Abstract:
An audio processing system processes an audio signal that may come from one or more microphones. The audio processing system may use information from one or more non-acoustic sensors to improve a variety of system characteristics, including responsiveness and quality. Especially those audio processing systems that use spatial information, for example to separate multiple audio sources, are undesirably susceptible to changes in the relative position of any audio sources, the audio processing system itself, or any combination thereof. Using the non-acoustic sensor information may decrease this susceptibility advantageously in an audio processing system.

Description:
CROSS REFERENCE TO RELATED APPLICATION 
     This application claims the benefit of U.S. Provisional Application No. 61/325,742, filed on Apr. 19, 2010, entitled “Selection of System Parameters According to Non-Microphone Sensor Information,” having inventors Carlo Murgia, Michael M. Goodwin, Peter Santos, and Dana Massie, which is hereby incorporated herein by reference in its entirety. 
    
    
     BACKGROUND 
     Communication devices that capture and transmit and/or store acoustic signals often use noise reduction techniques to provide a higher quality (i.e., less noisy) signal. Noise reduction may improve the audio quality in communication devices such as mobile telephones which convert analog audio to digital audio data streams for transmission over mobile telephone networks. 
     A device that receives an acoustic signal through a microphone can process the acoustic signal to distinguish between a desired and an undesired component. A noise reduction system based on acoustic information alone can be misguided or slow to respond to certain changes in environmental conditions. 
     There is a need to increase the quality and responsiveness of noise reduction systems to changes in environmental conditions. 
     SUMMARY OF THE INVENTION 
     The systems and methods of the present technology provide audio processing of an acoustic signal by non-acoustic sensor information. A system may receive and analyze an acoustic signal and information from a non-acoustic sensor, and process the acoustic signal based on the sensor information. 
     In some embodiments, the present technology provides methods for audio processing that may include receiving a first acoustic signal from a microphone. Information from a non-acoustic sensor may be received. The acoustic signal may be modified based on an analysis of the acoustic signal and the sensor information. 
     In some embodiments, the present technology provides systems for audio processing of an acoustic signal that may include a first microphone, a first sensor, and one or more executable modules that process the acoustic signal. The first microphone transduces an acoustic signal, wherein the acoustic signal includes a desired component and an undesired component. The first sensor provides non-acoustic sensor information. The one or more executable modules process the acoustic signal based on the non-acoustic sensor information. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  illustrates an environment in which embodiments of the present technology may be practiced. 
         FIG. 2  is a block diagram of an exemplary communication device. 
         FIG. 3  is a block diagram of an exemplary audio processing system. 
         FIG. 4  is a chart illustrating equalization curves for signal modification. 
         FIG. 5A  illustrates orientation-dependent receptivity of a communication device in a vertical orientation. 
         FIG. 5B  illustrates orientation-dependent receptivity of a communication device in a horizontal orientation. 
         FIG. 6  illustrates a flow chart of an exemplary method for audio processing. 
     
    
    
     DESCRIPTION OF EXEMPLARY EMBODIMENTS 
     The present technology provides audio processing of an acoustic signal based at least in part on non-acoustic sensor information. By analyzing not only an acoustic signal but also information from a non-acoustic sensor, processing of the audio signal may be improved. The present technology can be applied in single-microphone systems and multi-microphone systems that transform acoustic signals to the frequency domain, to the cochlear domain, or any other domain. The processing based on non-acoustic sensor information allows the present technology to be more robust and provide a higher quality audio signal in environments where the system or any acoustic sources are subject to motion during use. 
     Audio processing as performed in the context of the present technology may be used in noise reduction systems, including noise cancellation and noise suppression. A brief description of both noise cancellation systems and noise suppression systems is provided below. Note that the audio processing system discussed herein may use both. 
     Noise reduction may be implemented by subtractive noise cancellation or multiplicative noise suppression. Noise cancellation may be based on null processing, which involves cancelling an undesired component in an acoustic signal by attenuating audio from a specific direction, while simultaneously preserving a desired component in an acoustic signal, e.g. from a target location such as a main speaker. Noise suppression may use gain masks multiplied against a sub-band acoustic signal to suppress the energy levels of noise (i.e. undesired) components in the sub-band signals. Both types of noise reduction systems may benefit from implementing the present technology. 
