Patent Publication Number: US-8977545-B2

Title: System and method for multi-channel noise suppression

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
     This application claims the benefit of U.S. Provisional Patent Application No. 61/413,231, filed on Nov. 12, 2010, which is incorporated herein by reference in its entirety. 
    
    
     FIELD OF THE INVENTION 
     This application relates generally to systems that process audio signals, such as speech signals, to remove undesired noise components therefrom. 
     BACKGROUND 
     An input speech signal picked up by a microphone can be corrupted by acoustic noise present in the environment surrounding the microphone (also referred to as background noise). If no attempt is made to mitigate the impact of the noise, the corruption of the input speech signal will result in a degradation of the perceived quality and intelligibility of its desired speech component when played back to a listener. The corruption of the input speech signal can also adversely impact the performance of speech coding and recognition algorithms. 
     One additional source of noise that can corrupt the input speech signal picked up by the microphone is wind. Wind causes turbulence in air flow and, if this turbulence impacts the microphone, it can result in the microphone picking up sound referred to as “wind noise.” In general, wind noise is bursty in nature and can last from a few milliseconds up to a few hundred milliseconds or more. Because wind noise is impulsive and can exceed the nominal amplitude of the desired speech component in the input speech signal, the presence of such noise will further degrade the perceived quality and intelligibility of the desired speech component when played back to a listener. 
     Therefore, what is needed is a system and method that can effectively detect and suppress wind and background noise components in an input speech signal to improve the perceived quality and intelligibility of a desired speech component in the input speech signal when played back to a listener. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS/FIGURES 
       The accompanying drawings, which are incorporated herein and form a part of the specification, illustrate the present invention and, together with the description, further serve to explain the principles of the invention and to enable a person skilled in the pertinent art to make and use the invention. 
         FIG. 1  illustrates a front view of an example wireless communication device in which embodiments of the preset invention can be implemented. 
         FIG. 2  illustrates a back view of the example wireless communication device shown in  FIG. 1 . 
         FIG. 3  illustrates a block diagram of a multi-microphone speech communication system that includes a multi-channel noise suppression system in accordance with an embodiment of the present invention. 
         FIG. 4  illustrates a block diagram of a multi-channel noise suppression system in accordance with an embodiment of the present invention. 
         FIG. 5  illustrates plots of two exemplary functions that can be used by a non-linear processor to determine a suppression gain in accordance with an embodiment of the present invention 
         FIG. 6  illustrates a block diagram of an example computer system that can be used to implement aspects of the present invention. 
     
    
    
     The present invention will be described with reference to the accompanying drawings. The drawing in which an element first appears is typically indicated by the leftmost digit(s) in the corresponding reference number. 
     DETAILED DESCRIPTION 
     1. Introduction 
     In the following description, numerous specific details are set forth in order to provide a thorough understanding of the invention. However, it will be apparent to those skilled in the art that the invention, including structures, systems, and methods, may be practiced without these specific details. The description and representation herein are the common means used by those experienced or skilled in the art to most effectively convey the substance of their work to others skilled in the art. In other instances, well-known methods, procedures, components, and circuitry have not been described in detail to avoid unnecessarily obscuring aspects of the invention. 
     References in the specification to “one embodiment,” “an embodiment,” “an example embodiment,” etc., indicate that the embodiment described may include a particular feature, structure, or characteristic, but every embodiment may not necessarily include the particular feature, structure, or characteristic. Moreover, such phrases are not necessarily referring to the same embodiment. Further, when a particular feature, structure, or characteristic is described in connection with an embodiment, it is submitted that it is within the knowledge of one skilled in the art to affect such feature, structure, or characteristic in connection with other embodiments whether or not explicitly described. 
     As noted in the background section above, wind and background noise can corrupt an input speech signal picked up by a microphone, resulting in a degradation of the perceived quality and intelligibility of a desired speech component in the input speech signal when played back to a listener. Described herein are multi-channel noise suppression systems and methods that are configured to detect and suppress wind and background noise using at least two spatially separated microphones: a primary speech microphone and at least one noise reference microphone. The primary speech microphone is positioned to be close to a desired speech source during regular use of the multi-microphone system in which it is implemented, whereas the noise reference microphone is positioned to be farther from the desired speech source during regular use of the multi-microphone system in which it is further implemented. 
     In embodiments, the multi-channel noise suppression systems and methods are configured to first detect and suppress wind noise in the input speech signal picked up by the primary speech microphone and, potentially, the input speech signal picked up by the noise reference microphone. Following wind noise detection and suppression, the multi-channel noise suppression systems and methods are configured to perform further noise suppression in two stages: a first linear processing stage followed by a second non-linear processing stage. The linear processing stage performs background noise suppression using a blocking matrix (BM) and an adaptive noise canceler (ANC). The BM is configured to remove desired speech in the input speech signal received by the noise reference microphone to get a “cleaner” background noise component. Then, the ANC is used to remove the background noise in the input speech signal received by the primary speech microphone based on the “cleaner” background noise component to provide a noise suppressed input speech signal. The non-linear processing stage follows the linear processing stage and is configured to suppress any residual wind and/or background noise present in the noise suppressed input speech signal. 
