Patent Publication Number: US-9431023-B2

Title: Monaural noise suppression based on computational auditory scene analysis

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation of U.S. patent application Ser. No. 12/860,043, (now U.S. Pat. No. 8,447,596, issued May 21, 2013), filed Aug. 20, 2010, which claims the benefit of U.S. Provisional Application Ser. No. 61/363,638, filed Jul. 12, 2010, all of which are incorporated herein by reference. 
    
    
     BACKGROUND OF THE INVENTION 
     1. Field of the Invention 
     The present invention relates generally to audio processing, and more particularly to processing an audio signal to suppress noise. 
     2. Description of Related Art 
     Currently, there are many methods for reducing background noise in an adverse audio environment. A stationary noise suppression system suppresses stationary noise, by either a fixed or varying number of dB. A fixed suppression system suppresses stationary or non-stationary noise by a fixed number of dB. The shortcoming of the stationary noise suppressor is that non-stationary noise will not be suppressed, whereas the shortcoming of the fixed suppression system is that it must suppress noise by a conservative level in order to avoid speech distortion at low signal-to-noise ratios (SNR). 
     Another form of noise suppression is dynamic noise suppression. A common type of dynamic noise suppression systems is based on SNR. The SNR may be used to determine a degree of suppression. Unfortunately, SNR by itself is not a very good predictor of speech distortion due to the presence of different noise types in the audio environment. SNR is a ratio indicating how much louder speech is then noise. However, speech may be a non-stationary signal which may constantly change and contain pauses. Typically, speech energy, over a given period of time, will include a word, a pause, a word, a pause, and so forth. Additionally, stationary and dynamic noises may be present in the audio environment. As such, it can be difficult to accurately estimate the SNR. The SNR averages all of these stationary and non-stationary speech and noise components. There is no consideration in the determination of the SNR of the characteristics of the noise signal—only the overall level of noise. In addition, the value of SNR can vary based on the mechanisms used to estimate the speech and noise, such as whether it based on local or global estimates, and whether it is instantaneous or for a given period of time. 
     To overcome the shortcomings of the prior art, there is a need for an improved noise suppression system for processing audio signals. 
     SUMMARY OF THE INVENTION 
     The present technology provides a robust noise suppression system that may concurrently reduce noise and echo components in an acoustic signal while limiting the level of speech distortion. An acoustic signal may be received and transformed to cochlear-domain sub-band signals. Features, such as pitch, may be identified and tracked within the sub-band signals. Initial speech and noise models may be then be estimated at least in part from a probability analysis based on the tracked pitch sources. Improved speech and noise models may be resolved from the initial speech and noise models and noise reduction may be performed on the sub-band signals. An acoustic signal may be reconstructed from the noise-reduced sub-band signals. 
     In an embodiment, noise reduction may be performed by executing a program stored in memory to transform an acoustic signal from the time domain to cochlea-domain sub-band signals. Multiple sources of pitch may be tracked within the sub-band signals. A speech model and one or more noise models may be generated at least in part based on the tracked pitch sources. Noise reduction may be performed on the sub-band signals based on the speech model and one or more noise models. 
     A system for performing noise reduction in an audio signal may include a memory, frequency analysis module, source inference module, and a modifier module. The frequency analysis module may be stored in the memory and executed by a processor to transform a time domain acoustic to cochlea domain sub-band signals. The source inference engine may be stored in the memory and executed by a processor to track multiple sources of pitch within a sub-band signal and to generate a speech model and one or more noise models based at least in part on the tracked pitch sources. The modifier module may be stored in the memory and executed by a processor to perform noise reduction on the sub-band signals based on the speech model and one or more noise models. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is an illustration of an environment in which embodiments of the present technology may be used. 
         FIG. 2  is a block diagram of an exemplary audio device. 
         FIG. 3  is a block diagram of an exemplary audio processing system. 
         FIG. 4  is a block diagram of exemplary modules within an audio processing system. 
         FIG. 5  is a block diagram of exemplary components within a modifier module. 
         FIG. 6  is a flowchart of an exemplary method for performing noise reduction for an acoustic signal. 
         FIG. 7  is a flowchart of an exemplary method for estimating speech and noise models. 
         FIG. 8  is a flowchart of an exemplary method for resolving speech and noise models. 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     The present technology provides a robust noise suppression system that may concurrently reduce noise and echo components in an acoustic signal while limiting the level of speech distortion. An acoustic signal may be received and transformed to cochlear-domain sub-band signals. Features, such as pitch, may be identified and tracked within the sub-band signals. Initial speech and noise models may be then be estimated at least in part from a probability analysis based on the tracked pitch sources. Improved speech and noise models may be resolved from the initial speech and noise models and noise reduction may be performed on the sub-band signals. An acoustic signal may be reconstructed from the noise-reduced sub-band signals. 
