Abstract:
A Voice Activity Detection (VAD) algorithm provides a simple binary signal indicating the presence or absence of speech in a microphone signal. The VAD algorithm includes a first step of noise suppression which both estimates and removes (i.e., filters) ambient noise from the microphone signal to create a filtered signal. The magnitude of the filtered signal is then compared to a threshold in order to produce a VAD output signal. The threshold is dynamic and may be derived either from the filtered signal itself, or from a noise spectrum estimate calculated by the noise suppression step.

Description:
BACKGROUND OF THE INVENTION 
     The present invention relates to processing microphone signals and in particular to determining when a microphone signal includes human speech. 
     Various types of processing are performed on microphone signals for different purposes. For example, there may be a desire to cut off a microphone signal being recorded when there is no voice content for recording economy. There also are instances where it is desirable to distinguish between background noise and a speaker&#39;s voice. Known methods fail to adequately identify the presence of voice content in a microphone signal in these and other instances. 
     BRIEF SUMMARY OF THE INVENTION 
     The present invention addresses the above and other needs by providing a Voice Activity Detection (VAD) algorithm which provides a simple binary signal indicating the presence or absence of speech in a microphone signal. The VAD algorithm includes a first step of noise suppression which both estimates and removes (i.e., filters) ambient noise from the microphone signal to create a filtered signal. The magnitude of the filtered signal is then compared to a threshold in order to produce a VAD output signal. The threshold is dynamic and may be derived either from the filtered signal itself, or from a noise spectrum estimate calculated by the noise suppression step. 
     In accordance with one aspect of the invention, there is provided a first step of noise suppression. A microphone signal is processed to estimate the noise spectrum and then the noise spectrum is subtracted from the total signal spectrum to suppress noise to provide an estimate of voice content. In a second step, the voice content estimate in compared to a threshold to provide voice activity detection. 
     In accordance with another aspect of the invention, there are provided two methods for determining the threshold. A first method uses a noise floor spectral density computed in the noise suppression step to compute the threshold and a second method uses a smoothed estimate of noise only obtained during non-speech periods to compute a threshold. 
     In accordance with yet another aspect of the invention, there is provided an initialization period. During the initialization period the VAD is set to 0 (non-speech) and an initial threshold is computed for subsequent processing. 
    
    
     
       BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING 
       The above and other aspects, features and advantages of the present invention will be more apparent from the following more particular description thereof, presented in conjunction with the following drawings wherein: 
         FIG. 1  shows a first embodiment of a Voice Activity Detection (VAD) method using a threshold based on noise floor spectral density according to the present invention. 
         FIG. 2  shows a second embodiment of a VAD method using a threshold based on a smoothed estimate of noise only obtained during non-speech periods, according to the present invention. 
         FIG. 3  shows results of the threshold based on noise floor spectral density applied to female speech in the presence of street noise. 
         FIG. 4  shows results of the threshold based on noise floor spectral density applied to female speech in the presence of silence. 
         FIG. 5  shows results of the threshold based on non-speech periods applied to female speech in the presence of street noise. 
         FIG. 6  shows results of the threshold based on non-speech periods applied to female speech in the presence of silence. 
     
    
    
