Method and device for speech enhancement in the presence of background noise

In one aspect thereof the invention provides a method for noise suppression of a speech signal that includes, for a speech signal having a frequency domain representation dividable into a plurality of frequency bins, determining a value of a scaling gain for at least some of said frequency bins and calculating smoothed scaling gain values. Calculating smoothed scaling gain values includes, for the at least some of the frequency bins, combining a currently determined value of the scaling gain and a previously determined value of the smoothed scaling gain. In another aspect a method partitions the plurality of frequency bins into a first set of contiguous frequency bins and a second set of contiguous frequency bins having a boundary frequency there between, where the boundary frequency differentiates between noise suppression techniques, and changes a value of the boundary frequency as a function of the spectral content of the speech signal.

FIELD OF THE INVENTION

The present invention relates to a technique for enhancing speech signals to improve communication in the presence of background noise. In particular but not exclusively, the present invention relates to the design of a noise reduction system that reduces the level of background noise in the speech signal.

BACKGROUND OF THE INVENTION

Reducing the level of background noise is very important in many communication systems. For example, mobile phones are used in many environments where high level of background noise is present. Such environments are usage in cars (which is increasingly becoming hands-free), or in the street, whereby the communication system needs to operate in the presence of high levels of car noise or street noise. In office applications, such as video-conferencing and hands-free internet applications, the system needs to efficiently cope with office noise. Other types of ambient noises can be also experienced in practice. Noise reduction, also known as noise suppression, or speech enhancement, becomes important for these applications, often needed to operate at low signal-to-noise ratios (SNR). Noise reduction is also important in automatic speech recognition systems which are increasingly employed in a variety of real environments. Noise reduction improves the performance of the speech coding algorithms or the speech recognition algorithms usually used in above-mentioned applications.

Spectral subtraction is one the mostly used techniques for noise reduction (see S. F. Boll, “Suppression of acoustic noise in speech using spectral subtraction,”IEEE Trans. Acoust., Speech, Signal Processing, vol. ASSP-27, pp. 113-120, April 1979). Spectral subtraction attempts to estimate the short-time spectral magnitude of speech by subtracting a noise estimation from the noisy speech. The phase of the noisy speech is not processed, based on the assumption that phase distortion is not perceived by the human ear. In practice, spectral subtraction is implemented by forming an SNR-based gain function from the estimates of the noise spectrum and the noisy speech spectrum. This gain function is multiplied by the input spectrum to suppress frequency components with low SNR. The main disadvantage using conventional spectral subtraction algorithms is the resulting musical residual noise consisting of “musical tones” disturbing to the listener as well as the subsequent signal processing algorithms (such as speech coding). The musical tones are mainly due to variance in the spectrum estimates. To solve this problem, spectral smoothing has been suggested, resulting in reduced variance and resolution. Another known method to reduce the musical tones is to use an over-subtraction factor in combination with a spectral floor (see M. Berouti, R. Schwartz, and J. Makhoul, “Enhancement of speech corrupted by acoustic noise,” inProc. IEEE ICASSP, Washington, D.C., April 1979, pp. 208-211). This method has the disadvantage of degrading the speech when musical tones are sufficiently reduced. Other approaches are soft-decision noise suppression filtering (see R. J. McAulay and M. L. Malpass, “Speech enhancement using a soft decision noise suppression filter,”IEEE Trans. Acoust., Speech, Signal Processing, vol. ASSP-28, pp. 137-145, April 1980) and nonlinear spectral subtraction (see P. Lockwood and J. Boudy, “Experiments with a nonlinear spectral subtractor (NSS), hidden Markov models and projection, for robust recognition in cars,”Speech Commun., vol. 11, pp. 215-228, June 1992).

SUMMARY OF THE INVENTION

In one aspect thereof this invention provides a method for noise suppression of a speech signal that includes, for a speech signal having a frequency domain representation dividable into a plurality of frequency bins, determining a value of a scaling gain for at least some of said frequency bins and calculating smoothed scaling gain values. Calculating smoothed scaling gain values comprises, for the at least some of the frequency bins, combining a currently determined value of the scaling gain and a previously determined value of the smoothed scaling gain.

In another aspect thereof this invention provides a method for noise suppression of a speech signal that includes, for a speech signal having a frequency domain representation dividable into a plurality of frequency bins, partitioning the plurality of frequency bins into a first set of contiguous frequency bins and a second set of contiguous frequency bins having a boundary frequency there between, where the boundary frequency differentiates between noise suppression techniques, and changing a value of the boundary frequency as a function of the spectral content of the speech signal.

In a further aspect thereof this invention provides a speech encoder that comprises a noise suppressor for a speech signal having a frequency domain representation dividable into a plurality of frequency bins. The noise suppressor is operable to determine a value of a scaling gain for at least some of the frequency bins and to calculate smoothed scaling gain values for the at least some of the frequency bins by combining a currently determined value of the scaling gain and a previously determined value of the smoothed scaling gain.