     Before describing further details of the multi-channel noise suppression systems and methods of the present invention, the discussion below begins by providing an example multi-microphone communication device and multi-microphone speech communication system in which embodiments of the present invention can be implemented. 
     2. Example Operating Environment 
       FIGS. 1 and 2  respectively illustrate a front portion  100  and a back portion  200  of an example wireless communication device  102  in which embodiments of the present invention can be implemented. Wireless communication device  102  can be a personal digital assistant (PDA), a cellular telephone, or a tablet computer, for example. 
     As shown in  FIG. 1 , front portion  100  of wireless communication device  102  includes a primary speech microphone  104  that is positioned to be close to a user&#39;s mouth during regular use of wireless communication device  102 . Accordingly, primary speech microphone  104  is positioned to capture the user&#39;s speech (i.e., the desired speech). As shown in  FIG. 2 , a back portion  200  of wireless communication device  102  includes a noise reference microphone  106  that is positioned to be farther from the user&#39;s mouth during regular use than primary speech microphone  104 . For instance, noise reference microphone  106  can be positioned as far from the user&#39;s mouth during regular use as possible. 
     Although the input speech signals received by primary speech microphone  104  and noise reference microphone  106  will each contain desired speech and background noise, by positioning primary speech microphone  104  so that it is closer to the user&#39;s mouth than noise reference microphone  106  during regular use, the level of the user&#39;s speech that is captured by primary speech microphone  104  is likely to be greater than the level of the user&#39;s speech that is captured by noise reference microphone  106 , while the background noise levels captured by each microphone should be about the same. This information can be exploited to effectively suppress background noise as will be described below in regard to  FIG. 4 . 
     In addition, because the two microphones  104  and  106  are spatially separated, wind noise picked up by one of the two microphones often will not be picked up (or at least not to the same extent) by the other microphone. This is because air turbulence caused by wind is usually a fairly local event unlike sound based pressure waves that go everywhere. This fact can be exploited to detect and suppress wind noise as will be further described below in regard to  FIG. 4 . 
     Front portion  100  of wireless communication device  102  can further include, in at least one embodiment, a speaker  108  that is configured to produce sound in response to an audio signal received, for example, from a person located at a remote distance from wireless communication device  102 . 
     It should be noted that primary speech microphone  104  and noise reference microphone  106  are shown to be positioned on the respective front and back portions of wireless communication device  102  for illustrative purposes only and is not intended to be limiting. Persons skilled in the relevant art(s) will recognize that primary speech microphone  104  and noise reference microphone  106  can be positioned in any suitable locations on wireless communication device  102 . 
     It should be further noted that a single noise reference microphone  106  is shown in  FIG. 2  for illustrative purposes only and is not intended to be limiting. Persons skilled in the relevant art(s) will recognize that wireless communication device  102  can include any reasonable number of reference microphones. 
     Moreover, primary speech microphone  104  and noise reference microphone  106  are respectively shown in  FIGS. 1 and 2  to be included in wireless communication device  102  for illustrative purposes only. It will be recognized by persons skilled in the relevant art(s) that primary speech microphone  104  and noise reference microphone  106  can be implemented in any suitable multi-microphone system or device that operates to process audio signals for transmission, storage and/or playback to a user. For example, primary speech microphone  104  and noise reference microphone  106  can be implemented in a Bluetooth® headset, a hearing aid, a personal recorder, a video recorder, or a sound pick-up system for public speech. 
     Referring now to  FIG. 3 , a block diagram of a multi-microphone speech communication system  300  that includes a multi-channel noise suppression system in accordance with an embodiment of the present invention is illustrated. Speech communication system  300  can be implemented, for example, in wireless communication device  102 . As shown in  FIG. 3 , speech communication system  300  includes an input speech signal processor  305  and, in at least one embodiment, an output speech signal processor  310 . 
     Input speech signal processor  305  is configured to process the input speech signals received by primary speech microphone  104  and noise reference microphone  106 , which are physically positioned in the general manner as described above in  FIGS. 1 and 2  (i.e., with primary speech microphone  104  closer to the desired speech source during regular use than noise reference microphone  106 ). Input speech signal processor  305  includes analog-to-digital converters (ADCs)  315  and  320 , echo cancelers  325  and  330 , analysis modules  335 ,  340 , and  345 , multi-channel noise suppression system  350 , synthesis module  355 , high pass filter (HPF)  360 , and speech encoder  365 . 