     Information from the non-acoustic sensor may be used to determine one or more audio processing system parameters. Examples of system parameters that may be modified based on non-acoustic sensor data are gain (PreGain Amplifier or PGA control parameters and/or Digital Gain control of primary and secondary microphones), inter-level difference (ILD) equalization, directionality coefficients (for null processing), and thresholds or other factors that control the classification of echo vs. noise and noise vs. speech. 
     An audio processing system using spatial information, for example to separate multiple audio sources, may be susceptible to a change in the relative position of the communication device that includes the audio processing system. Decreasing this susceptibility is referred to as increasing the positional robustness. The operating assumptions and parameters of the underlying algorithm that are implemented by an audio processing system need to be changed according to the new relative position of the communication device that incorporates the audio processing system. Analyzing only acoustic signals may lead to ambiguity about the current operating conditions or a slow response to a change in the current operating conditions of an audio processing system. Incorporating information from one or more non-acoustic sensors may remove some or all of the ambiguity and/or improve response time and therefore improve the effectiveness and/or quality of the system. 
       FIG. 1  illustrates an environment  100  in which embodiments of the present technology may be practiced.  FIG. 1  includes audio source  102 , exemplary communication device  104 , and noise source  110 . The audio source  102  may be a user speaking in the vicinity of a communication device  104 . Audio from the user or main talker may be called main speech. The exemplary communication device  104  as illustrated includes two microphones: a primary microphone  106  and a secondary microphone  108  located a distance away from the primary microphone  106 . In other embodiments, the communication device  104  may include one or more than two microphones, such as for example three, four, five, six, seven, eight, nine, ten or even more microphones. 
     The primary microphone  106  and secondary microphone  108  may be omni-directional microphones. Alternatively, embodiments may utilize other forms of microphones or acoustic sensors/transducers. While the microphones  106  and  108  receive and transduce sound (i.e. an acoustic signal) from audio source  102 , microphones  106  and  108  also pick up noise  110 . Although noise  110  is shown coming from a single location in  FIG. 1 , it may comprise any undesired sounds from one or more locations different from audio source  102 , and may include sounds produced by a loudspeaker associated with device  104 , and may also include reverberations and echoes. Noise  110  may be stationary, non-stationary, and/or a combination of both stationary and non-stationary. Echo resulting from a far-end talker is typically non-stationary. 
     Some embodiments may utilize level differences (e.g. energy differences) between the acoustic signals received by microphones  106  and  108 . Because primary microphone  106  may be closer to audio source  102  than secondary microphone  108 , the intensity level is higher for primary microphone  106 , resulting in a larger energy level received by primary microphone  106  when the main speech is active, for example. The inter-level difference (ILD) may be used to discriminate speech and noise. An audio processing system may use a combination of energy level differences and time delays to identify speech components. An audio processing system may additionally use phase differences between the signals coming from different microphones to distinguish noise from speech, or distinguish one noise source from another noise source. Based on analysis of such inter-microphone differences, which can be referred to as binaural cues, speech signal extraction or speech enhancement may be performed. 
       FIG. 2  is a block diagram of an exemplary communication device  104 . In exemplary embodiments, communication device  104  (also shown in  FIG. 1 ) is an audio receiving device that includes a receiver  200 , a processor  202 , a primary microphone  106 , a secondary microphone  108 , an audio processing system  210 , a non-acoustic sensor  120 , and an output device  206 . Communication device  104  may comprise more or other components necessary for its operations. Similarly, communication device  104  may comprise fewer components that perform similar or equivalent functions to those depicted in  FIG. 2 . Additional details regarding each of the elements in  FIG. 2  is provided below. 
     Processor  202  in  FIG. 2  may include hardware and/or software which implements the processing function, and may execute a program stored in memory (not pictured in  FIG. 2 ). Processor  202  may use floating point operations, complex operations, and other operations. The exemplary receiver  200  may be configured to receive a signal from a communication network. In some embodiments, the receiver  200  may include an antenna device (not shown) for communicating with a wireless communication network, such as for example a cellular communication network. The signals received by receiver  200  and microphones  106  and  108  may be processed by audio processing system  210  and provided as output by output device  206 . For example, audio processing system  210  may implement noise reduction techniques on the received signals. The present technology may be used in both the transmit path and receive path of a communication device. 