     Multiple pitch sources may be identified in a sub-band frame and tracked over multiple frames. Each tracked pitch source (“track”) is analyzed based on several features, including pitch level, salience, and how stationary the pitch source is. Each pitch source is also compared to stored speech model information. For each track, a probability of being a target speech source is generated based on the features and comparison to the speech model information. 
     A track with the highest probability may be, in some cases, designated as speech and the remaining tracks are designated as noises. In some embodiments, there may be multiple speech sources, and a “target” speech may be the desired speech with other speech sources considered noise. Tracks with a probability over a certain threshold may be designated as speech. In addition, there may be a “softening” of the decision in the system. Downstream of the track probability determination, a spectrum may be constructed for each pitch track, and each track&#39;s probability may be mapped to gains through which the corresponding spectrum is added into the speech and non-stationary noise models. If the probability is high, the gain for the speech model will be 1 and the gain for the noise model will be 0, and vice versa. 
     The present technology may utilize any of several techniques to provide an improved noise reduction of an acoustic signal. The present technology may estimate speech and noise models based on tracked pitch sources and probabilistic analysis of the tracks. Dominant speech detection may be used to control stationary noise estimations. Models for speech, noise and transients may be resolved into speech and noise. Noise reduction may be performed by filtering sub-bands using filters based on optimal least-squares estimation or on constrained optimization. These concepts are discussed in more detail below. 
       FIG. 1  is an illustration of an environment in which embodiments of the present technology may be used. A user may act as an audio (speech) source  102 , hereinafter audio source  102 , to an audio device  104 . The exemplary audio device  104  includes a primary microphone  106 . The primary microphone  106  may be omni-directional microphones. Alternatively embodiments may utilize other forms of a microphone or acoustic sensors, such as a directional microphone. 
     While the microphone  106  receives sound (i.e. acoustic signals) from the audio source  102 , the microphone  106  also picks up noise  112 . Although the noise  112  is shown coming from a single location in  FIG. 1 , the noise  112  may include any sounds from one or more locations that differ from the location of audio source  102 , and may include reverberations and echoes. These may include sounds produced by the audio device  104  itself. The noise  112  may be stationary, non-stationary, and/or a combination of both stationary and non-stationary noise. 
     Acoustic signals received by microphone  106  may be tracked, for example by pitch. Features of each tracked signal may be determined and processed to estimate models for speech and noise. For example, an audio source  102  may be associated with a pitch track with a higher energy level than the noise  112  source. Processing signals received by microphone  106  is discussed in more detail below. 
       FIG. 2  is a block diagram of an exemplary audio device  104 . In the illustrated embodiment, the audio device  104  includes receiver  200 , processor  202 , primary microphone  106 , audio processing system  204 , and an output device  206 . The audio device  104  may include further or other components necessary for audio device  104  operations. Similarly, the audio device  104  may include fewer components that perform similar or equivalent functions to those depicted in  FIG. 2 . 
     Processor  202  may execute instructions and modules stored in a memory (not illustrated in  FIG. 2 ) in the audio device  104  to perform functionality described herein, including noise reduction for an acoustic signal. Processor  202  may include hardware and software implemented as a processing unit, which may process floating point operations and other operations for the processor  202 . 
     The exemplary receiver  200  may be configured to receive a signal from a communications network, such as a cellular telephone and/or data communication network. In some embodiments, the receiver  200  may include an antenna device. The signal may then be forwarded to the audio processing system  204  to reduce noise using the techniques described herein, and provide an audio signal to output device  206 . The present technology may be used in one or both of the transmit and receive paths of the audio device  104 . 
     The audio processing system  204  is configured to receive the acoustic signals from an acoustic source via the primary microphone  106  and process the acoustic signals. Processing may include performing noise reduction within an acoustic signal. The audio processing system  204  is discussed in more detail below. The acoustic signal received by primary microphone  106  may be converted into one or more electrical signals, such as, for example, a primary electrical signal and a secondary electrical signal. The electrical signal may be converted by an analog-to-digital converter (not shown) into a digital signal for processing in accordance with some embodiments. The primary acoustic signal may be processed by the audio processing system  204  to produce a signal with an improved signal-to-noise ratio. 
     The output device  206  is any device which provides an audio output to the user. For example, the output device  206  may include a speaker, an earpiece of a headset or handset, or a speaker on a conference device. 
     In various embodiments, the primary microphone is an omni-directional microphone; in other embodiments, the primary microphone is a directional microphone. 
       FIG. 3  is a block diagram of an exemplary audio processing system  204  for performing noise reduction as described herein. In exemplary embodiments, the audio processing system  204  is embodied within a memory device within audio device  104 . The audio processing system  204  may include a transform module  305 , a feature extraction module  310 , a source inference engine  315 , modification generator module  320 , modifier module  330 , reconstructor module  335 , and post processor module  340 . Audio processing system  204  may include more or fewer components than illustrated in  FIG. 3 , and the functionality of modules may be combined or expanded into fewer or additional modules. Exemplary lines of communication are illustrated between various modules of  FIG. 3 , and in other figures herein. The lines of communication are not intended to limit which modules are communicatively coupled with others, nor are they intended to limit the number of and type of signals communicated between modules. 