     In operation of input speech signal processor  305 , primary speech microphone  104  receives a primary input speech signal and noise reference microphone  106  receives a noise reference input speech signal. Both input speech signals may contain a desired speech component, an undesired wind noise component, and an undesired background noise component. The level of these components will generally vary over time. For example, assuming speech communication system  300  is implemented in a cellular telephone, the user of the cellular telephone may stop speaking, intermittently, to listen to a remotely located person to whom a call was placed. When the user stops speaking, the level of the desired speech component will drop to zero or near zero. In the same context, while the user is speaking, a truck may pass by creating background noise in addition to the desired speech of the user. As the truck gets farther away from the user, the level of the background noise component will drop to zero or near zero (assuming no other sources of background noise are present in the surrounding environment). 
     As the two continuous input speech signals are received by primary speech microphone  104  and noise reference microphone  106 , they are converted to discrete time digital representations by ADCs  315  and  320 , respectively. The sample rate of ADCs  315  and  320  can be determined to be equal to, or some marginal amount higher than, twice the maximum desired component frequency of the desired speech within the signals. 
     After being digitized by ADCs  315  and  320 , the primary input speech signal and the noise reference input speech signal are respectively processed in the time-domain by echo cancelers  325  and  330 . In an embodiment, echo cancelers  325  and  330  are configured to remove or suppress acoustic echo. 
     Acoustic echo can occur, for example, when an audio signal output by speaker  108  is picked up by primary speech microphone  104  and/or noise reference microphone  106 . When this occurs, an acoustic echo can be sent back to the source of the audio signal output by speaker  108 . For example, assuming speech communication system  300  is implemented in a cellular telephone, a user of the cellular telephone may be conversing with a remotely located person to whom a call was placed. En this instance, the audio signal output by speaker  108  may include speech received from the remotely located person. Acoustic echo can occur as a result of the remotely located person&#39;s speech, output by speaker  108 , being picked up by primary speech microphone  104  and/or noise reference microphone  106  and feedback to him or her, leading to adverse effects that degrade the call performance. 
     After echo cancelation, the primary input speech signal and the noise reference input speech signal are respectively processed by analysis modules  335  and  340 . More specifically, analysis module  335  is configured to process the primary input speech signal on a frame-by-frame basis, where a frame includes a set of consecutive samples taken from the time domain representation of the primary input speech signal it receives. Analysis module  335  calculates, in at least one embodiment, the Discrete Fourier Transform (DFT) of each frame to transform the frames into the frequency domain. Analysis module  335  can calculate the DFT using, for example, the Fast Fourier Transform (FFT). In general, the resulting frequency domain signal describes the magnitudes and phases of component cosine waves (also referred to as component frequencies) that make up the time domain frame, where each component cosine wave corresponds to a particular frequency between DC and one-half the sampling rate used to obtain the samples of the time domain frame. 
     For example, and in one embodiment, each time domain frame of the primary input speech signal includes 128 samples and can be transformed into the frequency domain using a 128-point DFT by analysis module  335 . The 128-point DFT provides 65 complex values that represent the magnitudes and phases of the component cosine waves that make up the time domain frame. In another embodiment, once the complex values that represent the magnitudes and phases of the component cosine waves are obtained for a frame of the primary input speech signal, analysis module  335  can group the cosine wave components into sub-bands, where a sub-band can include one or more cosine wave components. In one embodiment, analysis module  335  can group the cosine wave components into sub-bands based on the Bark frequency scale or based on some other acoustic perception quality of the human ear (such as decreased sensitivity to higher frequency components). As is well known, the Bark frequency scale ranges from 1 to 24 Barks and each Bark corresponds to one of the first 24 critical bands of hearing. Analysis module  340  can be constructed to process the noise reference input speech signal in a similar manner as analysis module  345  described above. 
     The frequency domain version of the primary input speech signal and the noise reference input speech signal are respectively denoted by P(m, f) and R(m, f) in  FIG. 3 , where m indexes a particular frame made up of consecutive time domain samples of the input speech signal and f indexes a particular frequency component or sub-band of the input speech signal for the frame indexed by m. Thus, for example, P(1,10) denotes the complex value of the 10 th  frequency component or sub-band for the 1 st  frame of the primary input speech signal P(m, f). The same signal representation is true, in at least one embodiment, for other signals and signal components similarly denoted in  FIG. 3 . 
     It should be noted that in other embodiments, echo cancelers  325  and  330  can be respectively placed after analysis modules  340  and  345  and process the frequency domain input speech signal to remove or suppress acoustic echo. 
