Patent Publication Number: US-9426566-B2

Title: Apparatus and method for suppressing noise from voice signal by adaptively updating Wiener filter coefficient by means of coherence

Description:
BACKGROUND OF THE INVENTION 
     1. Field of the Invention 
     The present invention relates to an apparatus and a method for processing voice signals, and more particularly to such an apparatus and a method applicable to, for example, telecommunications devices and software treating voice signals for use in, e.g. telephones or teleconference systems. 
     2. Description of the Background Art 
     As a noise suppression scheme, available is the voice switch, which is based upon a targeted voice section detection in which from input signals temporal sections are determined in which a targeted speaker is talking, i.e. “targeted voice sections”, to output signals in targeted voice sections as they are while attenuating signals in temporal sections other than targeted voice sections, i.e. “untargeted voice sections”. For example, when an input signal is received, a decision is made on whether or not the signal is in a targeted voice section. If the input signal is in a targeted voice section, then the gain of the voice section, or targeted voice section, is set to 1.0. Otherwise, the gain is set to an arbitrary positive value less than 1.0 to amplify the input signal with the gain to thereby attenuate the latter to develop a corresponding output signal. 
     As another noise suppression scheme, the Wiener filter approach is available, which is disclosed in U.S. patent application publication No. US 2009/0012783 A1 to Klein. According to Klein, background noise components contained in input signals are suppressed by determining untargeted voice sections, from which noise characteristics are estimated for the respective frequencies to calculate, or estimate, Wiener filter coefficients based on the noise characteristics to multiply the input signal by the Wiener filter coefficients. 
     The voice switch and the Wiener filter can be applied to a voice signal processor for use in, e.g. a video conference system or a mobile phone system, to suppress noise to enhance the quality of voice communication. 
     In order to apply the voice switch and the Wiener filter, it is necessary to distinguish targeted voice sections from untargeted voice sections, which may include “disturbing voice” uttered by a person other than the targeted speaker and/or “background noise” such as office or street noises. To take an example of distinction method available, the targeted/untargeted voice sections may be distinguished by means of a property known as coherence. In the context, coherence may be defined as a physical quantity depending upon an arrival direction in which an input signal is received. In an application of cellular phones, for example, targeted voices are distinguishable from untargeted voices in arrival directions so that the targeted voice, or speech sound, arrives from the front of a cellular phone set whereas among untargeted voice disturbing voice tends to arrive in directions other than the front and background noise is not distinctive in arrival direction. Accordingly, targeted voices can be discriminated from untargeted voices by focusing on the arrival directions thereof. 
     It will now briefly be described why coherence may be used in order to discriminate targeted voice sections from untargeted voice sections. In a normal detection of targeted voice sections, targeted voice sections may be discriminated from untargeted voice sections based on fluctuation in level of an input signal. In this method, it is impossible to discriminate between disturbing voice and targeted voice and, therefore, disturbing voice cannot be suppressed by the voice switch. Thus, the untargeted voice suppression will be insufficient. By contrast, in a detection relying on coherence, discrimination is made using the arrival directions of input signals. Hence, it is possible to discriminate between targeted and disturbing voices which arrive from the directions distinctive from each other. The untargeted voice suppression can effectively be attained by means of the voice switch. 
     When using the voice switch together with the Wiener filter, more effective noise suppression could be attained than where both measures are used separately since the voice switch effectively suppresses untargeted voice sections and simultaneously the Wiener filter effectively suppresses noise components involved in targeted voice sections. 
     Although the voice switch and the Wiener filter are classified into a noise suppressing technique, they are different in noise sections to be detected for the purpose of optimal operation. It is sufficient for the voice switch to have the capability of detecting untargeted voice sections which contain either or both of disturbing voice and background noise. By contrast, the Wiener filter has to detect temporal sections only containing background noise, or “background noise sections”, among untargeted voice sections. Because, if a filter coefficient were adapted in a disturbing voice section, then the character of “voice” that disturbing voice contains would also be reflected on a Wiener filter coefficient which should have been applied to noise, thus causing even voice components targeted voice contains to be suppressed so as to deteriorate the sound quality. 
     As described so far, when the voice switch and Wiener filter are used in combination, their respectively optimal temporal sections would have to be detected. In spite of this, in the prior art, the same reference was applied between the voice switch and the Wiener filter for detecting untargeted voice sections, raising a problem that a Wiener filter coefficient reflected form the characteristics of disturbing voice may deteriorate targeted voice. 
     This problem could be solved by using plural schemes in parallel which are respectively appropriate for a voice switch and a Wiener filter for detecting untargeted voice sections to thereby detect appropriate temporal sections. In this case, the amount of computation would be increased. In addition, adjustment would have to be made on plural parameters behaving differently from each other, raising a further problem that the user of the system would further be burdened with computation. 
     SUMMARY OF THE INVENTION 
     It is an object of the present invention to provide an apparatus and a method for processing voice signals by appropriately using coherence obtained from background noise sections to adaptively update a Wiener filter coefficient in higher accuracy without extensively burdening the user, thus being improved in sound quality. 
     In accordance with the present invention, an apparatus for suppressing a noise component of an input voice signal comprises: a first directivity signal generator calculating a difference in arrival time between input voice signals to form a first directivity signal having a directivity pattern substantially being null in a first direction; a second directivity signal generator calculating a difference in arrival time between the input voice signals to form a second directivity signal having a directivity pattern substantially being null in a second direction; a coherence calculator using the first and second directivity signals to obtain coherence; a targeted voice section detector making a decision based on the coherence on whether the input voice signal is in a targeted voice section including a voice signal arriving from a targeted direction or in an untargeted voice section including a voice signal arriving from an untargeted direction different from the targeted direction; a coherence behavior calculator obtaining information on a difference of an instantaneous value of the coherence from an average value of the coherence; a Wiener filter (WF) adapter comparing difference information obtained in the coherence behavior calculator with a predetermined threshold value to determine a temporal section in the untargeted voice section as a background noise section including a signal of background noise substantially containing no disturbing voice signal, the WF adapter using, when the temporal section currently determined is a background noise section, signal characteristics of the signal in the background noise section to calculate a new WF coefficient; and a WF coefficient multiplier multiplying the input voice signal by the WF coefficient from the WF adapter. 
     In accordance with an aspect of the present invention, a method for suppressing a noise component of an input voice signal by a voice signal processor comprises: calculating by a signal generator a difference in arrival time between input voice signals to form a first directivity signal having a directivity pattern substantially being null in a first direction; calculating by the signal generator a difference in arrival time between input voice signals to form a second directivity signal having a directivity pattern substantially being null in a second direction; using the first and second directivity signals by a coherence calculator to calculate coherence; making by a target voice section detector a decision based on the coherence on whether the input voice signal is in a temporal section of a targeted voice signal arriving from a targeted direction at a targeted direction or in an untargeted voice section at an untargeted direction; obtaining difference information on a difference of an instantaneous value of the coherence from an average value of the coherence by a coherence behavior calculator; comparing by a Wiener filter (WF) adapter the difference information with a predetermined threshold value to detect a background noise section from an untargeted voice section to determine a temporal section in the untargeted voice section as a background noise section including a signal of background noise substantially containing no voice signal, and using, when the temporal section currently checked is a background noise section, signal characteristics of the signal in the background noise section to calculate a new WF coefficient; updating the WF coefficient when the new WF coefficient is obtained; and multiplying the input voice signal by the WF coefficient by a WF coefficient multiplier. 
     In accordance with another aspect of the invention, there is provided a non-transitory computer-readable medium on which is stored a program for having a computer operate as a voice signal processor, wherein the program, when running on the computer, controls the computer to function as the apparatus for suppressing a noise component of an input voice signal described above. 
     According to the present invention, the apparatus and method for processing voice signals are improved in sound quality by using coherence in detecting background noise with higher accuracy in adaptively updating a Wiener filter coefficient without excessively burdening the user. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The objects and features of the present invention will become more apparent from consideration of the following detailed description taken in conjunction with the accompanying drawings in which: 
         FIG. 1  is a schematic block diagram showing the configuration of a voice signal processor according to an illustrative embodiment of the present invention; 
         FIG. 2  is a schematic block diagram useful for understanding a difference in arrival time of two input signals arriving at microphones in a direction at an angle of θ; 
         FIG. 3  shows a directivity pattern caused by a directional signal generator shown in  FIG. 1 ; 
         FIGS. 4 and 5  show directivity patterns exhibited by two directional signal generators shown in  FIG. 1  when θ is equal to 90 degree; 
         FIG. 6  is a schematic block diagram of a coherence difference calculator of the voice signal processor shown in  FIG. 1 ; 
         FIG. 7  is a schematic block diagram of a Wiener filter (WF) adapter of the voice signal processor shown in  FIG. 1 ; 
         FIG. 8  is a flowchart useful for understanding the operation of the coherence difference calculator of the voice signal processor shown in  FIG. 1 ; 
         FIG. 9  is a flowchart useful for understanding the operation of the WF adapter of the voice signal processor shown in  FIG. 1 ; 
         FIG. 10  is a schematic block diagram showing the configuration of a WF adapter according to an alternative embodiment of the present invention; 
         FIG. 11  is a flowchart useful for understanding the operation of a coefficient adaptation control portion of the WF adapter shown in  FIG. 10 ; 
         FIGS. 12 and 13  are schematic block diagrams showing the configuration of voice signal processors according to other alternative embodiments of the present invention; and 
         FIG. 14  shows a directivity pattern caused by a third directional signal generator shown in  FIG. 13 . 
     