In a still further aspect thereof this invention provides a speech encoder that comprises a noise suppressor for a speech signal having a frequency domain representation dividable into a plurality of frequency bins. The noise suppressor is operable to partition the plurality of frequency bins into a first set of contiguous frequency bins and a second set of contiguous frequency bins having a boundary frequency there between. The boundary frequency differentiates between noise suppression techniques. The noise suppressor is further operable to change a value of the boundary frequency as a function of the spectral content of the speech signal.

In another aspect thereof this invention provides a computer program embodied on a computer readable medium that comprises program instructions for performing noise suppression of a speech signal comprising operations of, for a speech signal for a speech signal having a frequency domain representation dividable into a plurality of frequency bins, determining a value of a scaling gain for at least some of said frequency bins and calculating smoothed scaling gain values, comprising for said at least some of said frequency bins combining a currently determined value of the scaling gain and a previously determined value of the smoothed scaling gain.

In another aspect thereof this invention provides a computer program embodied on a computer readable medium that comprises program instructions for performing noise suppression of a speech signal comprising operations of, for a speech signal for a speech signal having a frequency domain representation dividable into a plurality of frequency bins, partitioning the plurality of frequency bins into a first set of contiguous frequency bins and a second set of contiguous frequency bins having a boundary frequency there between and changing a value of the boundary frequency as a function of the spectral content of the speech signal.

In a still further and certainly non-limiting aspect thereof this invention provides a speech encoder that includes means for suppressing noise in a speech signal having a frequency domain representation dividable into a plurality of frequency bins. The noise suppressing means comprises means for partitioning the plurality of frequency bins into a first set of contiguous frequency bins and a second set of contiguous frequency bins having a boundary there between, and for changing the boundary as a function of the spectral content of the speech signal. The noise suppressing means further comprises means for determining a value of a scaling gain for at least some of the frequency bins and for calculating smoothed scaling gain values for the at least some of the frequency bins by combining a currently determined value of the scaling gain and a previously determined value of the smoothed scaling gain. Calculating a smoothed scaling gain value preferably uses a smoothing factor having a value determined so that smoothing is stronger for smaller values of scaling gain. The noise suppressing means further comprises means for determining a value of a scaling gain for at least some frequency bands, where a frequency band comprises at least two frequency bins, and for calculating smoothed frequency band scaling gain values. The noise suppressing means further comprises means for scaling a frequency spectrum of the speech signal using the smoothed scaling gains, where for frequencies less than the boundary the scaling is performed on a per frequency bin basis, and for frequencies above the boundary the scaling is performed on a per frequency band basis.

DETAILED DESCRIPTION OF THE ILLUSTRATIVE EMBODIMENTS

In the present specification, efficient techniques for noise reduction are disclosed. The techniques are based at least in part on dividing the amplitude spectrum in critical bands and computing a gain function based on SNR per critical band similar to the approach used in the EVRC speech codec (see 3GPP2 C.S0014-0 “Enhanced Variable Rate Codec (EVRC) Service Option for Wideband Spread Spectrum Communication Systems”, 3GPP2 Technical Specification, December 1999). For example, features are disclosed which use different processing techniques based on the nature of the speech frame being processed. In unvoiced frames, per band processing is used in the whole spectrum. In frames where voicing is detected up to a certain frequency, per bin processing is used in the lower portion of the spectrum where voicing is detected and per band processing is used in the remaining bands. In case of background noise frames, a constant noise floor is removed by using the same scaling gain in the whole spectrum. Further, a technique is disclosed in which the smoothing of the scaling gain in each band or frequency bin is performed using a smoothing factor which is inversely related to the actual scaling gain (smoothing is stronger for smaller gains). This approach prevents distortion in high SNR speech segments preceded by low SNR frames, as it is the case for voiced onsets for example.

One non-limiting aspect of this invention is to provide novel methods for noise reduction based on spectral subtraction techniques, whereby the noise reduction method depends on the nature of the speech frame being processed. For example, in voiced frames, the processing may be performed on per bin basis below a certain frequency.

In an illustrative embodiment, noise reduction is performed within a speech encoding system to reduce the level of background noise in the speech signal before encoding. The disclosed techniques can be deployed with either narrowband speech signals sampled at 8000 sample/s or wideband speech signals sampled at 16000 sample/s, or at any other sampling frequency. The encoder used in this illustrative embodiment is based on AMR-WB codec (see S. F. Boll, “Suppression of acoustic noise in speech using spectral subtraction,”IEEE Trans. Acoust., Speech, Signal Processing, vol. ASSP-27, pp. 113-120, April 1979), which uses an internal sampling conversion to convert the signal sampling frequency to 12800 sample/s (operating on a 6.4 kHz bandwidth).

Thus the disclose noise reduction technique in this illustrative embodiment operates on either narrowband or wideband signals after sampling conversion to 12.8 kHz.

In case of wideband inputs, the input signal has to be decimated from 16 kHz to 12.8 kHz. The decimation is performed by first upsampling by 4, then filtering the output through lowpass FIR filter that has the cut off frequency at 6.4 kHz. Then, the signal is downsampled by 5. The filtering delay is 15 samples at 16 kHz sampling frequency.

