Abstract:
An acoustic signal enhancement method is disclosed. The acoustic signal enhancement method comprises the steps of applying a spectral transformation on a frame derived from an input acoustic signal to generate a spectral representation of the frame, estimating an a posteriori SNR and an a priori SNR of the frame, determining an a priori SNR limit for the frame, limiting the a priori SNR with the a priori SNR limit to generate a final a priori SNR for the frame, determining a spectral gain for the frame according to the a posteriori SNR and the final a priori SNR, and applying the spectral gain on the spectral representation of the frame so as to generate an enhanced spectral representation of the frame. One of the characteristics of the acoustic signal enhancement method is that the a priori SNR limit is a function of frequency.

Description:
BACKGROUND 
     The present invention relates to a method and apparatus for enhancing acoustic signals, and more particularly, to a method and apparatus that adaptively reducing noise that contaminates acoustic signals. 
     During recent years, applications of acoustic signal processing have been developing rapidly. These applications comprise hearing aids, speech encoding, speech recognition, etc. A major challenge encountered by the acoustic signal processing related applications is that they usually have to deal with acoustic signals that are already contaminated by background noise. This fact makes the performance of these applications be downgraded. To solve this problem, a great amount of work has been done in the field of noise suppression, and the following papers are incorporated herein by reference:
     [1] Y. Ephraim and D. Malah, “Speech enhancement using a minimum mean-square error short-time spectral amplitude estimator,” IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. ASSP-32, no. 6, pp. 1109-1121, 1984.   [2] P. J. Wolfe and S. J. Godsill. “Efficient alternatives to the Ephraim and Malah suppression rule for audio signal enhancement.” EURASIP journal on Applied Signal Processing, 2003. To appear. Special Issue: Audio for Multimedia Communications.   [3] I. Cohen and B. Berdugo, “Noise Estimation by Minima Controlled Recursive Aver-aging for Robust Speech Enhancement,” IEEE Sig. Proc. Let., vol. 9, pp. 12-15, January 2002.   [4] D. E. Tsoukalas, J. N. Mourjopoulos, and G. Kokkinakis, “Speech enhancement based on audible noise suppression,” IEEE Trans. Speech and Audio Processing, vol. 88, pp. 497-514, November 1997.   

     Many of the proposed noise suppression algorithms are based on the manipulation of the short-time spectral amplitude (STSA) of the contaminated acoustic signal. This kind of STSA manipulation schemes is widely used for its computational advantage. Among others, MMSE (Minimum Mean Square Error) STSA proposed by Ephraim and Malah (reference [1]) is the most popular STSA based algorithm.  FIG. 1  shows an acoustic signal enhancement apparatus  100  according to the MMSE STSA algorithm proposed by Ephraim and Malah. The acoustic signal enhancement apparatus  100  comprises a frame decomposition &amp; windowing unit  110 , a Fourier transform unit  120 , a noise estimation unit  130 , an a posteriori SNR (signal-to-noise ratio) estimation unit  140 , an a priori SNR estimation unit  150 , a spectral gain calculation unit  160 , a multiplication unit  170 , an inverse Fourier transform unit  180 , and a frame synthesis unit  190 . 
     Assume that a clean speech s(t) is contaminated by a background noise d(t), a noisy speech x(t) received by the acoustic signal enhancement apparatus  100  is given by
 
 x ( t )= s ( t )+ d ( t ),  (1)
 
     where t represents a time index. The frame decomposition &amp; windowing unit  110  segments the noisy speech x(t) into frames of M samples. The frame decomposition &amp; windowing unit  110  further applies an analysis window h(t) of a size 2M with a 50% overlap on the segmented noisy speech x n (t) in frame n so as to generate a windowed frame x n ′ (t) with 2M samples as follows 
     
       
         
           
             
               
                 
                   
                     
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     The Fourier transform unit  120  applies a spectral transformation applies a discrete Fourier transform on the windowed frame x n ′(t) to generate X n (k), which can be thought of as a spectral representation of x n ′(t). Herein n and k refer to the analyzed frame and the frequency bin index respectively. In this example, the acoustic signal enhancement apparatus  100  applies noise suppression to only the spectral amplitude amp[X n (k)] of the noisy speech. The phase pha[X n (k)] of the noisy speech is directly used for the enhanced speech without being altered since the phase is trivial for speech quality and speech intelligibility. Herein the term amp[ . . . ] stands for an amplitude operator and the term pha[ . . . ] stands for a phase operator. 
     The noise estimation unit  130  estimates a noise spectrum λ n (k) for each of the spectral representation X n (k). There are many algorithms that can be applied by the noise estimation unit  130  to estimate the noise spectrum λ n (k). For example, the noise estimation unit  130  can obtain the noise spectrum λ n (k) by averaging the power spectrum of the noisy speech while only noise is included in the noisy speech. Reference [3] teaches another method for the noise estimation unit  130  to obtain the noise spectrum λ n (k). 
     Theoretically, the a posteriori SNR γ n (k) and the a priori SNR ξ n (k) are calculated by 
     