     In operation, an acoustic signal is received from the primary microphone  106 , is converted to an electrical signal, and the electrical signal is processed through transform module  305 . The acoustic signal may be pre-processed in the time domain before being processed by transform module  305 . Time domain pre-processing may also include applying input limiter gains, speech time stretching, and filtering using an FIR or IIR filter. 
     The transform module  305  takes the acoustic signals and mimics the frequency analysis of the cochlea. The transform module  305  comprises a filter bank designed to simulate the frequency response of the cochlea. The transform module  305  separates the primary acoustic signal into two or more frequency sub-band signals. A sub-band signal is the result of a filtering operation on an input signal, where the bandwidth of the filter is narrower than the bandwidth of the signal received by the transform module  305 . The filter bank may be implemented by a series of cascaded, complex-valued, first-order IIR filters. Alternatively, other filters or transforms such as a short-time Fourier transform (STFT), sub-band filter banks, modulated complex lapped transforms, cochlear models, wavelets, etc., can be used for the frequency analysis and synthesis. The samples of the sub-band signals may be grouped sequentially into time frames (e.g. over a predetermined period of time). For example, the length of a frame may be 4 ms, 8 ms, or some other length of time. In some embodiments, there may be no frame at all. The results may include sub-band signals in a fast cochlea transform (FCT) domain. 
     The analysis path  325  may be provided with an FCT domain representation  302 , hereinafter FCT  302 , and optionally a high-density FCT representation  301 , hereinafter HD FCT  301 , for improved pitch estimation and speech modeling (and system performance). A high-density FCT may be a frame of sub-bands having a higher density than the FCT  302 ; a HD FCT  301  may have more sub-bands than FCT  302  within a frequency range of the acoustic signal. The signal path also may be provided with an FCT representation  304 , hereinafter FCT  304 , after implementing a delay  303 . Using the delay  303  provides the analysis path  325  with a “lookahead” latency that can be leveraged to improve the speech and noise models during subsequent stages of processing. If there is no delay, the FCT  304  for the signal path is not necessary; the output of FCT  302  in the diagram can be routed to the signal path processing as well as to the analysis path  325 . In the illustrated embodiment, the lookahead delay  303  is arranged before the FCT  304 . As a result, the delay is implemented in the time domain in the illustrated embodiment, thereby saving memory resources as compared with implementing the lookahead delay in the FCT-domain. In alternative embodiments, the lookahead delay may be implemented in the FCT domain, such as by delaying the output of FCT  302  and providing the delayed output to the signal path. In doing so, computational resources may be saved compared with implementing the lookahead delay in the time-domain. 
     The sub-band frame signals are provided from transform module  305  to an analysis path  325  sub-system and a signal path sub-system. The analysis path  325  sub-system may process the signal to identify signal features, distinguish between speech components and noise components of the sub-band signals, and generate a modification. The signal path sub-system is responsible for modifying sub-band signals of the primary acoustic signal by reducing noise in the sub-band signals. Noise reduction can include applying a modifier, such as a multiplicative gain mask generated in the analysis path  325  sub-system, or applying a filter to each sub-band. The noise reduction may reduce noise and preserve the desired speech components in the sub-band signals. 
     Feature extraction module  310  of the analysis path sub-system  325  receives the sub-band frame signals derived from the acoustic signal and computes features for each sub-band frame, such as pitch estimates and second-order statistics. In some embodiments, a pitch estimate may be determined by feature extraction module  310  and provided to source inference engine  315 . In some embodiments, the pitch estimate may be determined by source inference engine  315 . The second-order statistics (instantaneous and smoothed autocorrelations/energies) are computed in feature extraction module  310  for each sub-band signal. For the HD FCT  301 , only the zero-lag autocorrelations are computed and used by the pitch estimation procedure. The zero-lag autocorrelation may be a time sequence of the previous signal multiplied by itself and averaged. For the middle FCT  302 , the first-order lag autocorrelations are also computed since these may be used to generate a modification. The first-order lag autocorrelations, which may be computed by multiplying the time sequence of the previous signal with a version of itself offset by one sample, may also be used to improve the pitch estimation. 