In case of narrow-band inputs, the signal has to be upsampled from 8 kHz to 12.8 kHz. This is performed by first upsampling by 8, then filtering the output through lowpass FIR filter that has the cut off frequency at 6.4 kHz. Then, the signal is downsampled by 5. The filtering delay is 8 samples at 8 kHz sampling frequency.

After the sampling conversion, two preprocessing functions are applied to the signal prior to the encoding process: high-pass filtering and pre-emphasizing.

The high-pass filter serves as a precaution against undesired low frequency components. In this illustrative embodiment, a filter at a cut off frequency of 50 Hz is used, and it is given by

In the pre-emphasis, a first order high-pass filter is used to emphasize higher frequencies, and it is given by
Hpre-emph(z)=1−0.68z−1

Preemphasis is used in AMR-WB codec to improve the codec performance at high frequencies and improve perceptual weighting in the error minimization process used in the encoder.

In the rest of this illustrative embodiment the signal at the input of the noise reduction algorithm is converted to 12.8 kHz sampling frequency and preprocessed as described above. However, the disclosed techniques can be equally applied to signals at other sampling frequencies such as 8 kHz or 16 kHz with and without preprocessing.

In the following, the noise reduction algorithm will be described in details. The speech encoder in which the noise reduction algorithm is used operates on 20 ms frames containing 256 samples at 12.8 kHz sampling frequency. Further, the coder uses 13 ms lookahead from the future frame in its analysis. The noise reduction follows the same framing structure. However, some shift can be introduced between the encoder framing and the noise reduction framing to maximize the use of the lookahead. In this description, the indices of samples will reflect the noise reduction framing.

FIG. 1shows an overview of a speech communication system including noise reduction. In block101, preprocessing is performed as the illustrative example described above.

In block102, spectral analysis and voice activity detection (VAD) are performed. Two spectral analysis are performed in each frame using 20 ms windows with 50% overlap. In block103, noise reduction is applied to the spectral parameters and then inverse DFT is used to convert the enhanced signal back to the time domain. Overlap-add operation is then used to reconstruct the signal.

In block104, linear prediction (LP) analysis and open-loop pitch analysis are performed (usually as a part of the speech coding algorithm). In this illustrative embodiment, the parameters resulting from block104are used in the decision to update the noise estimates in the critical bands (block105). The VAD decision can be also used as the noise update decision. The noise energy estimates updated in block105are used in the next frame in the noise reduction block103to computes the scaling gains. Block106performs speech encoding on the enhanced speech signal. In other applications, block106can be an automatic speech recognition system. Note that the functions in block104can be an integral part of the speech encoding algorithm.

Spectral Analysis

The discrete Fourier Transform is used to perform the spectral analysis and spectrum energy estimation. The frequency analysis is done twice per frame using 256-points Fast Fourier Transform (FFT) with a 50 percent overlap (as illustrated inFIG. 2). The analysis windows are placed so that all look ahead is exploited. The beginning of the first window is placed 24 samples after the beginning of the speech encoder current frame. The second window is placed 128 samples further. A square root of a Hanning window (which is equivalent to a sine window) has been used to weight the input signal for the frequency analysis. This window is particularly well suited for overlap-add methods (thus this particular spectral analysis is used in the noise suppression algorithm based on spectral subtraction and overlap-add analysis/synthesis). The square root Hanning window is given by

wFFT⁡(n)=0.5-0.5⁢⁢cos⁡(2⁢π⁢⁢nLFFT)=sin⁡(π⁢⁢nLFFT),⁢n=0,…⁢,LFFT-1(1)
where LFFT=256 is the size of FTT analysis. Note that only half the window is computed and stored since it is symmetric (from 0 to LFFT/2).

Let s′(n) denote the signal with index 0 corresponding to the first sample in the noise reduction frame (in this illustrative embodiment, it is 24 samples more than the beginning of the speech encoder frame). The windowed signal for both spectral analysis are obtained as
xw(1)(n)=wFFT(n)s′(n),n=0, . . . , LFFT−1
xw(2)(n)=wFFT(n)s′(n+LFFT/2),n=0, . . . , LFFT−1
where s′(0) is the first sample in the present noise reduction frame.

FFT is performed on both windowed signals to obtain two sets of spectral parameters per frame:

The output of the FFT gives the real and imaginary parts of the spectrum denoted by XR(k), k=0 to 128, and XI(k), k=1 to 127. Note that XR(0) corresponds to the spectrum at 0 Hz (DC) and XR(128) corresponds to the spectrum at 6400 Hz. The spectrum at these points is only real valued and usually ignored in the subsequent analysis.

After FFT analysis, the resulting spectrum is divided into critical bands using the intervals having the following upper limits (20 bands in the frequency range 0-6400 Hz):

The average energy in a critical band is computed as

ECB⁡(i)=1(LFFT/2)2⁢MCB⁡(i)⁢∑k=0MCB⁡(i)-1⁢(XR2⁡(k+ji)+XI2⁡(k+ji)),⁢i=0,…⁢,19,(2)
where XR(k) and XI(k) are, respectively, the real and imaginary parts of the kth frequency bin and jiis the index of the first bin in the ith critical band given by ji={1, 3, 5, 7, 9, 11, 13, 16, 19, 22, 26, 30, 35, 41, 47, 55, 64, 75, 89, 107}.