     Multi-channel noise suppression system  350  receives P(m, f) and R(m, f) and is configured to detect and suppress wind noise and background noise in at least P(m, f). In particular, multi-channel noise suppression system  350  is configured to exploit spatial information embedded in P(m, f) and R(m, f) to detect and suppress wind noise and background noise in P(m, f) to provide, as output, a noise suppressed primary input speech signal {circumflex over (Ŝ)} 1 (m, f). Further details of multi-channel noise suppression system  350  are described below in regard to  FIG. 4 . 
     Synthesis module  355  is configured to process the frequency domain version of the noise suppressed primary input speech signal {circumflex over (Ŝ)} 1 (m, f) to synthesize its time domain signal. More specifically, synthesis module  355  is configured to calculate, in at least one embodiment, the inverse DFT of the input speech signal {circumflex over (Ŝ)} 1 (m, f) to transform the signal into the time domain. Synthesis module  355  can calculate the inverse DFT using, for example, the inverse FFT. 
     HPF  360  removes undesired low frequency components of the time domain version of the noise suppressed primary input speech signal {circumflex over (Ŝ)} 1 (m, f) and speech encoder  365  then encodes the input speech signal {circumflex over (Ŝ)} 1 (m, f) by compressing the data of the input speech signal on a frame-by-frame basis. There are many speech encoding schemes available and, depending on the particular application or device in which speech communication system  300  is implemented, different speech encoding schemes may be better suited. For example, and in one embodiment, where speech communication system  300  is implemented in a wireless communication device, such as a cellular phone, speech encoder  365  can perform linear predictive coding, although this is just one example. The encoded speech signal is subsequently provided as output for eventual transmission over a communication channel. 
     Referring now to the second speech signal processor illustrated in  FIG. 3 , output speech signal processor  310  includes a speech decoder  370 , a DC remover  375 , a digital-to-analog converter (DAC)  380 , and a speaker  108 . This speech signal processor can be optionally included in speech communication system  300  when some type of audio feedback is, received for playback by speech communication system  300 . 
     In operation of output speech signal processor  310 , speech decoder  370  is configured to decompress an encoded speech signal received over a communication channel. More specifically, speech decoder  370  can apply any one of a number of speech decoding schemes, on a frame-by-frame basis, to the received speech signal. For example, and in one embodiment, where speech communication system  300  is implemented in a wireless communication device, such as a cellular phone, speech decoder  370  can perform decoding based on the speech signal being encoded using linear predictive coding, although this is just one example. 
     Once decoded, the speech signal is received by DC remover  375 , which is configured to remove any DC component of the speech signal. The DC removed and decoded speech signal is then converted by DAC  380  into an analog signal for playback by speaker  108 . 
     In an embodiment, the DC removed and decoded speech signal can be further provided to multi-channel noise suppression system  350 , as illustrated in  FIG. 3 , to further suppress acoustic echo in the primary input speech signal P(m, f). Prior to providing the DC removed and decoded speech signal to multi-channel noise suppression system  350 , the time domain signal can be converted to a frequency domain signal O(m, f) by analysis module  345 , which can be constructed to operate in a similar manner as described above in regard to analysis module  335 . 
     3. System and Method for Multi-Channel Noise Suppression 
       FIG. 4  illustrates a block diagram of multi-channel noise suppression system  350 , introduced in  FIG. 3 , in accordance with an embodiment of the present invention. Multi-channel noise suppression system  350  is configured to detect and suppress wind and acoustic background noise in the primary input speech signal P(m, f) using the noise reference input speech signal R(m, f). As illustrated in  FIG. 4 , multi-channel noise suppression system  350  specifically includes a wind noise detection and suppression module  405  for detecting and suppressing wind noise, followed by two additional noise suppression modules: a linear processor (LP)  410  and a non-linear processor (NLP)  415 . 
     Ignoring the operational details of wind noise detection and suppression module  405  for the moment, LP  410  is configured to process a wind noise suppressed primary input speech signal {circumflex over (P)}(m, f) and a wind noise suppressed reference input speech signal {circumflex over (R)}(m, f) to remove acoustic background noise from {circumflex over (P)}(m, f) by exploiting spatial diversity with linear filters. In general, {circumflex over (P)}(m, f) and {circumflex over (R)}(m, f) respectively represent the residual signals of {circumflex over (P)}(m, f) and {circumflex over (R)}(m, f) after having undergone wind noise detection and, potentially, wind noise suppression by wind noise detection and suppression module  405 . Both {circumflex over (P)}(m, f) and {circumflex over (R)}(m, f) contain components of the user&#39;s speech (i.e., desired speech) and acoustic background noise. However, because of the relative positioning of primary speech microphone  104  and noise reference microphone  106  with respect to the desired speech source as described above, the level of the desired speech S 1 (m, f) in {circumflex over (P)}(m, f) is likely to be greater than a level of the desired speech S 2 (m, f) in {circumflex over (R)}(m, f), while the acoustic background noise components N 1 (m, f) and N 2 (m, f) of each input speech signal are likely to be about equal in level. 