     Source inference engine  315  may process the frame and sub-band second-order statistics and pitch estimates provided by feature extraction module  310  (or generated by source inference engine  315 ) to derive models of the noise and speech in the sub-band signals. Source inference engine  315  processes the FCT-domain energies to derive models of the pitched components of the sub-band signals, the stationary components, and the transient components. The speech, noise and optional transient models are resolved into speech and noise models. If the present technology is utilizing non-zero lookahead, source inference engine  315  is the component wherein the lookahead is leveraged. At each frame, source inference engine  315  receives a new frame of analysis path data and outputs a new frame of signal path data (which corresponds to an earlier relative time in the input signal than the analysis path data). The lookahead delay may provide time to improve discrimination of speech and noise before the sub-band signals are actually modified (in the signal path). Also, source inference engine  315  outputs a voice activity detection (VAD) signal (for each tap) that is internally fed back to the stationary noise estimator to help prevent over-estimation of the noise. 
     The modification generator module  320  receives models of the speech and noise as estimated by source inference engine  315 . Modification generator module  320  may derive a multiplicative mask for each sub-band per frame. Modification generator module  320  may also derive a linear enhancement filter for each sub-band per frame. The enhancement filter includes a suppression backoff mechanism wherein the filter output is cross-faded with its input sub-band signals. The linear enhancement filter may be used in addition or in place of the multiplicative mask, or not used at all. The cross-fade gain is combined with the filter coefficients for the sake of efficiency. Modification generator module  320  may also generate a post-mask for applying equalization and multiband compression. Spectral conditioning may also be included in this post-mask. 
     The multiplicative mask may be defined as a Wiener gain. The gain may be derived based on the autocorrelation of the primary acoustic signal and an estimate of the autocorrelation of the speech (e.g. the speech model) or an estimate of the autocorrelation of the noise (e.g. the noise model). Applying the derived gain yields a minimum mean-squared error (MMSE) estimate of the clean speech signal given the noisy signal. 
     The linear enhancement filter is defined by a first-order Wiener filter. The filter coefficients may be derived based on the 0 th  and 1 st  order lag autocorrelation of the acoustic signal and an estimate of the 0 th  and 1 st  order lag autocorrelation of the speech or an estimate of the 0 th  and 1 st  order lag autocorrelation of the noise. In one embodiment, the filter coefficients are derived based on the optimal Wiener formulation using the following equations: 
     
       
         
           
             
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     where r xx [0] is the 0 th  order lag autocorrelation of the input signal, r xx [1] is the 1 st  order lag autocorrelation of the input signal, r ss [0] is the estimated 0 th  order lag autocorrelation of the speech, and r ss [1] is the estimated 1 st  order lag autocorrelation of the speech. In the Wiener formulations, * denotes conjugation and ∥ denotes magnitude. In some embodiments, the filter coefficients may be derived in part based on a multiplicative mask derived as described above. The coefficient β 0  may be assigned the value of the multiplicative mask, and β 1  may be determined as the optimal value for use in conjunction with that value of β 0  according to the formula: 
               β   1     =         (         r   ss     ⁡     [   1   ]       -       β   0     ⁢       r   xx     ⁡     [   1   ]           )         r   xx     ⁡     [   0   ]         .           
Applying the filter yields an MMSE estimate of the clean speech signal given the noisy signal.
 
     The values of the gain mask or filter coefficients output from modification generator module  320  are time and sub-band signal dependent and optimize noise reduction on a per sub-band basis. The noise reduction may be subject to the constraint that the speech loss distortion complies with a tolerable threshold limit. 
     In embodiments, the energy level of the noise component in the sub-band signal may be reduced to no less than a residual noise level, which may be fixed or slowly time-varying. In some embodiments, the residual noise level is the same for each sub-band signal, in other embodiments it may vary across sub-bands and frames. Such a noise level may be based on a lowest detected pitch level. 
     Modifier module  330  receives the signal path cochlear-domain samples from transform block  305  and applies a modification, such as for example a first-order FIR filter, to each sub-band signal. Modifier module  330  may also apply a multiplicative post-mask to perform such operations as equalization and multiband compression. For Rx applications, the post-mask may also include a voice equalization feature. Spectral conditioning may be included in the post-mask. Modifier module  330  may also apply speech reconstruction at the output of the filter, but prior to the post-mask. 
     Reconstructor module  335  may convert the modified frequency sub-band signals from the cochlea domain back into the time domain. The conversion may include applying gains and phase shifts to the modified sub-band signals and adding the resulting signals. 
     Reconstructor module  335  forms the time-domain system output by adding together the FCT-domain sub-band signals after optimized time delays and complex gains have been applied. The gains and delays are derived in the cochlea design process. Once conversion to the time domain is completed, the synthesized acoustic signal may be post-processed or output to a user via output device  206  and/or provided to a codec for encoding. 
     Post-processor module  340  may perform time-domain operations on the output of the noise reduction system. This includes comfort noise addition, automatic gain control, and output limiting. Speech time stretching may be performed as well, for example, on an Rx signal. 