     Non-acoustic sensor  120  may measure a spatial position or change in position of a microphone relative to the spatial position of an audio source, such as the mouth of a main speaker (a.k.a the “Mouth Reference Point” or MRP). The information measured by non-acoustic sensor  120  may be provided to processor  202  or stored in memory. As the microphone moves relative to the MRP, processing of the audio signal may be adapted accordingly. Generally, a non-acoustic sensor  120  may be implemented as a motion sensor, a (visible or infra-red) light sensor, a proximity sensor, a gyroscope, a level sensor, a compass, a Global Positioning System (GPS) unit, or an accelerometer. Alternatively, an embodiment of the present technology may combine sensor information of multiple non-acoustic sensors to determine when and how to modify the acoustic signal, or modify and/or select any system parameter of the audio processing system. 
     Audio processing engine  210  in  FIG. 2  may furthermore be configured to receive acoustic signals from an acoustic source via the primary and secondary microphones  106  and  108  (e.g., primary and secondary acoustic sensors) and process the acoustic signals. Primary and secondary microphones  106  and  108  may be spaced a distance apart such that acoustic waves impinging on the device from certain directions have different energy levels at the two microphones. After reception by microphones  106  and  108 , the acoustic signals may be converted into electric signals (i.e., a primary electric signal and a secondary electric signal). These electric signals may themselves be converted by an analog-to-digital converter (not shown) into digital signals for processing in accordance with some embodiments. In order to differentiate the acoustic signals, the acoustic signal received by primary microphone  106  is herein referred to as the primary acoustic signal, while the acoustic signal received by secondary microphone  108  is herein referred to as the secondary acoustic signal. Embodiments of the present invention may be practiced with any number of microphones/audio sources. 
     In various embodiments, where the primary and secondary microphones are omni-directional microphones that are closely spaced (e.g., 1-2 cm apart), a beamforming technique may be used to simulate a forward-facing and a backward-facing directional microphone response. A level difference may be obtained using the simulated forward-facing and the backward-facing directional microphone. The level difference may be used to discriminate speech and noise in e.g. the time-frequency domain, which can be used in noise and/or echo reduction. 
     Output device  206  in  FIG. 2  is any device that provides an audio output to a listener. For example, the output device  206  may comprise a speaker, an earpiece of a headset, or handset on communication device  104 . In some embodiments, the acoustic signals from output device  206  may be included as part of the (primary or secondary) acoustic signal recorded by microphones  106  and  108 . This may cause echoes, which are generally undesirable. The primary acoustic signal and the secondary acoustic signal may be processed by audio processing system  210  to produce a signal with an improved audio quality for transmission across a communications network and/or routing to output device  206 . The present technology may be used, e.g. in audio processing system  210 , to improve the audio quality of the primary and secondary acoustic signal. 
     Embodiments of the present invention may be practiced on any device configured to receive and/or provide audio such as, but not limited to, cellular phones, phone handsets, headsets, and systems for teleconferencing applications. While some embodiments of the present technology are described in reference to operation on a cellular phone, the present technology may be practiced on any communication device. 
     Some or all of the above-described modules in  FIG. 2  may be comprised of instructions that are stored on storage media. The instructions can be retrieved and executed by the processor  202 . Some examples of instructions include software, program code, and firmware. Some examples of storage media comprise memory devices and integrated circuits. The instructions are operational when executed by processor  202  to direct processor  202  to operate in accordance with embodiments of the present invention. Those skilled in the art are familiar with instructions, processor(s), and (computer readable) storage media. 
       FIG. 3  is a block diagram of an exemplary audio processing system  210 . In exemplary embodiments, the audio processing system  210  (also shown in  FIG. 2 ) may be embodied within a memory device inside communication device  104 . Audio processing system  210  may include a frequency analysis module  302 , a feature extraction module  304 , a source inference engine module  306 , a mask generator module  308 , noise canceller (Null Processing Noise Subtraction or NPNS) module  310 , modifier module  312 , and reconstructor module  314 . Descriptions for these modules are provided below. 