    
    
     DESCRIPTION OF THE PREFERRED EMBODIMENTS 
     Now, with reference to the accompanying drawings, referred embodiments in accordance with the present invention will be described below. Since the drawings are merely for illustration, the present invention is not to be restricted by what are specifically shown in the drawings. 
       FIG. 1  is a schematic block diagram showing the configuration of a voice signal processor, generally 1, in accordance with an illustrative embodiment of the present invention, where temporal sections optimal for a voice switch and a Wiener filter are detected only based on behaviors intrinsic to coherence without employing plural types of schemes for detecting voice sections and without extensively burdening the user of the system. Although the constituent elements expect a pair of microphones m_ 1  and m_ 2  may be implemented in place of, or addition to, hardware in the form of software to be stored in and run on a processor system including a central processing unit (CPU), they may be represented in the form of functional boxes as shown in  FIG. 1 . 
     In  FIG. 1 , the voice signal processor  1  according to the embodiment may be applied to, for example, a video conference or cellular phone system, particularly to its terminal set or handset. The voice signal processor  1  comprises microphones m_ 1  and m_ 2 , a fast Fourier transform (FFT) processor  10 , a first and a second directional signal generator  11  and  12 , a coherence calculator  13 , a targeted voice section detector  14 , a gain controller  15 , a Wiener filter (WF) adapter  30 , a WF coefficient multiplier  17 , an inverse fast Fourier transform (IFFT) processor  18 , a voice switch (VS) gain multiplier  19 , and a coherence difference calculator  20 , which are interconnected as depicted. 
     The microphones m_ 1  and m_ 2  are adapted to stereophonically catch sound therearound to produce corresponding input signals s 1 ( n ) and s 2 ( n ) to the FFT processor  10 , respectively, via analog-to-digital (A/D) converters, not shown. Note that the index n is a positive integer indicating the temporal order in which samples of sound signals are entered. In the present specification, a smaller n indicates an older sample and vice versa. 
     The FFT processor  10  is connected to receive strings of input signal s 1  and s 2  from the microphones m_ 1  and m_ 2 , and subjects the strings of input signal s 1  and s 2  to a discrete Fourier transform, i.e. fast Fourier transform with the embodiment. Consequently, the input signals s 1  and s 2  will be represented in the frequency domain. Before applying the fast Fourier transform, analysis frames FRAME  1 (K) and FRAME  2 (K) are made from the input signals s 1  and s 2 . Each of the frames is consisted of N samples, where N is a natural number. An example of FRAME  1  made from the input signal s 1  can be represented as a set of input signals by the following expressions, where the index K is a positive integer indicating the order in which frames are arranged. 
               FRAME   ⁢           ⁢   1   ⁢     (   1   )       =     {       s   ⁢           ⁢   1   ⁢     (   1   )       ,     s   ⁢           ⁢   1   ⁢     (   2   )       ,   …   ,     s   ⁢           ⁢   1   ⁢     (   i   )       ,   …   ,     s   ⁢           ⁢   1   ⁢     (   N   )         }               …           …               FRAME   ⁢           ⁢   1   ⁢     (   K   )       =     {       s   ⁢           ⁢   1   ⁢     (       N   ×   K     +   1     )       ,     s   ⁢           ⁢   1   ⁢     (       N   ×   K     +   2     )       ,   …   ,     s   ⁢           ⁢   1   ⁢     (       N   ×   K     +   i     )       ,   …   ,     s   ⁢           ⁢   1   ⁢     (       N   ×   K     +   N     )         }           
In the present specification, a smaller K indicates an older analysis frame and vice versa. In the following description of operation, it will be assumed that an index indicating the newest analysis frame to be analyzed is K unless otherwise stated.
 
     In the FFT processor  10 , each analysis frame is subjected to the fast Fourier transform. Thus, frequency-domain signals X 1 (f, K) and X 2 (f, K) obtained by subjecting the Fourier transform to the analysis frames FRAME 1 (K) and FRAME 2 (K), respectively, are supplied to the first and second directional signal generators  11  and  12 , where an index f indicates frequency. Additionally, the signal X 1 (f, K) does not take a single value but is composed of spectral components of plural frequencies f 1 -fm as given by the following expression:
 
 X 1( f,K )={ X 1( f 1 ,K ),  X 1( f 2 ,K ) . . . ,  X 1( fi,K ), . . . ,  X 1( fm,K )}
 
Also, the signals X 2 (f, K) as well as B 1 (f, K) and B 2 (f, K) appearing in the rear stage of a directional signal generator are composed of spectral components of plural frequencies.
 
     The first directional signal generator  11  functions as obtaining a signal B 1 (f, K) having its directivity specifically strongest in the rightward direction (R) defined by the following Expression (1): 
                     B   ⁢           ⁢   1   ⁢     (   f   )       =       X   ⁢           ⁢   2   ⁢     (   f   )       -     X   ⁢           ⁢   1   ⁢     (   f   )     ×     exp   ⁡     [       -       i   ⁢           ⁢   2   ⁢   π   ⁢           ⁢   f   ⁢           ⁢   S     N       ⁢   τ     ]                   (   1   )               
where S is the sampling frequency, N is an FFT analysis frame length, τ is the difference in time between a couple of microphones when catching a sound wave, and i is the imaginary unit.
 
     The second directional signal generator  12  functions as obtaining a signal B 2 (f, K) having its directivity strongest in the leftward direction (L) defined by the following Expression (2): 
     
       
         
           
             
               
                 
                   
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     The signals B 1 (f, K) and B 2 (f, K) are represented in the form of complex numbers. Since the frame index K is independent of calculations, it is not included in the computational expressions. 
     With reference to  FIGS. 2 to 5 , it will be described how those expressions mean with the Expression (1) taken as an example. It is assumed that sound waves arrive from a direction at an angle of θ indicated in  FIG. 2  with respect to a reference direction and are picked up by the pair of microphones m_ 1  and m_ 2  spaced apart by a distance of l from each other. In this case, there is a time difference between the instants at which the sound waves are captured by the microphones m_ 1  and m_ 2 . Since the sound wave path difference d may be expressed by d=l×sin θ, the time difference τ is given by the following Expression (3), where c is the sound velocity.
 