     LP  410  is configured to exploit this information to estimate filters for spatial suppression of background noise sources by filtering the wind noise suppressed primary input speech signal {circumflex over (P)}(m, f) using the wind noise suppressed reference input speech signal {circumflex over (R)}(m, f) to provide, as output, a noise suppressed primary input speech signal Ŝ 1 (m, f). As illustrated, LP  410  specifically includes a time-varying blocking matrix (BM)  420  and a time-varying active noise canceler (ANC)  425 . 
     Time-varying BM  420  is configured to estimate and remove the desired speech component S 2 (m, f) in {circumflex over (R)}(m, f) to produce a “cleaner” background noise component {circumflex over (N)} 2 (m, f). More specifically, BM  420  includes a BM filter  430  configured to filter {circumflex over (P)}(m, f) to provide an estimate of the desired speech component S 2 (m, f) in {circumflex over (R)}(m, f) BM  420  then subtracts the estimated desired speech component Ŝ 2 (m, f) from {circumflex over (R)}(m, f) using subtractor  435  to provide, as output, the “cleaner” background noise component {circumflex over (N)} 2 (m, f). 
     After {circumflex over (N)} 2 (m, f) has been obtained, time-varying ANC  425  is configured to estimate and remove the undesirable background noise component N 1 (m, f) in {circumflex over (P)}(m, f) to provide, as output, the noise suppressed primary input speech signal Ŝ 1 (m, f). More specifically, ANC  425  includes an ANC filter  440  configured to filter the “cleaner” background noise component {circumflex over (N)} 2 (m, f) to provide an estimate of the background noise component N 1 (m, f) in {circumflex over (P)}(m, f). ANC  425  then subtracts the estimated background noise component {circumflex over (N)} 1 (m, f) from {circumflex over (P)}(m, f) using subtractor  445  to provide, as output, the noise suppressed primary input speech signal Ŝ 1 (m, f). 
     In an embodiment, BM filter  430  and ANC filter  440  are derived using closed-form solutions that require calculation of time-varying statistics of complex signals in noise suppression system  350 . More specifically, and in at least one embodiment, statistics estimator  450  is configured to estimate the necessary statistics used to derive the closed form solution for the transfer function of BM filter  430  based on {circumflex over (P)}(m, f) and {circumflex over (R)}(m, f), and statistics estimator  460  is configured to estimate the necessary statistics used to derive the closed form solution for the transfer function of ANC filter  440  based on {circumflex over (N)} 2 (m, f) and {circumflex over (P)}(m, f). In general, spatial information embedded in the signals received by statistics estimators  450  and  460  is exploited to estimate these necessary statistics. After the statistics have been estimated, filter controllers  455  and  465  respectively determine and update the transfer functions of BM filter  430  and ANC filter  440 . 
     Further details and alternative embodiments of LP  410  are set forth in U.S. patent application Ser. No. 13/295,818 to Thyssen et al., filed Nov. 14, 2011, and entitled “System and Method for Multi-Channel Noise Suppression Based on Closed-Form Solutions and Estimation of Time-Varying Complex Statistics,” the entirety of which is incorporated by reference herein. 
     It should be noted that, although closed form solutions based on time varying statistics are used to derive the transfer functions of BM filter  430  and ANC filter  440  in  FIG. 4 , in other embodiments adaptive algorithms (e.g., least mean square adaptive algorithm) can be used to derive or update the transfer functions of one or both of these filters. 
     In at least one embodiment, and as further shown in  FIG. 4 , wind noise detection and suppression module  405  is configured to process primary input speech signal P(m, f) and noise reference input speech signal R(m, f) before LP  410 . This is because LP module  410  works under the general assumption that primary input speech signal P(m, f) includes the same background noise and desired speech as noise reference input speech signal R(m, f), albeit subject to different acoustic channels between a source and the respective microphones. [No, this is not quite right, or at least, can easily be misunderstood]. Wind noise corruption present in one or both of primary input speech signal P(m, f) and noise reference input speech signal R(m, f) can affect the ability of LP  410  to effectively remove acoustic background noise from primary input speech signal P(m, f). Therefore, it can be important to detect and, potentially, suppress wind noise present in primary input speech signal P(m, f) and/or noise reference input speech signal R(m, f) before acoustic noise suppression is performed by LP  410  or, alternatively, forego acoustic noise suppression by LP  410  when wind noise is detected to be present (or above a certain threshold) in primary input speech signal P(m, f) and/or noise reference input speech signal R(m, f). 
     In U.S. patent application Ser. No. 13/250,291 to Chen et al., filed Sep. 30, 2011, and entitled “Method and Apparatus for Wind Noise Detection and Suppression Using Multiple Microphones” (the entirety of which is incorporated by reference herein), two different wind noise detection and suppression modules were disclosed, each of which presents a potential implementation for wind noise detection and suppression module  405  illustrated in  FIG. 4 . 