The spectral analysis module also computes the energy per frequency bin, EBIN(k), for the first 17 critical bands (74 bins excluding the DC component)
EBIN(k)=XR2(k)+XI2(k),k=0, . . . , 73  (3)

Finally, the spectral analysis module computes the average total energy for both FTT analyses in a 20 ms frame by adding the average critical band energies ECB. That is, the spectrum energy for a certain spectral analysis is computed as

Eframe=∑i=019⁢ECB⁡(i)(4)
and the total frame energy is computed as the average of spectrum energies of both spectral analysis in a frame. That is
Et=10 log(0.5(Eframe(0)+Eframe(1)),dB(5)

The output parameters of the spectral analysis module, that is average energy per critical band, the energy per frequency bin, and the total energy, are used in VAD, noise reduction, and rate selection modules.

Note that for narrow-band inputs sampled at 8000 sample/s, after sampling conversion to 12800 sample/s, there is no content at both ends of the spectrum, thus the first lower frequency critical band as well as the last three high frequency bands are not considered in the computation of output parameters (only bands from i=1 to 16 are considered).

Voice Activity Detection

The spectral analysis described above is performed twice per frame. Let ECB(1)(i) and ECB(2)(i) denote the energy per critical band information for the first and second spectral analysis, respectively (as computed in Equation (2)). The average energy per critical band for the whole frame and part of the previous frame is computed as
Eav(i)=0.2ECB(0)(i)+0.4ECB(1)(i)+0.4ECB(2)(i)  (6)
where ECB(0)(i) denote the energy per critical band information from the second analysis of the previous frame. The signal-to-noise ratio (SNR) per critical band is then computed as
SNRCB(i)=Eav(i)/NCB(i) bounded by SNRCB≧1.  (7)
where NCB(i) is the estimated noise energy per critical band as will be explained in the next section. The average SNR per frame is then computed as

SNRav=10⁢log⁡(∑i=bminbmax⁢SNRCB⁡(i)),(8)
where bmin=0 and bmax=19 in case of wideband signals, and bmin=1 and bmax=16 in case of narrowband signals.

The voice activity is detected by comparing the average SNR per frame to a certain threshold which is a function of the long-term SNR. The long-term SNR is given by
SNRLT=Ēf−Nf(9)
where ĒfandNfare computed using equations (12) and (13), respectively, which will be described later. The initial value of Ēfis 45 dB.

The threshold is a piece-wise linear function of the long-term SNR. Two functions are used, one for clean speech and one for noisy speech.

Further, a hysteresis in the VAD decision is added to prevent frequent switching at the end of an active speech period. It is applied in case the frame is in a soft hangover period or if the last frame is an active speech frame. The soft hangover period consists of the first 10 frames after each active speech burst longer than 2 consecutive frames. In case of noisy speech (SNRLT<35) the hysteresis decreases the VAD decision threshold by
thVAD=0.95thVAD

In case of clean speech the hysteresis decreases the VAD decision threshold by
thVAD=thVAD−11

If the average SNR per frame is larger than the VAD decision threshold, that is, if SNRav>thVAD, then the frame is declared as an active speech frame and the VAD flag and a local VAD flag are set to 1. Otherwise the VAD flag and the local VAD flag are set to 0. However, in case of noisy speech, the VAD flag is forced to 1 in hard hangover frames, i.e. one or two inactive frames following a speech period longer than 2 consecutive frames (the local VAD flag is then equal to 0 but the VAD flag is forced to 1).

First Level of Noise Estimation and Update

In this section, the total noise energy, relative frame energy, update of long-term average noise energy and long-term average frame energy, average energy per critical band, and a noise correction factor are computed. Further, noise energy initialization and update downwards are given.

The total noise energy per frame is given by

Ntot=10⁢log⁡(∑i=019⁢NCB⁡(i))(10)
where NCB(i) is the estimated noise energy per critical band.

The relative energy of the frame is given by the difference between the frame energy in dB and the long-term average energy. The relative frame energy is given by
Erel=Et−Ēf(11)
where Et, is given in Equation (5).

The long-term average noise energy or the long-term average frame energy are updated in every frame. In case of active speech frames (VAD flag=1), the long-term average frame energy is updated using the relation
Ēf=0.99Ēf+0.01Et(12)
with initial value Ēf=45 dB.

In case of inactive speech frames (VAD flag=0), the long-term average noise energy is updated by
Nf=0.99Nf+0.01Ntot(13)

The initial value ofNfis set equal to Ntotfor the first 4 frames. Further, in the first 4 frames, the value of Ēfis bounded by Ēf≧Ntot+10.