τ= l ×sin θ/ c   (3)
 
     When a signal s 1 ( n −τ) represents a signal caught by the microphone m_ 1  earlier by a period of time τ than the time at which the input signal s 2 ( n ) is caught by the microphone m_ 2 , the signal s 1 ( n −τ) and the input signal s 2 ( n ) comprise the same sound component arriving from the direction at the angle of θ. Therefore, calculation of a difference between them will make it possible to obtain a signal which does not include the sound component in the direction at the angle of θ. The signal obtained by finding the difference between the signals of s 2 ( n ) and s 1 ( n −τ) will now be referred to as a signal y(n), i.e. y(n)=s 2 ( n )−s 1 ( n −τ). As a result, the microphone array, m_ 1  and m_ 2 , has its directivity pattern shown in  FIG. 3 , in this example. 
     The description has been provided so far on calculations in the time domain. Similar calculations may be performed in the frequency domain. In the frequency domain calculation, the Expressions (1) and (2) are applied. As an example, it is assumed that angles θ of the directions in which signals arrive are ±90 degrees. Specifically, as shown in  FIG. 4 , the first directional signal generator  11  obtains the directivity signal B 1 (f, K) which has its directivity strongest in the rightward direction. Further, as shown in  FIG. 5 , the second directional signal generator  12  obtains the second directivity signal B 2 (f, K) which has its directivity strongest in the leftward direction. 
     The coherence calculator  13  is adapted to perform calculations according to the following Expressions (4) and (5) on the directivity signals B 1 (f, K) and B 2 (f, K) to thereby obtain coherence COH(K). In Expression (4), B 2 (f, K)* is a complex conjugate to B 2 (f, K). Since the frame index K is again not dependent upon calculations, the index does not appear in those expressions. 
     
       
         
           
             
               
                 
                   
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     In the targeted voice section detector  14 , the coherence COH (K) is compared with a targeted voice section decision threshold value Θ. If the coherence is greater than the threshold value Θ, it is determined that the temporal section is a targeted voice section. Otherwise, it is determined that the temporal section is an untargeted voice section. 
     Now, it will briefly be described why a targeted voice section is detected depending on the magnitude of coherence. The concept of coherence can be described as a correlation between a signal incoming from the right and a signal incoming from the left with respect to a microphone. Expression (4) is for use in calculating the correlation for a frequency component. Expression (5) is used to calculate the average of correlation values over the entire frequency components. Accordingly, when coherence COH is smaller, the correlation between the two directivity signals B 1  and B 2  is smaller. Conversely, when coherence COH is larger, the correlation is larger. When the input signals have the correlation thereof smallest, their directions of arrival are extreme right or left with respect to the microphone, or the input signals are small in periodicity as with noises even though the arrival directions are not in directions other than the front (F) of the microphone. Therefore, it can be said that a temporal section where the value of coherence COH of the input signals is smaller may be deemed as a disturbing voice or a background noise section, i.e. an untargeted voice section. By contrast, a temporal section where the value of coherence COH of the input signals is larger, the directions of arrival are not in directions other than the front, and hence it can be said that the input signals arrive from the front. Under those circumstances, since it is assumed that the targeted voice arrives from the front of the microphone, it can be said that the section where the coherence COH of the input signals is larger is a targeted voice section. 
     In a gain controller  15 , if the temporal section is a targeted voice section, the gain VS_GAIN of the voice section is set to 1.0. If the temporal section is an untargeted voice section, the gain VS_GAIN is set to an arbitrary positive value cc less than 1.0. 
     The coherence difference calculator  20  calculates the difference δ(K) between an instantaneous value COH(K) of coherence in an untargeted voice section and the long-term average value AVE_COH (K) of coherence settled in the calculator  20 . The WF adapter  30  of the embodiment is adapted for detecting background noise sections, and using the difference δ(K) and the instantaneous value COH(K) of the coherence to calculate a new Weiner filter coefficient to deliver the new WF_COEF(f, K) to the WF coefficient multiplier  17 . 
     The background noise sections will be detected by means of the features of coherence, as will be described below. In a targeted voice section, coherence generally exhibits larger values, and targeted voice greatly fluctuates in amplitude, i.e. involves larger and smaller amplitude components. By contrast, in an untargeted voice section, the value is generally smaller and fluctuates only a little. Furthermore, even in the untargeted voice sections, coherence varies in a limited range. In a temporal section where the waveform such as disturbing voice includes a clear periodicity, such as pitch of speech, a correlation tends to appear and coherence is relatively larger. In a temporal section having its regularity smaller, coherence shows especially smaller values. It can be said that a temporal section having its periodicity smaller is a background noise section. 
       FIG. 6  is a schematic block diagram particularly showing the configuration of the coherence difference calculator  20 . As shown in the figure, the coherence difference calculator  20  has a coherence receiver  21 , a coherence long-term average calculator  22 , a coherence subtractor  23 , and a coherence difference sender  24 , which are interconnected as depicted. 
     The coherence receiver  21  is connected to receive the coherence COH(K) computed by the coherence calculator  13 . The targeted voice section detector  14  is adapted for determining whether or not the coherence COH (K) of the currently processed subject, e.g. frame, belongs to an untargeted voice section. 
     The coherence long-term average value calculator  22  serves as updating, if the currently processed signal belongs to an untargeted voice section, the coherence long-term average AVE_COH (K) according to the following Expression (6):
 
AVE_ COH ( K )=β× COH ( K )+(1−β)×AVE_ COH ( K− 1)  (6)
 
where 0.0&lt;β&lt;1.0. It is to be noted that the expression for calculating the coherence long-term average AVE_COH(K) is not restricted to the Expression (6). Rather, other calculation expressions such as simple averaging of a given number of sample values may be applied.
 
     The coherence subtractor  23  serves to calculate the difference δ(K) between the coherence long-term average AVE_COH (K) and the coherence COH (K) according to the following Expression (7).
 