     Comfort noise may be generated by a comfort noise generator and added to the synthesized acoustic signal prior to providing the signal to the user. Comfort noise may be a uniform constant noise not usually discernible to a listener (e.g., pink noise). This comfort noise may be added to the synthesized acoustic signal to enforce a threshold of audibility and to mask low-level non-stationary output noise components. In some embodiments, the comfort noise level may be chosen to be just above a threshold of audibility and may be settable by a user. In some embodiments, the modification generator module  320  may have access to the level of comfort noise in order to generate gain masks that will suppress the noise to a level at or below the comfort noise. 
     The system of  FIG. 3  may process several types of signals received by an audio device. The system may be applied to acoustic signals received via one or more microphones. The system may also process signals, such as a digital Rx signal, received through an antenna or other connection. 
       FIG. 4  is a block diagram of exemplary modules within an audio processing system. The modules illustrated in the block diagram of  FIG. 4  include source inference engine (SIE)  315 , modification generator (MG) module  320 , and modifier (MOD) module  330 . 
     Source inference engine  315  receives second order statistics data from feature extraction module  310  and provides this data to polyphonic pitch and source tracker (tracker)  420 , stationary noise modeler  428  and transient modeler  436 . Tracker  420  receives the second order statistics and a stationary noise model and estimates pitches within the acoustic signal received by microphone  106 . 
     Estimating the pitches may include estimating the highest level pitch, removing components corresponding to the pitch from the signal statistics, and estimating the next highest level pitch, for a number of iterations per a configurable parameter. First, for each frame, peaks may be detected in the FCT-domain spectral magnitude, which may be based on the 0 th  order lag autocorrelation and may further be based on a mean subtraction such that the FCT-domain spectral magnitude has zero mean. In some embodiments, the peaks must meet a certain criteria, such as being larger than their four nearest neighbors, and must have a large enough level relative to the maximum input level. The detected peaks form the first set of pitch candidates. Subsequently, sub-pitches are added to the set for each candidate, i.e., f0/2 f0/3 f0/4, and so forth, where f0 denotes a pitch candidate. Cross correlation is then performed by adding the level of the interpolated FCT-domain spectral magnitude at harmonic points over a specific frequency range, thereby forming a score for each pitch candidate. Because the FCT-domain spectral magnitude is zero-mean over that range (due to the mean subtraction), pitch candidates are penalized if a harmonic does not correspond to an area of significant amplitude (because the zero-mean FCT-domain spectral magnitude will have negative values at such points). This ensures that frequencies below the true pitch are adequately penalized relative to the true pitch. For example, a 0.1 Hz candidate would be given a near-zero score (because it would be the sum of all FCT-domain spectral magnitude points, which is zero by construction). 
     The cross-correlation may then provide scores for each pitch candidate. Many candidates are very close in frequency (because of the addition of the sub-pitches f0/2 f0/3 f0/4 etc to the set of candidates). The scores of candidates close in frequency are compared, and only the best one is retained. A dynamic programming algorithm is used to select the best candidate in the current frame, given the candidates in previous frames. The dynamic programming algorithm ensures the candidate with the best score is generally selected as the primary pitch, and helps avoid octave errors. 
     Once the primary pitch has been chosen, the harmonic amplitudes are computed simply using the level of the interpolated FCT-domain spectral magnitude at harmonic frequencies. A basic speech model is applied to the harmonics to make sure they are consistent with a normal speech signal. Once the harmonic levels are computed, the harmonics are removed from the interpolated FCT-domain spectral magnitude to form a modified FCT-domain spectral magnitude. 
     The pitch detection process is repeated, using the modified FCT-domain spectral magnitude. At the end of the second iteration, the best pitch is selected, without running another dynamic programming algorithm. Its harmonics are computed, and removed from the FCT-domain spectral magnitude. The third pitch is the next best candidate, and its harmonic levels are computed on the twice-modified FCT-domain spectral magnitude. This process is continued until a configurable number of pitches has been estimated. The configurable number may be, for example, three or some other number. As a last stage, the pitch estimates are refined using the phase of the 1 st  order lag autocorrelation. 
     A number of the estimated pitches are then tracked by the polyphonic pitch and source tracker  420 . The tracking may determine changes in frequency and level of the pitch over multiple frames of the acoustic signal. In some embodiments, a subset of the estimated pitches are tracked, for example the estimated pitches having the highest energy level(s). 
     The output of the pitch detection algorithm consists of a number of pitch candidates. The first candidate may be continuous across frames because it is selected by the dynamic programming algorithm. The remaining candidates may be output in order of salience, and therefore may not form frequency-continuous tracks across frames. For the task of assigning types to sources (talker associated with speech or distractor associated with noise), it is important to be able to deal with pitch tracks that are continuous in time, rather than collections of candidates at each frame. This is the goal of the multi-pitch tracking step, carried out on the per-frame pitch estimates determined by the pitch detection. 