       
         
           
             
               
                 
                   
                     
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     where D n (k) and S n (k) are the discrete Fourier transform of d(t) and s(t) respectively. E{ . . . } stands for an expectation operator. Since E{amp[D n (k)] 2 } is not available, the estimated noise spectrum λ n (k) will be utilized to approximate E{amp[D n (k)] 2 }. Therefore, the a posteriori SNR estimation unit  140  can approximate the a posteriori SNR γ n (k) by γ n ′ (k) as
 
γ n ′( k )=amp[ X   n ( k )] 2 /λ n ( k )  (5)
 
     Having γ n ′ (k) for the current frame and γ n-1 ′ (k) for the previously frame, the a priori SNR estimation unit  150  approximates the a priori SNR ξ n (k) by ξ n ′(k) as
 
ξ n ′( k )=αγ n-1 ′( k ) G   n-1 ( k ) 2 +(1−α) P[γ   n ′( k )−1]  (6)
 
     where α is a forgetting factor satisfying 0&lt;α&lt;1, P[ . . . ] is a rectifying function, and G n-1 (k) is the spectral gain determined for the previously frame. 
     With already determined γ n ′ (k) and ξ n ′ (k), the spectral gain calculation unit  160  can obtain the spectral gain for the current frame by
 
 G   n ( k )={ξ n ′( k )+sqrt[ξ n ′( k ) 2 +2(1+ξ n ′( k ))(ξ n ′( k )/γ n ′( k ))]}/[2(1+ξ n ′( k ))]  (7)
 
     where sqrt[ . . . ] is a square root operator. 
     Next, the multiplication unit  170  multiplies the original spectral amplitude amp[X n (k)] by the spectral gain G n (k) to get the enhanced spectral amplitude G n (k)amp[X n (k)]. The enhanced spectral representation Y n (k) of the frame x n ′ (t) is constructed with enhanced spectral amplitude G n (k)amp[X n (k)] and the original phase pha[X n (t)] as: 
     
       
         
           
             
               
                 
                   
                     
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     where j=sqrt(−1). Then, the inverse Fourier transform unit  180  applies a discrete inverse Fourier transform on the enhanced spectral representation Y n (k) to get y n ′(t). Finally, the frame synthesis unit  190  obtains the enhanced speech y n (t) by performing an overlap-add processing as follows
 
 y   n ( t )= y   n-1 ′( t+M )+ y   n ′( t ),1 &lt;=t&lt;=M   (9)
 
     The acoustic signal enhancement apparatus  100  works fine only when the SNR of the noisy speech x(t) is sufficiently good. However, when the SNR of the noisy speech x(t) is poor, the acoustic signal enhancement apparatus  100  will overly suppress the actual speech information included in the noisy speech x(t). Musical noise that deteriorates the quality of the enhanced speech y n (t) will probably be generate as a side effect. In other words, the performance of the acoustic signal enhancement apparatus  100  of the related art is not sufficiently good for a wide range of SNR. 
     SUMMARY OF THE INVENTION 
     The embodiments disclose an acoustic signal enhancement method. The acoustic signal enhancement method comprises the steps of applying a spectral transformation on a frame derived from an input acoustic signal to generate a spectral representation of the frame, estimating an a posteriori signal-to-noise ratio (SNR) and an a priori SNR of the frame, determining an a priori SNR limit for the frame, limiting the a priori SNR with the a priori SNR limit to generate a final a priori SNR for the frame, determining a spectral gain for the frame according to the a posteriori SNR and the final a priori SNR, and applying the spectral gain on the spectral representation of the frame so as to generate an enhanced spectral representation of the frame. One of the characteristics of the acoustic signal enhancement method is that the a priori SNR limit is a function of frequency. 
     The embodiments disclose an acoustic signal enhancement method. The acoustic signal enhancement method comprises the steps of applying a spectral transformation on a frame derived from an input acoustic signal to generate a spectral representation of the frame, estimating an a posteriori signal-to-noise ratio (SNR) and an a priori SNR of the frame, determining a spectral gain for the frame according to the a posteriori SNR and the a priori SNR, determining a spectral gain limit for the frame, limiting the spectral gain with the spectral gain limit to generate a final spectral gain for the frame, and applying the final spectral gain on the spectral representation of the frame to generate an enhanced spectral representation of the frame. One of the characteristics of the acoustic signal enhancement method is that the a priori SNR limit is a function of frequency. 
     These and other objectives of the present invention will no doubt become obvious to those of ordinary skill in the art after reading the following detailed description of the preferred embodiment that is illustrated in the various figures and drawings. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  shows an acoustic signal enhancement apparatus of the related art. 
         FIG. 2  shows an acoustic signal enhancement apparatus according to a first embodiment. 
         FIG. 3  shows an acoustic signal enhancement apparatus according to a second embodiment. 
         FIG. 4  shows an acoustic signal enhancement apparatus according to a third embodiment. 
     