δ( K )=AVE_ COH ( K )− COH ( K )  (7)
 
The coherence difference sender  24  supplies the WF adapter  30  with the obtained difference δ(K).
 
       FIG. 7  is a schematic block diagram of the WF adapter  30  of the embodiment, particularly showing the configuration of the adapter  30 . As seen from the figure, the WF adapter  30  has a coherence difference receiver  31 , a background noise section determiner  32 , a WF coefficient adapter  33 , and a WF coefficient sender  34 , which are interconnected as illustrated. 
     The coherence difference receiver  31  is connected to receive the coherence COH (K) and the coherence difference δ(K) from the coherence difference calculator  20 . 
     The background noise section determiner  32  functions to determine whether or not a temporal section is a background noise section. If a background noise section has its coherence COH(K) smaller than a threshold value Θ for a targeted voice and the coherence difference δ(K) is smaller than a threshold value Φ(Φ&lt;0.0) for a coherence difference, then the background noise section determiner  32  determines the temporal section of interest is a background noise section. 
     If the result of the determination made by the background noise section determiner  32  is that the temporal section under determination is a background noise section, the WF coefficient adapter  33  then obtains the characteristic of background noise based on the signals in this section determined as a noise section and calculates a new Wiener filter coefficient. Otherwise, the adapter  33  does not obtain a new Wiener filter coefficient. The adapter  33  may obtain the characteristic of the background noise according to a well-known method as disclosed in Klein described earlier. 
     The WF coefficient sender  34  supplies the WF coefficient multiplier  17  with the new Wiener filter coefficient obtained by the WF coefficient adapter  33 . In the following, the operation performed by the adapter  30  may be referred to as “adaptation operation.” 
     When the WF coefficient multiplier  17  receives the Wiener filter coefficient WF_COEF(f, K) from the WF adapter  30 , it updates the Wiener filter coefficient set in the multiplier  17 . In the WF coefficient multiplier  17 , the FFT-transformed signal X 1 (f, K) of the input signal string s 1 ( n ) is multiplied by the coefficient defined by the following Expression (8). Consequently, obtained is a signal P(f, K) that is an input signal whose background noise characteristics have been suppressed.
 
 P ( f,K )= X 1( f,K )× WF _ COEF ( f,K )  (8)
 
     The IFFT processor  18  converts the background noise suppressed signal P(f, K) to a corresponding time-domain signal string q(n), and then the VS gain multiplier multiplies the signal string q(n) by the gain VS_GAIN (K) set by the gain controller and defined by the following Expression (9). As a result, an output signal y(n) is obtained.
 
 y ( n )= q ( n )× VS _GAIN( K )  (9)
 
