density estimate.

13. A hearing aid, comprising the acoustic signal processing system according to claim 9.

14. A computer program product, comprising a non-transitory computer program with computer-executable software means configured to execute the method according to claim 1 when the computer program is loaded onto and executed in a processing unit.