     Although not shown in  FIG. 4 , wind noise detection and suppression module  405  can provide an indication as to, or the actual value of, the level of wind noise determined to be present in primary input speech signal P(m, f) and/or noise reference input speech signal R(m, f) to LP  410 . In an embodiment, LP  410  can use these indications or values to determine whether to update BM filter  430  and ANC filter  440  and/or adjust the rate at which BM filter  430  and ANC filter  440  are updated. For example, statistics estimators  455  and  460  can halt updating the statistics used to derive the transfer functions of BM filter  430  and ANC filter  440  when the indications or values from wind noise detection and suppression module  405  show that wind noise is present or above some threshold amount in segments of P(m, f) and/or R(m, f). 
     In another embodiment, where adaptive algorithms are used to derive BM filter  430  and ANC filter  440 , adaptation of BM filter  430  and ANC filter  440  can be halted or slowed when the indications or values from wind noise detection and suppression module  405  show that wind noise is present or above some threshold amount in either P(m, f) and/or R(m, f). 
     In yet another embodiment, depending on the indications or values from wind noise detection and suppression module  405  regarding the amount of wind noise present in P(m, f) and/or R(m, f), ANC  425  can be bypassed and not used to perform background noise suppression on P(m, f). For example, when wind noise detection and suppression module  405  indicates that wind noise is present or above some threshold in noise reference input speech signal R(m, f), ANC  425  can be bypassed. This is because noise reference input speech signal R(m, f) has wind noise and, assuming wind noise detection and suppression module  405  cannot adequately suppress the wind noise in {circumflex over (R)}(m, f), ANC  425  may not be able to effectively reduce any background noise that is present in {circumflex over (P)}(m, f) using {circumflex over (R)}(m, f). 
     However, simply bypassing ANC  425  can lead to its own problems. For example, if ANC  425  provides, on average, X dB of background noise reduction when wind noise is absent or below some threshold in both P(m, f) and R(m, f), simply turning ANC  425  off when wind noise is present or above some threshold in R(m, f) can cause the background noise level in the noise suppressed primary input speech signal Ŝ 1 (m, f), provided as output by ANC  425 , to be X dB higher in the regions where R(m, f) is corrupted by wind noise. If this is not dealt with, the background noise level in Ŝ 1 (m, f) will modulate with the presence of wind noise in R(m, f). 
     To combat this problem, a single-channel noise suppression module can be further included in wind noise detection and suppression module  405  or LP  425  to perform single-channel noise suppression with X dB of target noise suppression to {circumflex over (P)}(m, f) when ANC  425  is bypassed. Doing so can help to maintain a roughly constant background noise level. 
     Referring now to NLP  415 , NLP  415  is configured to further reduce residual background noise in the noise suppressed primary input speech signal Ŝ 1 (m, f) provided as output by LP  410 . In general, LP  410  uses linear processing to suppress or attenuate noise sources. In practice, the noise field is highly complex with multiple noise sources and reverberations from the objects in the physical environment. The linear spatial filtering has the ability to implement spatially well-defined directions of attenuation, e.g. highly attenuate a point noise in an environment without reverberation, but is generally unable to attenuate all directions except for a well-defined direction (such as the direction of the desired source), unless a very high number of microphones is used. Hence, the noise suppressed primary input speech signal Ŝ 1 (m, f), provided as output by LP  410 , can have unacceptable levels of residual background noise. 
     For example, the above description assumes that only a single noise reference microphone is used by the multi-microphone system in which LP  410  is implemented. In this scenario, LP  410  can effectively cancel, at most, a single background noise point source from {circumflex over (P)}(m, f) in an anechoic environment. Therefore, when there is more than one background noise source in the environment surrounding primary speech microphone  104  and noise reference microphone  106  or the environment is not anechoic or result in acoustic channels more complex than LP  410  is capable of modeling effectively, the noise suppressed primary input speech signal Ŝ 1 (m, f) can have unacceptable levels of residual background noise. 
     In an embodiment, NLP  415  is configured to determine and apply a suppression gain to the noise suppressed primary input speech signal Ŝ 1 (m, f) based on a difference in level between the primary input speech signal P(m, f) (or a signal indicative of the level of the primary input speech signal P(m, f)) and the noise reference input speech signal R(m, f) (or a signal indicative of the level of the noise reference input speech signal R(m, f)) to further reduce such residual background noise. The difference between the two microphone levels can provide an indication as to the amount of background noise present in the primary input speech signal P(m, f). 