    
    
     DETAILED DESCRIPTION 
       FIG. 2  shows an acoustic signal enhancement apparatus  200  according to a first embodiment. Herein similar reference numerals are used for those components of the acoustic signal enhancement apparatus  200  that serve the same function as the corresponding components of the acoustic signal enhancement apparatus  100  of the related art. These functions have been previously described and will not be again elaborated on here. One of the major differences between the acoustic signal enhancement apparatus  200  and the acoustic signal enhancement apparatus  100  is that to prevent the actual speech information included in the noisy speech x(t) from being suppressed too much, the acoustic signal enhancement apparatus  200  of the first embodiment further comprises a perceptual limit module  251 . The perceptual limit module  251  utilizes an a priori SNR limit ξ n     —     lo (k) to restrict the a priori SNR ξ n ′(k) generated by the a priori SNR estimation unit  150 . Another different point is that the spectral gain calculation unit  160  calculates the spectral gain G n (k) for the current frame according to the final a priori SNR ξ n     —     final (k) generated by the perceptual limit module  251  rather than according to the a priori SNR ξ n ′(k). 
     The perceptual limit module  251  comprises an a priori SNR limit determine unit  252  and a limiter  253 . The a priori SNR limit determine unit  252  calculates an a priori SNR limit ξ n     —     lo (k), for k=1, k max . The limiter  253  then utilizes the a priori SNR limit ξ n     —     lo (k) as a low limit to restrict the a priori SNR so as to generate the final a priori SNR ξ n     —     final (k) as follows
 
ξ n     —     final ( k )=max[ξ n     —     lo ( k ),ξ n ′( k )], k= 1 , . . . , k   max   (10)
 
     There are many feasible ways that the a priori SNR limit determine unit  252  can utilize to calculates the a priori SNR limit ξ n     —     lo (k). Three of the feasible ways are illustrated herein after. 
     In a first feasible way for the a priori SNR limit determine unit  252  to calculate the a priori SNR limit ξ n     —     lo (k), the concept of auditory masking threshold (AMT) is utilized. Briefly speaking, the AMT defines a spectral amplitude threshold below which noise components are masked in the presence of the speech signal. Detailed derivation of the AMT can be found in many papers. For example, to derive the AMT, first a critical band analysis is performed to obtain energies in speech critical bands as follows 
     
       
         
           
             
               
                 
                   
                     
                       
                         
                           
                             
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     where b_high(i) and b_low(i) are the upper and lower limits of the i th  critical band respectively. Next, a spreading function S(i) is utilized to generate a spread critical band spectrum C(i) as follows
 
 C ( i )= S ( i )* B ( i )  (12)
 
     Then, the tonelike/noiselike nature of the spectrum should be determined. For example, a spectral flatness measure (SFM) can be utilized to determine the tonelike/noiselike nature of the spectrum as follows
 
SFM dB =10 log 10 ( G   m   /A   m )  (13)
 
α T =min[(SFM dB /SFM dB     —     max ),1]  (14)
 
     where G m  stands for the geometric mean of C(i), and A m  stands for the arithmetic mean of C(i). SFM dB     —     max  equals −60 dB for completely tonelike signal. When the spectrum is completely noiselike, SFM dB  equals 0 dB and α T  equals 0. An offset O(i) for the i th  critical band is then determined according to α T . For example, O(i) is given by
 
 O ( i )=α T (14.5+(1+α T )5.5  (15)
 