     Since the background noise characteristic is thus obtained from the signals in the background noise section and the noise characteristic is used to calculate the Wiener filter coefficient, the Wiener filter coefficient is not reflected by the characteristic of disturbing voice, and thus, deterioration of the targeted voice can be prevented. 
     The operation of the voice signal processor  1  of the embodiment will next be described with further reference to  FIGS. 8 and 9 . The general operation, and detailed operation of the coherence difference calculator  20  and the WF adapter  30  will be described in turn. 
     Signals produced from the pair of microphones m_ 1  and m_ 2  are transformed from the time domain into frequency-domain signals X 1 (f, K) and X 2 (f, K) by the FFT processor  10 . From the signals X 1 (f, K) and X 2 (f, K), directivity signals B 1 (f, K) and B 2 (f, K) that have null in certain azimuthal directions, or blind directions, are produced by the first and second directional signal generators  11  and  12 , respectively. The signals B 1 (f, K) and B 2 (f, K) are used to calculate the coherence COH(K) by means of Expressions (4) and (5). 
     The targeted voice section detector  14  makes a decision on whether or not the temporal section the signals s 1 ( n ) and s 2 ( n ) belong to is a targeted voice section. Based on the result of the decision made in the detector  14 , the gain VS_GAIN(K) is set in the gain controller  15 . 
     The coherence difference calculator  20  calculates the difference δ(K) between the instantaneous value COH (K) of the coherence in an untargeted voice section and the long-term average value AVE_COH(K) of the coherence. In the WF adapter  30 , the coherence COH(K) and the difference δ(K) are used to detect background noise sections. Then a noise characteristic is newly obtained from the background noise section to calculate a Wiener filter coefficient to send the latter to the WF coefficient multiplier  17  so as to update the Wiener filter coefficient set in the multiplier  17 . In the WF coefficient multiplier  17 , the input signal X 1 (f, K) in the frequency domain is multiplied by the Wiener filter coefficient WF_COEF(f, K). The resultant signal P(f, K), namely, the signal P(f, K) suppressed by a Wiener filter technique, is converted to a time-domain signal string q(n) by the IFFT processor  18 . In the VS gain multiplier  19 , this signal q(n) is multiplied by the gain VS_GAIN (K) set by the gain controller  15 , thus producing a resultant output signal y(n). 
     The operation of the coherence difference calculator  20  will be described.  FIG. 8  is a flowchart for use in understanding the operation of the coherence difference calculator  20 . 
     When the coherence receiver  21  receives the coherence COH(K), the receiver  21  references the targeted voice section detector  14  to determine whether or not the subject signal belongs to an untargeted voice section (step S 200 ). If the subject signal is determined as an untargeted voice section, then the coherence long-term average calculator  22  updates the coherence long-term average AVE_COH(K) according to Expression (6) (step S 201 ). Thence, the coherence subtractor  23  subtracts the coherence COH(K) from the coherence long-term average AVE_COH(K) according to Expression (7) to thereby obtain the difference δ(K) (step S 202 ). The obtained coherence difference δ(K) is fed from the coherence difference sender  24  to the WF adapter  30 . The subject to be processed is in turn updated (step S 203 ) to repetitively proceed to the processing operations described so far. 
     The operation of the WF adapter  30  will be described with reference to  FIG. 9 , which is a flowchart useful for understanding the operation of the WF adapter  30 . 
     When the coherence difference receiver  31  receives the coherence COH (K) and the coherence difference δ(K) in step S 250 , the background noise section detector  32  determines whether or not the coherence COH(K) is substantially smaller than the threshold value Θ and the coherence difference δ(K) is smaller than the threshold value Φ(&lt;0.0), in other words, whether or not the temporal section to which the subject signal belongs is a background noise section (step S 251 ). If it is determined as a background noise section, the WF coefficient adapter  33  obtains a noise characteristic from the signals in this noise section to calculate a new Wiener filter coefficient (step S 252 ). Otherwise, the adapter  33  does not obtain a new Wiener filter coefficient (step S 253 ). The new Wiener filter coefficient WF_COEF(f, K) is supplied from the WF coefficient sender  34  to the WF coefficient multiplier  17  so as to update the Wiener filter coefficient set in the multiplier  17  (step S 254 ). 
     In summary, according to the illustrative embodiment, the feature that coherence is smaller especially in background noise sections is utilized to detect sections purely including background noise among untargeted voice sections, and only the feature of the background noise is used for calculation of the Wiener filter coefficient. Signal sections adapted for the voice switch and the Wiener filter can thus be detected using a single parameter, i.e. coherence, thus making it possible to properly use both of the voice switch and the Wiener filter. The problem raised in the prior art that targeted voice was distorted by a Wiener filter coefficient on which the characteristics of disturbing voice are reflected can be overcome. Furthermore, optimum sections can be detected without introducing multiple voice section detecting schemes. Hence, the amount of calculation can be prevented from increasing. It is not necessary to adjust plural parameters of different characteristics. The burden on the user of the system can be prevented from increasing. 
     A telecommunications device or system such as a video conference system or cellular phone system comprised of the voice signal processor of the illustrative embodiment may advantageously be improved in the quality of telephone communications. 
     Next, an alternative embodiment of the present invention will be described by referring further to  FIGS. 10 and 11 . The embodiment shown in  FIG. 1  is adapted to discriminate the background noise sections from the untargeted voice sections to estimate the Wiener filter coefficient. Thus, the coefficient can accurately be estimated. However, the coefficient may be estimated less frequently. This would take a long time until sufficient noise suppressing performance is attained so as to render the user of the system exposed to the unfavorable circumstances of sound quality. 
     The WF adapter according to the alternative embodiment comprises a coefficient adaptation rate controller  38 ,  FIG. 10 . The reflection of characteristics of background noise on the Wiener filter coefficient is changeable in such a fashion that immediately after the start of adaptive operation the characteristic of the instantaneous background noise will immediately be reflected on the coefficient and thereafter its reflection on the coefficient will be reduced. 
     The voice signal processor according to this alternative embodiment may be similar to the voice signal processor  1  according to the illustrative embodiment shown in and described with reference to  FIG. 1  except for the details of configuration and operation of the WF adapter  30 A,  FIG. 10 . Therefore, only the WF adapter  30 A of the alternative embodiment will be described. 