     For example, if the level of the primary input speech signal P(m, f) (or a signal indicative of the level of the primary input speech signal P(m, f)) is much greater than the noise reference input speech signal R(m, f) (or a signal indicative of the level of the noise reference input speech signal R(m, f)), there is a strong likelihood that desired speech is present in primary input speech signal P(m, f). On the other hand, if the level of the primary input speech signal P(m, f) (or a signal indicative of the level of the primary input speech signal P(m, f)) is about the same as the level of the noise reference input speech signal R(m, f) (or a signal indicative of the level of the noise reference input speech signal R(m, f)), there is a strong likelihood that desired speech is absent in primary input speech signal P(m, f). 
     In one embodiment, the difference in level between the primary input speech signal P(m, f) and the noise reference input speech signal R(m, f) can be determined based on the difference between calculated signal-to-noise ratio (SNR) values for each signal. 
       FIG. 5  illustrates plots of two exemplary functions  505  and  510  that can be used by NLP  415  to determine a suppression gain for a calculated difference in signal level between the primary input speech signal P(m, f) (or a signal indicative of the level of the primary input speech signal P(m, f)) and the noise reference input speech signal R(m, f) (or a signal indicative of the level of the noise reference input speech signal R(m, f)) in accordance with an embodiment of the present invention. 
     In general, both functions  505  and  510  provide monotonically increasing values of suppression gain for increasing values in difference in level between the primary input speech signal P(m, f) (or a signal indicative of the level of the primary input speech signal P(m, f)) and the noise reference input speech signal R(m, f) (or a signal indicative of the level of the noise reference input speech signal R(m, f)). The more aggressive function  510  can be used by NLP  415  when it is determined that desired speech is absent from the primary input speech signal P(m, f), whereas the less aggressive function  505  can be used by NLP  415  when it is determined that desired speech is present in the primary input speech signal P(m, f). In other embodiments, a single function, rather than two functions as shown in  FIG. 5 , can be used by NLP  415  to determine the suppression gain independent of whether desired speech is determined to be present in the primary input speech signal P(m, f). 
     Once a suppression gain is determined by NLP  415 , the suppression gain can be smoothed in time. For example, a suppression gain determined for a current frame of the primary input speech signal P(m, f) can be smoothed across one or more suppression gains determined for previous frames of the primary input speech signal P(m, f). In addition, in the instance where NLP  415  determines suppression gains for the primary input speech signal P(m, f) on a per frequency component or per sub-band basis, the suppression gains determined by NLP  415  can be smoothed across suppression gains for adjacent frequency components or sub-bands. 
     To determine whether speech is present in, or absent from, the primary input speech signal P(m, f) such that either function  505  or  510  can be chosen, NLP  415  can make use of voice activity detector (VAD)  470 . VAD  470  is configured to identify the presence or absence of desired speech in the primary input speech signal P(m, f) and provide a desired speech detection signal to NLP  415  that indicates whether desired speech is present in, or absent from, a particular frame of the primary input speech signal P(m, f). VAD  470  can identify the presence or absence of desired speech in the primary input speech signal P(m, f) by calculating multiple desired speech indication values, for example, the difference between the level of the primary input signal P(m, f) and the level of the noise reference input speech signal R(m, f), and further by calculation the short-term cross-correlation between the primary input signal {P(m, f)} and the noise reference input speech signal {R(m, f)}. Although not shown in  FIG. 4 , the primary input speech signal P(m, f) and noise reference input speech signal R(m, f) can be received by VAD  470  as inputs. 
     VAD  470  can indicate to NLP  415  the presence of desired speech with comparatively little or no background noise in the primary input speech signal P(m, f) if the difference between the level of the primary input signal P(m, f) and the level of the noise reference input speech signal R(m, f) is large (e.g., above some threshold value), and the short-term cross-correlation between the two input signals is high (e.g., above some threshold value). 
     In addition, VAD  470  can indicate to NLP  415  the presence of similar levels of desired speech and background noise is the primary input speech signal P(m, f) if the difference between the level of the primary input signal P(m, f) and the level of the noise reference input speech signal R(m, f) is small (e.g., below some threshold value), and the short-term cross-correlation between the two input signals is low (e.g., below some threshold value). 
     Finally, VAD  470  can indicate to NLP  415  the presence of background noise with comparatively little or no desired speech if the difference between the level of the primary input signal P(m, f) and the level of the noise reference input speech signal R(m, f) is small (e.g., below some threshold value), and the short-term cross-correlation between the two input signals is high (e.g., above some threshold value). 
     Although not shown in  FIG. 4 , wind noise detection and suppression module  405  can further provide an indication as to, or the actual value of, the level of wind noise determined to be present in primary input speech signal P(m, f) and/or noise reference input speech signal R(m, f) to NLP  415 . In an embodiment, NLP  415  can use these indications or values to further determine suppression gains for the noise suppressed primary input speech signal Ŝ 1 (m, f), provided as output by LP  410 . For example, for a segment of the primary input speech signal P(m, f) indicated as being corrupted by wind noise, NLP  415  can determine and apply an aggressive suppression gain to the corresponding segment of the noise suppressed primary input speech signal Ŝ 1 (m, f). 