Frame Energy per Critical Band, Noise Initialization, and Noise Update Downward:

The frame energy per critical band for the whole frame is computed by averaging the energies from both spectral analyses in the frame. That is,
ĒCB(i)=0.5ECB(1)(i)+0.5ECB(2)(i)  (14)

The noise energy per critical band NCB(i) is initially initialized to 0.03. However, in the first 5 subframes, if the signal energy is not too high or if the signal doesn't have strong high frequency components, then the noise energy is initialized using the energy per critical band so that the noise reduction algorithm can be efficient from the very beginning of the processing. Two high frequency ratios are computed: r15,16is the ratio between the average energy of critical bands 15 and 16 and the average energy in the first 10 bands (mean of both spectral analyses), and r18,19is the same but for bands 18 and 19.

In the first 5 frames, if Et<49 and r15,16<2 and r18,19<1.5 then for the first 3 frames,
NCB(i)=ĒCB(i),i=0, . . . ,19  (15)
and for the following two frames NCB(i) is updated by
NCB(i)=0.33NCB(i)+0.66ĒCB(i),i=0, . . . ,19  (16)

For the following frames, at this stage, only noise energy update downward is performed for the critical bands whereby the energy is less than the background noise energy. First, the temporary updated noise energy is computed as
Ntmp(i)=0.9NCB(i)+0.1(0.25ECB(0)(i)+0.75ĒCB(i))  (17)
where ECB(0)(i) correspond to the second spectral analysis from previous frame.

Then for i=0 to 19, if Ntmp(i)<NCB(i) then NCB(i)=Ntmp(i).

A second level of noise update is performed later by setting NCB(i)=Ntmp(i) if the frame is declared as inactive frame. The reason for fragmenting the noise energy update into two parts is that the noise update can be executed only during inactive speech frames and all the parameters necessary for the speech activity decision are hence needed. These parameters are however dependent on LP prediction analysis and open-loop pitch analysis, executed on denoised speech signal. For the noise reduction algorithm to have as accurate noise estimate as possible, the noise estimation update is thus updated downwards before the noise reduction execution and upwards later on if the frame is inactive. The noise update downwards is safe and can be done independently of the speech activity.

Noise reduction is applied on the signal domain and denoised signal is then reconstructed using overlap and add. The reduction is performed by scaling the spectrum in each critical band with a scaling gain limited between gminand 1 and derived from the signal-to-noise ratio (SNR) in that critical band. A new feature in the noise suppression is that for frequencies lower than a certain frequency related to the signal voicing, the processing is performed on frequency bin basis and not on critical band basis. Thus, a scaling gain is applied on every frequency bin derived from the SNR in that bin (the SNR is computed using the bin energy divided by the noise energy of the critical band including that bin). This new feature allows for preserving the energy at frequencies near to harmonics preventing distortion while strongly reducing the noise between the harmonics. This feature can be exploited only for voiced signals and, given the frequency resolution of the frequency analysis used, for signals with relatively short pitch period. However, these are precisely the signals where the noise between harmonics is most perceptible.

FIG. 3shows an overview of the disclosed procedure. In block301, spectral analysis is performed. Block302verifies if the number of voiced critical bands is larger than 0. If this is the case then noise reduction is performed in block304where per bin processing is performed in the first voiced K bands and per band processing is performed in the remaining bands. If K=0 then per band processing is applied to all the critical bands. After noise reduction on the spectrum, block305performs inverse DFT analysis and overlap-add operation is used to reconstruct the enhanced speech signal as will be described later.

The minimum scaling gain gminis derived from the maximum allowed noise reduction in dB, NRmax. The maximum allowed reduction has a default value of 14 dB. Thus minimum scaling gain is given by
gmin=10−NRmax/20(18)
and it is equal to 0.19953 for the default value of 14 dB.

In case of inactive frames with VAD=0, the same scaling is applied over the whole spectrum and is given by gs=0.9gminif noise suppression is activated (if gminis lower than 1). That is, the scaled real and imaginary components of the spectrum are given by
X′R(k)=gsXR(k),k=1, . . . ,128, andX′I(k)=gsXI(k),k=1, . . . ,127.  (19)

Note that for narrowband inputs, the upper limits in Equation (19) are set to 79 (up to 3950 Hz).

For active frames, the scaling gain is computed related to the SNR per critical band or per bin for the first voiced bands. If KVOIC>0 then per bin noise suppression is performed on the first KVOICbands. Per band noise suppression is used on the rest of the bands. In case KVOIC=0 per band noise suppression is used on the whole spectrum. The value of KVOICis updated as will be described later. The maximum value of KVOICis 17, therefore per bin processing can be applied only on the first 17 critical bands corresponding to a maximum frequency of 3700 Hz. The maximum number of bins for which per bin processing can be used is 74 (the number of bins in the first 17 bands). An exception is made for hard hangover frames that will be described later in this section.

In an alternative implementation, the value of KVOICmay be fixed. In this case, in all types of speech frames, per bin processing is performed up to a certain band and the per band processing is applied to the other bands.