     Given N input candidates, the algorithm outputs N tracks, immediately reusing a track slot when the track terminates and a new one is born. At each frame the algorithm considers the N! associations of (N) existing tracks to (N) new pitch candidates. For example, if N=3, tracks 1,2,3 from the previous frame can be continued to candidates 1,2,3 in the current frame in 6 manners: (1-1,2-2,3-3), (1-1,2-3,3-2), (1-2,2-3,3-1), (1-2,2-1,3-3), (1-3,2-2,3-1), (1-3,3-2,2-1). For each of these associations, a transition probability is computed to evaluate which association is the most likely. The transition probability is computed based on how close in frequency the candidate pitch is from the track pitch, the relative candidate and track levels, and the age of the track (in frames, since its beginning). The transition probabilities tend to favor continuous pitch tracks, tracks with larger levels, and tracks that are older than other ones. 
     Once the N! transition probabilities are computed, the largest one is selected, and the corresponding transition is used to continue the tracks into the current frame. A track dies when its transition probability to any of the current candidates is 0 in the best association (in other words, it cannot be continued into any of the candidates). Any candidate pitch that isn&#39;t connected to an existing track forms a new track with an age of 0. The algorithm outputs the tracks, their level, and their age. 
     Each of the tracked pitches may be analyzed to estimate the probability of whether the tracked source is a talker or speech source The cues estimated and mapped to probabilities are level, stationarity, speech model similarity, track continuity, and pitch range. 
     The pitch track data is provided to buffer  422  and then to pitch track processor  424 . Pitch track processor  424  may smooth the pitch tracking for consistent speech target selection. Pitch track processor  424  may also track the lowest-frequency identified pitch. The output of pitch track processor  424  is provided to pitch spectral modeler  426  and to compute modification filter module  450 . 
     Stationary noise modeler  428  generates a model of stationary noise. The stationary noise model may be based on second order statistics as well as a voice activity detection signal received from pitch spectral modeler  426 . The stationary noise model may be provided to pitch spectral modeler  426 , update control module  432 , and polyphonic pitch and source tracker  420 . Transient modeler  436  may receive second order statistics and provide the transient noise model to transient model resolution  442  via buffer  438 . The buffers  422 ,  430 ,  438 , and  440  are used to account for the “lookahead” time difference between the analysis path  325  and the signal path. 
     Construction of the stationary noise model may involve a combined feedback and feed-forward technique based on speech dominance. For example, in one feed-forward technique, if the constructed speech and noise models indicate that the speech is dominant in a given sub-band, the stationary noise estimator is not updated for that sub-band. Rather, the stationary noise estimator is reverted to that of the previous frame. In one feedback technique, if speech (voice) is determined to be dominant in a given sub-band for a given frame, the noise estimation is rendered inactive (frozen) in that sub-band during the next frame. Hence, a decision is made in a current frame not to estimate stationary noise in a subsequent frame. 
     The speech dominance may be indicated by a voice activity detector (VAD) indicator computed for the current frame and used by update control module  432 . The VAD may be stored in the system and used by the stationary noise modeler  428  in the subsequent frame. This dual-mode VAD prevents damage to low-level speech, especially high-frequency harmonics; this reduces the “voice muffling” effect frequently incurred in noise suppressors. 
     Pitch spectral modeler  426  may receive pitch track data from pitch track processor  424 , a stationary noise model, a transient noise model, second orders statistics, and optionally other data and may output a speech model and a nonstationary noise model. Pitch spectral modeler  426  may also provide a VAD signal indicating whether speech is dominant in a particular sub-band and frame. 
     The pitch tracks (each comprising pitch, salience, level, stationarity, and speech probability) are used to construct models of the speech and noise spectra by the pitch spectral modeler  426 . To construct models of the speech and noise, the pitch tracks may be reordered based on the track saliences, such that the model for the highest salience pitch track will be constructed first. An exception is that high-frequency tracks with a salience above a certain threshold are prioritized. Alternatively, the pitch tracks may be reordered based on the speech probability, such that the model for the most probable speech track will be constructed first. 
     In pitch spectral modeler  426 , a broadband stationary noise estimate may be subtracted from the signal energy spectrum to form a modified spectrum. Next, the present system may iteratively estimate the energy spectra of the pitch tracks according to the processing order determined in the first step. An energy spectrum may be derived by estimating an amplitude for each harmonic (by sampling the modified spectrum), computing a harmonic template corresponding to the response of the cochlea to a sinusoid at the harmonic&#39;s amplitude and frequency, and accumulating the harmonic&#39;s template into the track spectral estimate. After the harmonic contributions are aggregated, the track spectrum is subtracted to form a new modified signal spectrum for the next iteration. 
     To compute the harmonic templates, the module uses a pre-computed approximation of the cochlea transfer function matrix. For a given sub-band, the approximation consists of a piecewise linear fit of the sub-band&#39;s frequency response where the approximation points are optimally selected from the set of sub-band center frequencies (so that sub-band indices can be stored instead of explicit frequencies). 