       FIG. 10  is a schematic block diagram of the WF adapter  30 A of this alternative embodiment, particularly showing the configuration of the adaptation portion  30 A. As shown in the figure, the WF adapter  30 A has a coefficient adaptation rate controller  35  in addition to the coherence difference receiver  31 , background noise section detector  32 , WF coefficient adapter  33 A and WF coefficient sender  34 , which are interconnected as depicted. Like components or elements are designated with the same reference numerals, and a repetitive description thereon will be avoided. 
     The coefficient adaptation rate controller  35  is adapted to count the number of temporal sections determined as background noise sections and sets the value of a parameter λ that is used to control to which extent the noise characteristics of the subject background noise section reflects on the Wiener filter coefficient according to whether or not the obtained count is substantially smaller than a predetermined threshold value. 
     If the result of the determination made by the background noise section detector  32  is that the temporal section under determination is not a background noise section, then the WF coefficient adapter  33 A will not calculate a new Wiener filter coefficient and the signal X 1 (f, K) will be multiplied with the Wiener filter coefficient obtained from the signals in the preceding background noise section. If the result of the determination made by the background noise section detector  32  is that the temporal section under determination is a background noise section, then the adapter  33 A will make use of the parameter λ received from the coefficient adaptation rate controller  35  to estimate in computation a new Wiener filter coefficient. 
     The role of the parameter λ will now briefly be described. A Wiener filter coefficient may be obtained by a calculation according to the expression disclosed in Klein. 
     Prior to this calculation, background noise characteristics have to be calculated for each frequency. Background noise may be estimated using the expression disclosed in Klein. The parameter λ assumes values from 0.0 to 1.0, inclusive, and acts to control how much the instantaneous input value is reflected on the background noise characteristic. 
     As the parameter λ is increased, the effect of the instantaneous input becomes more intensive. Conversely, as the parameter decreases, the effect of the instantaneous input becomes less intensive. Accordingly, when the parameter λ is larger, the instantaneous input is more strongly reflected on the Wiener filter coefficient, and it is thus possible to promptly adapt the Wiener filter coefficient to the background noise. However, since the effect of the instantaneous input is strong, the coefficient value remarkably varies so as to deteriorate the naturalness of sound quality. Conversely, when the parameter λ is smaller, the prompt reflection of the instantaneous input cannot be achieved but the obtained coefficient is not greatly affected by the instantaneous characteristics, and past noise characteristics are reflected averagely. Thus, the coefficient does not vary greatly so that the naturalness of sound quality may be maintained. 
     Since the parameter λ behaves as described so far, high-speed erasing performance can be accomplished by setting larger the parameter λ immediately after the start of the adaptive operation. After some period of time has lapsed, the parameter λ is set smaller. As a result, natural sound quality can be accomplished. The operation of the WF adapter  30 A of the instant embodiment has briefly been described thus far. 
     The operation of the coefficient adaptation controller  35  will be described with reference to the flowchart shown in  FIG. 11 . 
     First, based on the result of the decision made by the background noise section detector  32 , the coefficient adaptation controller  35  makes a decision on whether or not the temporal section being checked is a background noise section (step S 300 ). If the decision reveals the temporal section is a background noise section, then the counter value is incremented by one n(K) in order to determine whether or not the background noise section occurred immediately after the start of the adaptation operation (step S 301 ). Otherwise, the counter value n(K) is not incremented. Then, the counter value n(K) is compared with a threshold value T, where T is a positive integer, for an initial adaptation time to make a determination on whether or not the background noise section occurred immediately after the start of the adaptation operation. If the counter value n(K) is less than the threshold value T, it is determined that the background noise section occurred immediately after the start of the adaptation operation for the Wiener filter coefficient. If the value is equal to or greater than the threshold value T, it is determined that the background noise section did not occur immediately after the start of the adaptation operation (step S 302 ). If the background noise section is determined as one having occurred immediately after the start of the adaptation operation, then the parameter λ is set to a larger value in order to reflect the noise characteristic of the subject background noise on the Wiener filter coefficient promptly (step S 303 ). If that is not the case, the parameter λ is set to a smaller value to suppress the reflection of the noise characteristic of the subject background noise (step S 304 ). 
     According to the alternative embodiment, immediately after the start of the adaptation operation, the Wiener filter coefficient is quickly adapted to background noise so that high-speed noise suppression may be accomplished. Furthermore, after a lapse of some period of time, the influence of background noise at the time on the Wiener filter coefficient is reduced, so that excessive adaptation to instantaneous noises can be prevented. Thus, natural sound quality may be maintained. 
     Improvement may thus be expected on the sound quality of telephone communications in a telecommunications system or device such as a video conference system or cellular phone system exploiting the voice signal processor of the instant alternative embodiment. 
     Next, another alternative embodiment of voice signal processor according to the present invention will be described with reference to  FIG. 12 . A voice signal processor  1 B according to the present alternative embodiment may be similar in configuration to the embodiment shown in  FIG. 1  except that a coherence filter configuration is added. 
     A coherence filter is adapted to multiply an input signal X 1 (f, K) by an obtained coherence “coef(f, K)” so as to suppress components of the signal incoming not from the front but from the left or right with respect to the microphone. 
       FIG. 12  is a schematic block diagram showing the configuration of the voice signal processor  1 B associated with this alternative embodiment. Again, like components or elements are designated with the same reference numerals. 
     In  FIG. 12 , the voice signal processor  1 B according to this alternative embodiment may be similar in configuration to that of the embodiment shown in  FIG. 1  except that a coherence filter coefficient multiplier  40  is added and that the WF coefficient multiplier  173  is slightly modified in operation. 
     The coherence filter coefficient multiplier  40  has its one input port supplied with coherence “coef(f, K)” from the coherence calculator  13 . The multiplier  40  also has its other input port supplied with an input signal X 1 (f, K) converted in the frequency domain from the FFT processor  10 . The multiplier  40  multiplies both of them with each other by means of the following Expression (10) to thereby obtain a coherence-filtered signal R 0 (f, K).
 