The scaling gain in a certain critical band, or for a certain frequency bin, is computed as a function of SNR and given by
(gs)2=ksSNR+cs, bounded bygmin≦gs≦1  (20)

The values of ksand csare determined such as gs=gminfor SNR=1, and gs=1 for SNR=45. That is, for SNRs at 1 dB and lower, the scaling is limited to gsand for SNRs at 45 dB and higher, no noise suppression is performed in the given critical band (gs=1). Thus, given these two end points, the values of ksand csin Equation (20) are given by
ks=(1−gmin2)/44 andcs=(45gmin2−1)/44.  (21)

The variable SNR in Equation (20) is either the SNR per critical band, SNRCB(i), or the SNR per frequency bin, SNRBIN(k), depending on the type of processing.

The SNR per critical band is computed in case of the first spectral analysis in the frame as

SNRCB⁡(i)=0.2⁢ECB(0)⁡(i)+0.6⁢ECB(1)⁡(i)+0.2⁢ECB(2)⁡(i)NCB⁡(i)⁢⁢i=0,…⁢,19(22)
and for the second spectral analysis, the SNR is computed as

SNRCB⁡(i)=0.4⁢ECB(1)⁡(i)+0.6⁢ECB(2)⁡(i)NCB⁡(i)⁢⁢i=0,…⁢,19(23)
where ECB(1)(i) and ECB(2)(i) denote the energy per critical band information for the first and second spectral analysis, respectively (as computed in Equation (2)), ECB(0)(i) denote the energy per critical band information from the second analysis of the previous frame, and NCB(i) denote the noise energy estimate per critical band.

The SNR per critical bin in a certain critical band i is computed in case of the first spectral analysis in the frame as

SNRBIN⁡(k)=0.2⁢EBIN(0)⁡(k)+0.6⁢EBIN(1)⁡(k)+0.2⁢EBIN(2)⁡(k)NCB⁡(i),⁢k=ji,…⁢,ji+MCB⁡(i)-1(24)
and for the second spectral analysis, the SNR is computed as

EBIN(1)⁡(k)⁢⁢and⁢⁢EBIN(2)⁡(k)
denote the energy per frequency bin for the first and second spectral analysis, respectively (as computed in Equation (3)),

EBIN(0)⁡(k)
denote the energy per frequency bin from the second analysis of the previous frame, NCB(i) denote the noise energy estimate per critical band, jiis the index of the first bin in the ith critical band and MCB(i) is the number of bins in critical band i defined in above.

In case of per critical band processing for a band with index i, after determining the scaling gain as in Equation (20), and using SNR as defined in Equations (24) or (25), the actual scaling is performed using a smoothed scaling gain updated in every frequency analysis as
gCB,LP(i)=αgsgCB,LP(i)+(1−αgs)gs(26)

In this invention, a novel feature is disclosed where the smoothing factor is adaptive and it is made inversely related to the gain itself In this illustrative embodiment the smoothing factor is given by αgs=1−gs. That is, the smoothing is stronger for smaller gains gs. This approach prevents distortion in high SNR speech segments preceded by low SNR frames, as it is the case for voiced onsets. For example in unvoiced speech frames the SNR is low thus a strong scaling gain is used to reduce the noise in the spectrum. If an voiced onset follows the unvoiced frame, the SNR becomes higher, and if the gain smoothing prevents a speedy update of the scaling gain, then it is likely that a strong scaling will be used on the voiced onset which will result in poor performance. In the proposed approach, the smoothing procedure is able to quickly adapt and use lower scaling gains on the onset.

The scaling in the critical band is performed as
X′R(k+ji)=gCB,LP(i)XR(k+ji), and
X′I(k+ji)=gCB,LP(i)XI(k+ji),k=0, . . . ,MCB(i)−1′  (27)
where jiis the index of the first bin in the critical band i and MCB(i) is the number of bins in that critical band.

In case of per bin processing in a band with index i, after determining the scaling gain as in Equation (20), and using SNR as defined in Equations (24) or (25), the actual scaling is performed using a smoothed scaling gain updated in every frequency analysis as
gBIN,LP(k)=αgsgBIN,LP(k)+(1−αgs)gs(28)
where αgs=1−gssimilar to Equation (26).

Temporal smoothing of the gains prevents audible energy oscillations while controlling the smoothing using αgsprevents distortion in high SNR speech segments preceded by low SNR frames, as it is the case for voiced onsets for example.

The scaling in the critical band i is performed as
X′R(k+ji)=gBIN,LP(k+ji)XR(k+ji), and
X′I(k+ji)=gBIN,LP(k+ji)XI(k+ji),k=0, . . . ,MCB(i)−1′  (29)
where jiis the index of the first bin in the critical band i and MCB(i) is the number of bins in that critical band.

The smoothed scaling gains gBIN,LP(k) and gCB,LP(i) are initially set to 1. Each time an inactive frame is processed (VAD=0), the smoothed gains values are reset to gmindefined in Equation (18).