     Now the auditory masking threshold for a speech frame can be given by
 
 T ( i )=10 10log     10     [C(i)]−[O(i)/ 10]  (16)
 
     The auditory masking threshold T(i) still have to be transferred back to the bark domain through renormalization as follows
 
 T ′( i )=[ B ( i )/ C ( i )]× T ( i )  (17)
 
     Incorporating the renormalized AMT with the absolute threshold of hearing (ATH), the final AMT is generated as follows
 
 T   J ( m )=max{ T′[z ( f   s ( m/M ))], T   q ( f   s ( m/M ))  (18)
 
     where f s (m/M) is the central frequency of the m th  Fourier band and T q ( . . . ) is the absolute threshold of hearing. Putting the acquired AMT value onto the corresponding Fourier spectrum T J ′(k), the a priori SNR limit ξ n     —     lo (k) can finally be obtained through the following equations
 
 w   n ( k )=max{0,λ n ( k )− T   J ′( k )/ T   Jmax   },k= 1 , . . . , k   max   (19)
 
ξ n     —     lo ( k )= t   1   +t   2 ×exp[1 −w   n ( k )], k= 1 , . . . , k   max   (20)
 
     where t 1  and t 2  are two constant values that can be determined beforehand. In equation (19), T J ′(k)/T Jmax  can be thought of as a relative AMT of the frame, and w n (k) that equals either 0 or λ n (k)−T J ′(k)/T Jmax  can be thought of as a surplus noise spectrum of the frame. 
     In a second feasible way for the a priori SNR limit determine unit  252  to calculates the a priori SNR limit ξ n     —     lo (k), the similar AMT concept is applied. Briefly speaking, when the amplitude of a specific band of the speech signal become larger, the noise tolerance of the specific band also becomes better, and eliminating less noise can still generate acceptable speech quality. In addition, according to the estimated noise spectrum, more noise is eliminated on frequency band with relative large noise amplitude, while less noise is eliminated on frequency band with relative small noise amplitude. 
     A first function, which is a second order curve in this example, approximating a speech spectrum of the frame is given by
 
 v   n ( k )= c−b ( k−ind ) 2   ,k= 1 , . . . , k   max   (21)
 
     where c, b, and ind are three unknowns. Apparently, c corresponds to the largest v n (k) and ind corresponds to the frequency with the largest v n (k). Hence, ind could be determined as the frequency within a fix searching range that corresponds to the largest a posteriori SNR γ n ′ (k), as follows
 
ind=max_ind[γ n ′(mid_bin:high_bin)].  (22)
 
     wherein mid_bin and high_bin constitutes two boundaries of the aforementioned searching range. And c can be determined as an average SNR of several frequency bands near ind, therefore c is given by
 
 c =max{1, log [mean(γ n (ind− L :ind+ L ))]}  (23)
 
     where ind−L and ind+L define a frequency range for determining the aforementioned average SNR. Assume that v n (k) equals 0 when k equals 0, b can be determined by
 
 b=c /ind 2   (24)
 
     Next, according to the estimated noise spectrum λ n (k), a second function approximating a relative noise spectrum of the frame is given by
 
 w   n ( k )=min[ t   3 ,λ n ( k )/λ n     —     max ],  (25)
 
     Finally, the a priori SNR limit ξ n     —     lo (k) can be obtained through utilizing the following third function, which utilizes the outputs of the first and second function as its inputs, as follows
 
ξ n     —     lo ( k )= t   5 ×exp[1 −t   4   w   n ( k )]×exp[ v   n ( k )], k= 1 , . . . , k   max   (26)
 
     where t 3 , t 4 , and t 5  are three constant values that can be determined beforehand. 
     In a third feasible way, the a priori SNR limit determine unit  252  determines the a priori SNR limit ξ n     —     lo (k) by examining the characteristics of the frame x n ′(t). For example, the a priori SNR limit determine unit  252  can categorize the frame x n ′(t) into one of a plurality of speech classes by detecting the speech gender of the frame x n ′(t) or by applying a voice activity detection (VAD) on the frame x n ′(t). For each of the speech classes, the a priori SNR limit determine unit  252  has access to a predetermined a priori SNR limit ξ n     —     lo (k) corresponding to the speech class, as follows 
     
       
         
           
             
               
                 
                   
                     
                       
                         