 R 0( f,K )= X 1( f,K )× coef ( f,K )  (10)
 
     The WF coefficient multiplier  17 B of this embodiment multiplies the coherence-filtered signal R 0 (f, K) by the Wiener filter coefficient WF_COEF(f, K) from the WF adapter  30  as given by the following Expression (11), thus obtaining a Wiener-filtered signal P(f, K).
 
 P ( f,K )= R 0( f,K )× WF _ COEF ( f,K )  (11)
 
     The subsequent processing performed by the IFFT processor  18  and VS gain multiplier  19  may be the same as the embodiment shown in  FIG. 1 . 
     The present alternative embodiment has the coherence filtering function thus added. That makes higher noise suppressing performance attained than that of the embodiment shown in and described with reference to  FIG. 1 . 
     Another alternative embodiment of voice signal processor according to the present invention will be described with reference to  FIGS. 13 and 14 . The voice signal processor  10  according to this alternative embodiment may be similar in configuration to the embodiment shown in  FIG. 1  except that a frequency reduction is added to reduce noise by subtracting a noise signal from an input signal. 
       FIG. 13  is a schematic block diagram showing the configuration of the voice signal processor  10  associated with this alternative embodiment. Again, like components and elements are designated with the same reference numerals. 
     With reference to  FIG. 13 , the voice signal processor associated with this embodiment may be similar in configuration to the embodiment shown in  FIG. 1  except that a frequency reducer  50  is added and that the WF coefficient multiplier  17 C is slightly modified in operation. The frequency reducer  50  has a third directional signal generator  51  and a subtractor  52 , which are interconnected as illustrated. 
     The third directional signal generator  51  is connected to be supplied with two input signals X 1 (f, K) and X 2 (f, K) transformed in the frequency domain from the FFT processor  10 . The third directional signal generator  51  is adapted to form a third directivity signal B 3 (f, K) complying with a directivity pattern that is null in the front as shown in  FIG. 14 . The third directivity signal B 3 (f, K), i.e. noise signal, is in turn connected to one input, or subtrahend input, of the subtractor  52 , which has its other input, or minuend input, connected to receive an input signal X 1 (f, K) transformed in the frequency domain. The subtractor  52  is adapted to subtract the third directivity signal B 3 (f, K) from the input signal X 1 (f, K) according to the following Expression (12) to thereby obtain a frequency-reduced signal R 1 (f, K).
 