As mentioned above, if KVOIC>0 per bin noise suppression is performed on the first KVOICbands, and per band noise suppression is performed on the remaining bands using the procedures described above. Note that in every spectral analysis, the smoothed scaling gains gCB,LP(i) are updated for all critical bands (even for voiced bands processed with per bin processing—in this case gCB,LP(i) is updated with an average of gBIN,LP(k) belonging to the band i). Similarly, scaling gains gBIN,LP(k) are updated for all frequency bins in the first 17 bands (up to bin 74). For bands processed with per band processing they are updated by setting them equal to gCB,LP(i) in these 17 specific bands.

Note that in case of clean speech, noise suppression is not performed in active speech frames (VAD=1). This is detected by finding the maximum noise energy in all critical bands, max(NCB(i)), i=0, . . . , 19, and if this value is less or equal 15 then no noise suppression is performed.

As mentioned above, for inactive frames (VAD=0), a scaling of 0.9 gminis applied on the whole spectrum, which is equivalent to removing a constant noise floor. For VAD short-hangover frames (VAD=1 and local_VAD=0), per band processing is applied to the first 10 bands as described above (corresponding to 1700 Hz), and for the rest of the spectrum, a constant noise floor is subtracted by scaling the rest of the spectrum by a constant value gmin. This measure reduces significantly high frequency noise energy oscillations. For these bands above the 10thband, the smoothed scaling gains gCB,LP(i) are not reset but updated using Equation (26) with gs=gminand the per bin smoothed scaling gains gBIN,LP(k) are updated by setting them equal to gCB,LP(i) in the corresponding critical bands.

The procedure described above can be seen as a class-specific noise reduction where the reduction algorithm depends on the nature of speech frame being processed. This is illustrated inFIG. 4. Block401verifies if the VAD flag is 0 (inactive speech). If this is the case then a constant noise floor is removed from the spectrum by applying the same scaling gain on the whole spectrum (block402). Otherwise, block403verifies if the frame is VAD hangover frame. If this is the case then per band processing is used in the first 10 bands and the same scaling gain is used in the remaining bands (block406). Otherwise, block405verifies if voicing is detected in the first bands in the spectrum. If this is the case then per bin processing is performed in the first K voiced bands and per band processing is performed in the remaining bands (block406). If no voiced bands are detected then per band processing is performed in all critical bands (block407).

In case of processing of narrowband signals (upsampled to 12800 Hz), the noised suppression is performed on the first 17 bands (up to 3700 Hz). For the remaining 5 frequency bins between 3700 Hz and 4000 Hz, the spectrum is scaled using the last scaling gain gsat the bin at 3700 Hz. For the remaining of the spectrum (from 4000 Hz to 6400 Hz), the spectrum is zeroed.

After determining the scaled spectral components, X′R(k) and X′I(k), inverse FFT is applied on the scaled spectrum to obtain the windowed denoised signal in the time domain.

This is repeated for both spectral analysis in the frame to obtain the denoised windowed signals

xw,d(1)⁡(n)⁢⁢and⁢⁢xw,d(2)⁡(n).
For every half frame, the signal is reconstructed using an overlap-add operation for the overlapping portions of the analysis. Since a square root Hanning window is used on the original signal prior to spectral analysis, the same window is applied at the output of the inverse FFT prior to overlap-add operation. Thus, the doubled windowed denoised signal is given by

For the first half of the analysis window, the overlap-add operation for constructing the denoised signal is performed as

s⁡(n)=xww,d(0)⁡(n+LFFT/2)+xww,d(1)⁡(n),n=0,…⁢,LFFT/2-1
and for the second half of the analysis window, the overlap-add operation for constructing the denoised signal is performed as

xww,d(0)⁡(n)
is the double windowed denoised signal from the second analysis in the previous frame.

Note that with overlap-add operation, since there a 24 sample shift between the speech encoder frame and noise reduction frame, the denoised signal can be reconstructed up to 24 sampled from the lookahead in addition to the present frame. However, another 128 samples are still needed to complete the lookahead needed by the speech encoder for linear prediction (LP) analysis and open-loop pitch analysis. This part is temporary obtained by inverse windowing the second half of the denoised windowed signal

xw,d(2)⁡(n)
without performing overlap-add operation. That is

Note that this portion of the signal is properly recomputed in the next frame using overlap-add operation.

Noise Energy Estimates Update

This module updates the noise energy estimates per critical band for noise suppression. The update is performed during inactive speech periods. However, the VAD decision performed above, which is based on the SNR per critical band, is not used for determining whether the noise energy estimates are updated. Another decision is performed based on other parameters independent of the SNR per critical band. The parameters used for the noise update decision are: pitch stability, signal non-stationarity, voicing, and ratio between 2nd order and 16thorder LP residual error energies and have generally low sensitivity to the noise level variations.

The reason for not using the encoder VAD decision for noise update is to make the noise estimation robust to rapidly changing noise levels. If the encoder VAD decision were used for the noise update, a sudden increase in noise level would cause an increase of SNR even for inactive speech frames, preventing the noise estimator to update, which in turn would maintain the SNR high in following frames, and so on. Consequently, the noise update would be blocked and some other logic would be needed to resume the noise adaptation.