     4. Example Computer System Implementation 
     It will be apparent to persons skilled in the relevant art(s) that various elements and features of the present invention, as described herein, can be implemented in hardware using analog and/or digital circuits, in software, through the execution of instructions by one or more general purpose or special-purpose processors, or as a combination of hardware and software. 
     The following description of a general purpose computer system is provided for the sake of completeness. Embodiments of the present invention can be implemented in hardware, or as a combination of software and hardware. Consequently, embodiments of the invention may be implemented in the environment of a computer system or other processing system. An example of such a computer system  600  is shown in  FIG. 6 . All of the modules depicted in  FIGS. 3 and 4  can execute on one or more distinct computer systems  600 . 
     Computer system  600  includes one or more processors, such as processor  604 . Processor  604  can be a special purpose or a general purpose digital signal processor. Processor  604  is connected to a communication infrastructure  602  (for example, a bus or network). Various software implementations are described in terms of this exemplary computer system. After reading this description, it will become apparent to a person skilled in the relevant art(s) how to implement the invention using other compute systems and/or computer architectures. 
     Computer system  600  also includes a main memory  606 , preferably random access memory (RAM), and may also include a secondary memory  608 . Secondary memory  608  may include, for example, a hard disk drive  610  and/or a removable storage drive  612 , representing a floppy disk drive, a magnetic tape drive, an optical disk drive, or the like. Removable storage drive  1212  reads from and/or writes to a removable storage unit  616  in a well-known manner. Removable storage unit  616  represents a floppy disk, magnetic tape, optical disk, or the like, which is read by and written to by removable storage drive  612 . As will be appreciated by persons skilled in the relevant art(s), removable storage unit  616  includes a computer usable storage medium having stored therein computer software and/or data. 
     In alternative implementations, secondary memory  608  may include other similar means for allowing computer programs or other instructions to be loaded into computer system  600 . Such means may include, for example, a removable storage unit  618  and an interface  614 . Examples of such means may include a program cartridge and cartridge interface (such as that found in video game devices), a removable memory chip (such as an EPROM, or PROM) and associated socket, a thumb drive and USB port, and other removable storage units  618  and interfaces  614  which allow software and data to be transferred from removable storage unit  618  to computer system  600 . 
     Computer system  600  may also include a communications interface  620 . Communications interface  620  allows software and data to be transferred between computer system  600  and external devices. Examples of communications interface  620  may include a modem, a network interface (such as an Ethernet card), a communications port, a PCMCIA slot and card, etc. Software and data transferred via communications interface  620  are in the form of signals which may be electronic, electromagnetic, optical, or other signals capable of being received by communications interface  620 . These signals are provided to communications interface  620  via a communications path  622 . Communications path  622  carries signals and may be implemented using wire or cable, fiber optics, a phone line, a cellular phone link, an RF link and other communications channels. 
     As used herein, the terms “computer program medium” and “computer readable medium” are used to generally refer to tangible storage media such as removable storage units  616  and  618  or a hard disk installed in hard disk drive  610 . These computer program products are means for providing software to computer system  600 . 
     Computer programs (also called computer control logic) are stored in main memory  606  and/or secondary memory  608 . Computer programs may also be received via communications interface  620 . Such computer programs, when executed, enable the computer system  600  to implement the present invention as discussed herein. In particular, the computer programs, when executed, enable processor  604  to implement the processes of the present invention, such as any of the methods described herein. Accordingly, such computer programs represent controllers of the computer system  600 . Where the invention is implemented using software, the software may be stored in a computer program product and loaded into computer system  600  using removable storage drive  612 , interface  614 , or communications interface  620 . 
     In another embodiment, features of the invention are implemented primarily in hardware using, for example, hardware components such as application-specific integrated circuits (ASICs) and gate arrays. Implementation of a hardware state machine so as to perform the functions described herein will also be apparent to persons skilled in the relevant art(s). 
     6. Conclusion 
     The present invention has been described above with the aid of functional building blocks illustrating the implementation of specified functions and relationships thereof. The boundaries of these functional building blocks have been arbitrarily defined herein for the convenience of the description. Alternate boundaries can be defined so long as the specified functions and relationships thereof are appropriately performed. 
     In addition, while various embodiments have been described above, it should be understood that they have been presented by way of example only, and not limitation. It will be understood by those skilled in the relevant art(s) that various changes in form and details can be made to the embodiments described herein without departing from the spirit and scope of the invention as defined in the appended claims. Accordingly, the breadth and scope of the present invention should not be limited by any of the above-described exemplary embodiments, but should be defined only in accordance with the following claims and their equivalents.