 R 1( f,K )= X 1( f,K )− B 3( f,K )  (12)
 
     The WF coefficient multiplier  170  of this alternative embodiment multiplies the frequency-reduced signal R 1 (f, K) by the Wiener filter coefficient WF_COEF(f, K) fed from the WF adapter  30  according to the following Expression (13) to thereby obtain a Wiener filtered signal P(f, K).
 
 P ( f,K )= R 1( f,K )× WF _ COEF ( f,K )  (13)
 
     The subsequent processing performed by the IFFT processor  18  and VS gain multiplier  19  may be the same as the illustrative embodiment shown in  FIG. 1 . 
     According to the current alternative embodiment shown in  FIG. 13 , the frequency reducing function is added, thus accomplishing higher noise suppression. 
     The present invention may not be restricted to the above illustrative embodiments. Rather, modified embodiments as exemplified below are also possible. 
     As can be seen from the description of the above embodiments, two kinds of noise suppressing schemes, i.e. a voice switch and a Wiener filter, are used in the above embodiments. The above-described embodiments are specifically featured by extracting temporal sections consisting only of background noise based on the coherence. This feature especially contributes to improvement of the Wiener filter performance. Accordingly, the invention may also be applied to a voice signal processor introducing only a Wiener filter as a noise suppressing scheme. One example of a voice signal processor having only a Wiener filter as a noise suppressing scheme may be designed by eliminating the gain controller  15  and the VS gain multiplier  19  from the configuration shown in  FIG. 1 . 
     In the above-described embodiments, temporal sections consisting only of background noise among determined untargeted voice sections are detected based on the difference δ(K) between the instantaneous value COH (K) of the coherence and the long-term average value AVE_COH (K) of the coherence. Temporal sections consisting only of background noise may also be detected according to the magnitude of the variance or standard deviation of the coherence. The variance of the coherence indicates the deviation of instantaneous values COH(K) of the coherence from the average value of a given number of the newest instantaneous values of the coherence, and thus can be a parameter indicating the behavior of the coherence in the same way as the coherence difference. 
     The coherence filter shown in  FIG. 12  and the frequency reducer shown in  FIG. 13  may both be added to the embodiment shown in  FIG. 1 . 
     Still alternatively, at least either of the coherence filter and the frequency reducer may be added to the configuration of the embodiment shown in and described with reference to  FIGS. 10 and 11 . 
     In the embodiment shown in  FIGS. 10 and 11 , the adaptation rate is switched between two levels according to the value of the parameter λ. By setting plural threshold values, the influence of instantaneous background noise on the Wiener filter coefficient may be adjusted at three or more levels according to the values of the parameter λ corresponding to the threshold values. 
     Regarding the targeted voice section detector, the WF adapter in the above-described embodiments makes a decision based on coherence on whether or not the temporal section of interest is a targeted voice section. Alternatively, the decision may be made on another component on behalf of the WF adapter so that the WF adapter can only utilize the result of the detection. The term “targeted voice section detector”, particularly set forth in the following claims, may be comprehended as any component which makes a decision based on coherence on whether or not the temporal section is a targeted voice section. Thus, when the WF adapter is adapted to make the decision, the targeted voice section detector in the claims may be comprehended as the WF adapter. When the WF adapter only utilizes the result of the detection made by an external, targeted voice section detector, this external detector may be comprehended as the targeted voice section detector. 
     In the above-described embodiments, the voice switch processing is performed after having performed the Wiener filter processing. These two types of processing may be reversed in order. 
     In the above illustrative embodiments, the input signals in the time domain may be transformed into the signals in the frequency domain to be processed. If desired, a system may be adapted to process signals in the time domain. Conversely, processing of signals in the time domain may be replaced by processing of signals in the frequency domain. 
     The above-described illustrative embodiments are adapted to a voice signal processor that processes signals immediately when picked up by a pair of microphones. Sound signals to be processed in accordance with the present invention may not be restricted to this type of signal. For instance, the voice signal processor may be adapted to process a pair of stereophonic sound signals read out from a recording medium. Further, the processor may be adapted to process a pair of sound signals sent from opposite devices. 
     The entire disclosure of Japanese patent application No. 2011-198728 filed on Sep. 12, 2011, including the specification, claims, accompanying drawings and abstract of the disclosure is incorporated herein by reference in its entirety. 
     While the present invention has been described with reference to the particular illustrative embodiments, it is not to be restricted by the embodiments. It is to be appreciated that those skilled in the art can change or modify the embodiments without departing from the scope and spirit of the present invention.