In this illustrative embodiment, open-loop pitch analysis is performed at the encoder to compute three open-loop pitch estimates per frame: d0,d1, and d2, corresponding to the first half-frame, second half-frame, and the lookahead, respectively. The pitch stability counter is computed as
pc=|d0−d−1|+|d1−d0|+|d2−d1|  (31)
where d−1is the lag of the second half-frame of the pervious frame. In this illustrative embodiment, for pitch lags larger than 122, the open-loop pitch search module sets d2=d1. Thus, for such lags the value of pc in equation (31) is multiplied by 3/2 to compensate for the missing third term in the equation. The pitch stability is true if the value of pc is less than 12. Further, for frames with low voicing, pc is set to 12 to indicate pitch instability. That is
IfCnorm(d0)+Cnorm(d1)+Cnorm(d2))/3+re<0.7 thenpc=12,  (32)
where Cnorm(d) is the normalized raw correlation and reis an optional correction added to the normalized correlation in order to compensate for the decrease of normalized correlation in the presence of background noise. In this illustrative embodiment, the normalized correlation is computed based on the decimated weighted speech signal swd(n) and given by

Cnorm⁡(d)=∑n=0Lsec⁢⁢swd⁡(n)⁢swd⁡(n-d)∑n=0Lsec⁢⁢swd2⁡(n)⁢∑n=0Lsec⁢swd2⁡(n-d),
where the summation limit depends on the delay itself. In this illustrative embodiment, the weighted signal used in open-loop pitch analysis is decimated by 2 and the summation limits are given according to

The signal non-stationarity estimation is performed based on the product of the ratios between the energy per critical band and the average long term energy per critical band.

The average long term energy per critical band is updated by
ECB,LT(i)=αeECB,LT(i)+(1−αe)ĒCB(i), fori=bmintobmax,  (33)
where bmin=0 and bmax=19 in case of wideband signals, and bmin=1 and bmax=16 in case of narrowband signals, and ĒCB(i) is the frame energy per critical band defined in Equation (14). The update factor αeis a linear function of the total frame energy, defined in Equation (5), and it is given as follows:

The frame non-stationarity is given by the product of the ratios between the frame energy and average long term energy per critical band. That is

The voicing factor for noise update is given by
voicing=(Cnorm(d0)+Cnorm(d1))/2+re.  (35)

Finally, the ratio between the LP residual energy after 2ndorder and 16thorder analysis is given by
resid_ratio=E(2)/E(16)  (36)
where E(2) and E(16) are the LP residual energies after 2ndorder and 16thorder analysis, and computed in the Levinson-Durbin recursion of well known to people skilled in the art. This ratio reflects the fact that to represent a signal spectral envelope, a higher order of LP is generally needed for speech signal than for noise. In other words, the difference between E(2) and E(16) is supposed to be lower for noise than for active speech.

The update decision is determined based on a variable noise_update which is initially set to 6 and it is decreased by 1 if an inactive frame is detected and incremented by 2 if an active frame is detected. Further, noise_update is bounded by 0 and 6. The noise energies are updated only when noise_update=0.

The value of the variable noise_update is updated in each frame as follows:

If (nonstat>thstat) OR (pc<12) OR (voicing>0.85) OR (resid_ratio>thresid)

In other words, frames are declared inactive for noise update when

(nonstat≦thstat) AND (pc≧12) AND (voicing≦0.85) AND (resid_ratio≦thresid) and a hangover of 6 frames is used before noise update takes place.

Thus, if noise_update=0 then

where Ntmp(i) is the temporary updated noise energy already computed in Equation (17).

Update of Voicing Cutoff Frequency:

The cut-off frequency below which a signal is considered voiced is updated. This frequency is used to determine the number of critical bands for which noise suppression is performed using per bin processing.

First, a voicing measure is computed as
νg=0.4Cnorm(d1)+0.6Cnorm(d2)+re(37)
and the voicing cut-off frequency is given by
fc=0.00017118e17.9772νgbounded by 325≦fc≦3700  (38)

Then, the number of critical bands, Kvoic, having an upper frequency not exceeding fcis determined. The bounds of 325≦fc≦3700 are set such that per bin processing is performed on a minimum of 3 bands and a maximum of 17 bands (refer to the critical bands upper limits defined above). Note that in the voicing measure calculation, more weight is given to the normalized correlation of the lookahead since the determined number of voiced bands will be used in the next frame.

Thus, in the following frame, for the first Kvoiccritical bands, the noise suppression will use per bin processing as described in above.

Note that for frames with low voicing and for large pitch delays, only per critical band processing is used and thus Kvoicis set to 0. The following condition is used:
If (0.4Cnorm(d1)+0.6Cnorm(d2)≦0.72) OR (d1>116) OR (d2>116) thenKvoic=0.

Of course, many other modifications and variations are possible. In view of the above detailed illustrative description of embodiments of this invention and associated drawings, such other modifications and variations will now become apparent to those of ordinary skill in the art. It should also be apparent that such other variations may be effected without departing from the spirit and scope of the present invention.