     After the harmonic spectra are iteratively estimated, each spectrum is allocated in part to the speech model and in part to the non-stationary noise model, where the extent of the allocation to the speech model is dictated by the speech probability of the corresponding track, and the extent of the allocation to the noise model is determined as an inverse of the extent of the allocation to the speech model. 
     Noise model combiner  434  may combine stationary noise and non-stationary noise and provide the resulting noise to transient model resolution  442 . Update control  432  may determine whether or not the stationary noise estimate is to be updated in the current frame, and provide the resulting stationary noise to noise model combiner  434  to be combined with the non-stationary noise model. 
     Transient model resolution  442  receives a noise model, speech model, and transient model and resolves the models into speech and noise. The resolution involves verifying the speech model and noise model do not overlap, and determining whether the transient model is speech or noise. The noise and non-speech transient models are deemed noise and the speech model and transient speech are determined to be speech. The transient noise models are provided to repair module  462 , and the resolved speech and noise modules are provided to SNR estimator  444  as well as the compute modification filter module  450 . The speech model and the noise model are resolved to reduce cross-model leakage. The models are resolved into a consistent decomposition of the input signal into speech and noise. 
     SNR estimator  444  determines an estimate of the signal to noise ratio. The SNR estimate can be used to determine an adaptive level of suppression in the crossfade module  464 . It can also be used to control other aspects of the system behavior. For example, the SNR may be used to adaptively change what the speech/noise model resolution does. 
     Compute modification filter module  450  generates a modification filter to be applied to each sub-band signal. In some embodiments, a filter such as a first-order filter is applied in each sub-band instead of a simple multiplier. Modification filter module  450  is discussed in more detail below with respect to  FIG. 5 . 
     The modification filter is applied to the sub-band signals by module  460 . After applying the generated filter, portions of the sub-band signal may be repaired at repair module  462  and then linearly combined with the unmodified sub-band signal at crossfade module  464 . The transient components may be repaired by module  462  and the crossfade may be performed based on the SNR provided by SNR estimator  444 . The sub-bands are then reconstructed at reconstructor module  335 . 
       FIG. 5  is a block diagram of exemplary components within a modifier module. Modifier module  500  includes delays  510 ,  515 , and  520 , multipliers  525 ,  530 ,  535 , and  540  and summing modules  545 ,  550 ,  555  and  560 . The multipliers  525 ,  530 ,  535 , and  540  correspond to the filter coefficients for the modifier module  500 . A sub-band signal for the current frame, x[k, t], is received by the modifier module  500 , processed by the delays, multipliers, and summing modules, and an estimate of the speech s[k,t] is provided at the output of the final summing module  545 . In the modifier module  500 , noise reduction is carried out by filtering each sub-band signal, unlike previous systems which apply a scalar mask. With respect to scalar multiplication, such per-sub-band filtering allows nonuniform spectral treatment within a given sub-band; in particular this may be relevant where speech and noise components have different spectral shapes within the sub-band (as in the higher frequency sub-bands), and the spectral response within the sub-band can be optimized to preserve the speech and suppress the noise. 
     The filter coefficients β 0  and β 1  are computed based on speech models derived by the source inference engine  315 , combined with a sub-pitch suppression mask (for example by tracking the lowest speech pitch and suppressing the sub-bands below this min pitch by reducing the β 0  and β 1  values for those sub-bands), and crossfaded based on the desired noise suppression level. In another approach, the VQOS approach is used to determine the crossfade. The β 0  and β 1  values are then subjected to interframe rate-of-change limits and interpolated across frames before being applied to the cochlear-domain signals in the modification filter. For the implementation of the delay, one sample of cochlear-domain signals (a time slice across sub-bands) is stored in the module state. 
     To implement a first-order modification filter, the received sub-band signal is multiplied by β 0  and also delayed by one sample. The signal at the output of the delay is multiplied by β 1 . The results of the two multiplications are summed and provided as the output s[k,t]. The delay, multiplications, and summation correspond to the application of a first-order linear filter. There may be N delay-multiply-sum stages, corresponding to an Nth order filter. 
     When applying a first-order filter in each sub-band instead of a simple multiplier, an optimal scalar multiplier (mask) may be used in the non-delayed branch of the filter. The filter coefficient for the delayed branch may be derived to be optimal conditioned on the scalar mask. In this way, the first-order filter is able to achieve a higher-quality speech estimate than using the scalar mask alone. The system can be extended to higher orders (an N-th order filter) if desired. Also, for an N-th order filter, the autocorrelations up to lag N may be computed in feature extraction module  310  (second-order statistics). In the first-order case, the zero-th and first-order lag autocorrelations are computed. This is a distinction from prior systems which rely solely on the zero-th order lag. 