     Corresponding reference characters indicate corresponding components throughout the several views of the drawings. 
     DETAILED DESCRIPTION OF THE INVENTION 
     The following description is of the best mode presently contemplated for carrying out the invention. This description is not to be taken in a limiting sense, but is made merely for the purpose of describing one or more preferred embodiments of the invention. The scope of the invention should be determined with reference to the claims. 
     A first embodiment of a Voice Activity Detection (VAD) method  10   a  using a threshold based on noise floor spectral density, according to the present invention is shown in  FIG. 1 . A microphone  12  produces a microphone signal  14  provided to noise suppression processing  16 . The noise suppression processing  16  processes the microphone signal and generates ambient noise estimate frames  18  and a noise suppressed (i.e., ambient noise removed) signal  54 . The ambient noise estimate frames  18  are provided to a threshold calculation  20  which calculates a threshold  22  provided to VAD logic  24   a . The noise suppressed signal  54  is also provided to the VAD logic  24   a . The VAD logic  24   a  compares the noise suppressed signal  54  to the threshold  22  and sets a VAD signal  28  to “0” when a voice is not present in the microphone signal  14 , and sets a VAD signal  28  to “1” when a voice is present in the microphone signal  14 . 
     The noise suppression processing  16  receives the microphone signal  14  and processes the microphone signal  14  in buffering/windowing  30  forming time domain frames  32 . The time domain frames  32  are processed by FFT  34  generating microphone signal FFT frames  36 . The microphone signal FFT frames  36  are processed sequentially by noise estimation  38  to generate the ambient noise estimate frames  18 . The noise estimation  38  preferably uses the noise power spectral density estimation technique presented by Rainer Martin in “Noise Power Spectral Density Estimation Based on Optimal Smoothing and Minimum Statistics” IEEE Trans. Speech and Audio Processing, 9(5):504-512, July 2001. On a bin by bin bases, the technique keeps a running update of detected minima, incorporating minimum statistics in the final estimate in order to avoid underestimating the noise level. The noise estimation  38  preferably operates in the power domain. 
     The ambient noise estimate frames  18  are processed by trim calculation  40  to provide trims  42 . The trims  42  are each applied to the corresponding bins of the FFT frames  36  to obtain filtered FFT frames  46 , thus implementing spectral subtraction through bin scaling. The trims  42  are preferably calculated in trim calculation  40  according to a method set forth by Berouti in “Enhancement of Speech Corrupted by Acoustic Noise” Proc. IEEE ICASSP, 1979, 4, 208-211. Berouti&#39;s method is based upon spectral subtraction, where an estimated noise spectrum is subtracted from the spectrum of a corrupted signal containing content as well as noise. Berouti improved on this method by introducing an over subtraction factor, along with a noise floor factor where a scaled version of the noise estimate is subtracted from the corrupted signal, on a bin by bin basis. If the resulting bin value falls below the noise floor value, the bin is simply scaled by the noise floor value, otherwise, the original result of subtracting the scaled version of the noise estimate is kept. The subtraction factor is further altered to vary with estimated signal to noise ratio. The over subtraction factor sets an upper limit for the scale factor and governs changes in the scale factor with signal to noise ratio. The spectral subtraction is performed by multiplication of the trims  42  times the corresponding bins of the FFT frames  36 , thus scaling the FFT frames  36  to values equivalent to those obtained by subtraction. 
     The filtered FFT frames  46  are processed by IFFT  48  to generate filtered time domain frames  50 . The filtered time domain frames  52  are processed by buffering/windowing  52  to generate a filtered time domain (i.e., the noise suppressed signal) sequence  54 . The buffering/windowing  52  includes overlapping and adding consecutive frames. 
     The ambient noise estimate frames  18  are preferably updated at between five and 64 ms and more preferably every 64 ms, and the scale factor is preferably set to between three and 100 and more preferably to about 100, meaning that noise peaks up to about 20 dB above the noise estimate will be attenuated. The suppression effectively isolates and emphasizes speech components so that they can be more consistently identified with an overall threshold. 
     The ambient noise estimate frames  18  are also processed by threshold calculation  20 , taking the square root  56  and the mean  58  of each FFT bin to generate a threshold  22 . The noise suppressed signal  54  is compared to the threshold  22 , and when the noise suppressed signal  54  exceeds the threshold  22 , the VAD signal is set to “1” for voice present and when the noise suppressed signal  54  does not exceed the threshold  22 , the VAD signal is set to “0” for voice absent. 
     A second embodiment of a Voice Activity Detection (VAD) method  10   b  using a threshold based on non-speech periods, according to the present invention is shown in  FIG. 2 . The second method  10   b  includes the noise suppression  16  described for the first method above. The second method  10   b  departs from the first method  10   a  in not using the ambient noise estimate frames  18  to determine a threshold and instead determines the threshold from the noise suppressed signal  54 . The noise suppressed signal  54  is processed by square magnitude  60  to generate a signal energy estimate  62 . The signal energy estimate  62  is provided to VAD logic and threshold determination  24   b . 
     In contrast to the first method, the VAD decision threshold is determined by the VAD logic and threshold determination  24   b  in terms of the signal energy estimate  62 . During non-speech frames, a first order smoother with attack and release time constants is applied to the signal energy estimate  62  in the time domain. The final value of the smoothed output in the most recent non-speech frame, scaled by an appropriate scalar constant, serves as the decision threshold. Typical values of this scalar have been roughly 256, meaning the energy must exceed the calculated noise floor by about 24 dB. 
     In order to avoid overly sensitive or sporadic behavior, each of the threshold determination methods described in  FIGS. 1 and 2  require an additional stage of logic. During an initialization period, the VAD signal  28  is held to 0 (non-speech), regardless of the microphone signal  14 . Following the initialization period P, each point in the noise suppressed signal  54  of the noise suppression processing  16  is compared to the threshold. If any one value in a given frame exceeds the threshold, the VAD signal  28  for that frame is 1 (speech). In order for the output to return to 0, the VAD logic requires a number N 1  of consecutive frames with no value exceeding the threshold, N 1  is preferably about 16. A preferred initialization period P is preferably about 30 frames. 
       FIGS. 3-6  show examples of the present invention applied to female speech. Results of the threshold based on noise floor spectral density applied to female speech in the presence of street noise are shown in  FIG. 3 . Results of the threshold based on noise floor spectral density applied to female speech in the presence of silence are shown in  FIG. 4 . Results of the threshold based on non-speech periods applied to female speech in the presence of street noise are shown in  FIG. 5 . Results of the threshold based on non-speech periods applied to female speech in the presence of silence are shown in  FIG. 6 . 
     While the invention herein disclosed has been described by means of specific embodiments and applications thereof, numerous modifications and variations could be made thereto by those skilled in the art without departing from the scope of the invention set forth in the claims.