                           
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     Please note that in the embodiment shown in  FIG. 2 , the a priori SNR limit ξ n     —     lo (k) adaptively generated by the a priori SNR limit determine unit  252  is a function of frequency. In other words, the a priori SNR limit is a frequency dependent value rather than being a single value for all the frequency bands. This ensures that the noise that contaminates the noisy speech x(t) will be suppressed adaptively. 
       FIG. 3  shows an acoustic signal enhancement apparatus  300  according to a second embodiment. Herein similar reference numerals are used for those components of the acoustic signal enhancement apparatus  300  that serve the same function as the corresponding components of the acoustic signal enhancement apparatus  100  of the related art. These functions have been previously described and will not be again elaborated on here. One of the different points between the acoustic signal enhancement apparatus  300  and the acoustic signal enhancement apparatus  100  is that to prevent the actual speech information included in the noisy speech x(t) from being suppressed too much, the acoustic signal enhancement apparatus  300  of the second embodiment further comprises a perceptual gain limiter  365  for limiting the spectral gain G n (k) by utilizing a gain limit G lim (k). Please note that the gain limit G lim (k) utilized by the perceptual gain limiter  365  is a function of frequency. In other words, the gain limit is a frequency dependent value rather than being a single value for all the frequency bands. Besides, in one example the a priori SNR estimation module  350  includes only the a priori SNR estimation unit  150  shown in  FIG. 1 . In another example, the a priori SNR estimation module  350  includes both the a priori SNR estimation unit  150  and the perceptual limit module  251  shown in  FIG. 2 , and the final a priori SNR ξ n     —     final (k) generated by the perceptual limit module  251  serves as the a priori SNR (k) generated by the a priori SNR estimation module  350 . 
     There are many feasible ways that the perceptual gain limiter  365  can utilize to calculates the gain limit G lim (k). In one of the feasible ways the concept of AMT is utilized. More specifically, the perceptual gain limiter  365  can first calculate the AMT with equations (11)˜(18). Then the perceptual gain limiter  365  calculates the gain limit G lim (k) according to the AMT and the estimated noise spectrum λ n (k) of the considered frame as follows
 
 G   lim ( k )=sqrt[ T   J ′( k )/λ n ( k )+ z],k= 1 , . . . , k   max   (28)
 
     where z is an adjustable parameter. The final gain G final (k) that is sent to the multiplication unit  170  is given by
 
 G   final ( k )=max[ G   lim ( k ), G   n ( k )], k= 1 , . . . , k   max   (29)
 
     Using the frequency dependent gain limit G lim (k) to limit the spectral gain G n (k) prevents the final gain G final (k) from being set too small. This ensures that the actual speech information included in the noisy speech x(t) will not be suppressed too much. 
       FIG. 4  shows an acoustic signal enhancement apparatus according to a third embodiment. Herein similar reference numerals are used for those components of the acoustic signal enhancement apparatus  400  that serve the same function as the corresponding components of the acoustic signal enhancement apparatus  100  of the related art. These functions have been previously described and will not be again elaborated on here. A different point between the acoustic signal enhancement apparatus  400  and the acoustic signal enhancement apparatus  100  is that to prevent the actual speech information included in the noisy speech x(t) from being suppressed too much, the acoustic signal enhancement apparatus  400  of the third embodiment further comprises a signal classifier  462  and an adaptive gain limiter  465 . The signal classifier  462  categorizes the frame x n ′(t) through examining the characteristics of the frame x n ′(t). For example, the signal classifier  462  categorize the frame x n ′(t) into one of a plurality of speech classes by detecting the speech gender of frame x n ′(t) or by applying a voice activity detection (VAD) on the frame x n ′(t). For each of the speech classes, the adaptive gain limiter  465  has access to a predetermined gain limit G lim (k) corresponding to the speech class, as follows 
     
       
         
           
             
               
                 
                   
                     
                       
                         
                           
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     The adaptive gain limiter  465  then utilizes the gain limit G limit (k) as a lower limit to restrict the spectral gain G n (k) so as to generate a final gain G final (k) that will then be sent to the multiplication unit  170 , as follows
 
 G   final ( k )=max[ G   lim ( k ), G   n ( k )], k= 1 , . . . , k   max   (31)
 
     Using the frequency dependent gain limit G lim (k) to limit the spectral gain G n (k) prevents the final gain G final (k) from being set too small. This ensures that the actual speech information included in the noisy speech x(t) will not be suppressed too much. 
     Those skilled in the art will readily observe that numerous modifications and alterations of the device and method may be made while retaining the teachings of the invention. Accordingly, the above disclosure should be construed as limited only by the metes and bounds of the appended claims.