       FIG. 6  is a flowchart of an exemplary method for performing noise reduction for an acoustic signal. First, an acoustic signal may be received at step  605 . The acoustic signal may be received by microphone  106 . The acoustic signal may be transformed to the cochlea domain at step  610 . Transform module  305  may perform a fast cochlea transform to generate cochlea domain sub-band signals. In some embodiments, the transformation may be performed after a delay is implemented in the time domain. In such a case, there can be two cochleas, one for the analysis path  325 , and one for the signal path after the time-domain delay. 
     Monaural features are extracted from the cochlea domain sub-band signals at step  615 . The monaural features are extracted by feature extraction module  310  and may include second order statistics. Some features may include pitch, energy level, pitch salience, and other data. 
     Speech and noise models may be estimated for cochlea sub-bands at step  620 . The speech and noise models may be estimated by source inference engine  315 . Generating the speech model and noise model may include estimating a number of pitch elements for each frame, tracking a selected number of the pitch elements across frames, and selecting one of the tracked pitches as a talker based on a probabilistic analysis. The speech model is generated from the tracked talker. A non-stationary noise model may be based on the other tracked pitches and a stationary noise model may be based on extracted features provided by feature extraction module  310 . Step  620  is discussed in more detail with respect to the method of  FIG. 7 . 
     The speech model and noise models may be resolved at step  625 . Resolving the speech model and noise model may be performed to eliminate any cross-leakage between the two models. Step  625  is discussed in more detail with respect to the method of  FIG. 8 . Noise reduction may be performed on the subband signals based on the speech model and noise models at step  630 . The noise reduction may include applying a first order (or Nth order) filter to each sub-band in the current frame. The filter may provide better noise reduction than simply applying a scalar gain for each sub-band. The filter may be generated in modification generator  320  and applied to the sub-band signals at step  630 . 
     The sub-bands may be reconstructed at step  635 . Reconstruction of the sub-bands may involve applying a series of delays and complex-multiply operations to the sub-band signals by reconstructor module  335 . The reconstructed time-domain signal may be post-processed at step  640 . Post-processing may consist of adding comfort noise, performing automatic gain control (AGC) and applying a final output limiter. The noise-reduced time-domain signal is output at step  645 . 
       FIG. 7  is a flowchart of an exemplary method for estimating speech and noise models. The method of  FIG. 7  may provide more detail for step  620  in the method of  FIG. 6 . First, pitch sources are identified at step  705 . Polyphonic pitch and source tracker (tracker)  420  may identify pitches present within a frame. The identified pitches may be tracked across frames at step  710 . The pitches may be tracked over different frames by tracker  420 . 
     A speech source is identified by a probability analysis at step  715 . The probability analysis identifies a probability that each pitch track is the desired talker based on each of several features, including level, salience, similarity to speech models, stationarity, and other features. A single probability for each pitch is determined based on the feature probabilities for that pitch, for example, by multiplying the feature probabilities. The speech source may be identified as the pitch track with the highest probability of being associated with the talker. 
     A speech model and noise model are constructed at step  720 . The speech model is constructed in part based on the pitch track with the highest probability. The noise model is constructed based in part on the pitch tracks having a low probability of corresponding to the desired talker. Transient components identified as speech may be included in the speech model and transient components identified as non-speech transient may be included in the noise model. Both the speech model and the noise model are determined by source inference engine  315 . 
       FIG. 8  is a flowchart of an exemplary method for resolving speech and noise models. A noise model estimation may be configured using feedback and feed-forward control at step  805 . When a sub-band within a current frame is determined to be dominated by speech, the noise estimate from the previous frame is frozen (e.g., used in the current frame) as well as in the next frame for that sub-band. 
     A speech model and noise model are resolved into speech and noise at step  810 . Portions of a speech model may leak into a noise model, and vice-versa. The speech and noise models are resolved such that there is no leakage between the two. 
     A delayed time-domain acoustic signal may be provided to the signal path to allow additional time (look-ahead) for the analysis path to discriminate between speech and noise in step  815 . By utilizing a time-domain delay in the look-ahead mechanism, memory resources are saved as compared to implementing the lookahead delay in the cochlear domain. 
     The steps discussed in  FIGS. 6-8  may be performed in a different order than that discussed, and the methods of  FIGS. 4 and 5  may each include additional or fewer steps than those illustrated. 
     The above described modules, including those discussed with respect to  FIG. 3 , may include instructions stored in a storage media such as a machine readable medium (e.g., computer readable medium). These instructions may be retrieved and executed by the processor  202  to perform the functionality discussed herein. Some examples of instructions include software, program code, and firmware. Some examples of storage media include memory devices and integrated circuits. 
     While the present invention is disclosed by reference to the preferred embodiments and examples detailed above, it is to be understood that these examples are intended in an illustrative rather than a limiting sense. It is contemplated that modifications and combinations will readily occur to those skilled in the art, which modifications and combinations will be within the spirit of the invention and the scope of the following claims.