     Audio processing system  210  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. 
     Data provided by non-acoustic sensor  120  ( FIG. 2 ) may be used in audio processing system  210 , for example by analysis path sub-system  320 . This is illustrated in  FIG. 3  by sensor data  325 , which may be provided by non-acoustic sensor  120 , leading into analysis path sub-system  320 . Utilization of non-acoustic sensor information is discussed in more detail below, for example with respect to Noise Canceller  310  and the equalization charts of  FIG. 4 . 
     In the audio processing system of  FIG. 3 , acoustic signals received from primary microphone  106  and secondary microphone  108  are converted to electrical signals, and the electrical signals are processed by frequency analysis module  302 . In one embodiment, frequency analysis module  302  takes the acoustic signals and mimics the frequency analysis of the cochlea (e.g., cochlear domain), simulated by a filter bank. Frequency analysis module  302  separates each of the primary and secondary acoustic signals 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 frequency analysis module  302 . Alternatively, other filters 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. 
     Because most sounds (e.g. acoustic signals) are complex and include more than one frequency, a sub-band analysis of the acoustic signal determines what individual frequencies are present in each sub-band of the complex acoustic signal during a frame (e.g. a predetermined period of time). For example, the duration of a frame may be 4 ms, 8 ms, or some other length of time. Some embodiments may not use a frame at all. Frequency analysis module  302  may provide sub-band signals in a fast cochlea transform (FCT) domain as an output. 
     Frames of sub-band signals are provided by frequency analysis module  302  to an analysis path sub-system  320  and to a signal path sub-system  330 . Analysis path sub-system  320  may process a signal to identify signal features, distinguish between speech components and noise components of the sub-band signals, and generate a signal modifier. Signal path sub-system  330  modifies sub-band signals of the primary acoustic signal, e.g. by applying a modifier such as a multiplicative gain mask or a filter, or by using subtractive signal components as may be generated in analysis path sub-system  320 . The modification may reduce undesired components (i.e. noise) and preserve desired speech components (i.e. main speech) in the sub-band signals. 
     Noise suppression can use gain masks multiplied against a sub-band acoustic signal to suppress the energy levels of noise (i.e. undesired) components in the subband signals. This process is also referred to as multiplicative noise suppression. In some embodiments, acoustic signals can be modified by other techniques, such as a filter. The energy level of a noise component may be reduced to less than a residual noise target level, which may be fixed or slowly time-varying. A residual noise target level may for example be defined as a level at which the noise component ceases to be audible or perceptible, below a self-noise level of a microphone used to capture the acoustic signal, or below a noise gate of a component such as an internal Automatic Gain Control (AGC) noise gate or baseband noise gate within a system used to perform the noise cancellation techniques described herein. 
     Signal path sub-system  330  within audio processing system  210  of  FIG. 3  includes NPNS module  310  and modifier module  312 . NPNS module  310  receives sub-band frame signals from frequency analysis module  302 . NPNS module  310  may subtract (e.g., cancel) an undesired component (i.e. noise) from one or more sub-band signals of the primary acoustic signal. As such, NPNS module  310  may output sub-band estimates of noise components in the primary signal and sub-band estimates of speech components in the form of noise-subtracted sub-band signals. 
     NPNS module  310  within signal path sub-system  330  may be implemented in a variety of ways. In some embodiments, NPNS module  310  may be implemented with a single NPNS module. Alternatively, NPNS module  310  may include two or more NPNS modules, which may be arranged for example in a cascaded fashion. NPNS module  310  can provide noise cancellation for two-microphone configurations, for example based on source location, by utilizing a subtractive algorithm. It can also provide echo cancellation. Since noise and echo cancellation can usually be achieved with little or no voice quality degradation, processing performed by NPNS module  310  may result in an increased signal-to-noise-ratio (SNR) in the primary acoustic signal received by subsequent post-filtering and multiplicative stages, some of which are shown elsewhere in  FIG. 3 . The amount of noise cancellation performed may depend on the diffuseness of the noise source and the distance between microphones. These both contribute towards the coherence of the noise between the microphones, with greater coherence resulting in better cancellation by the NPNS module. 
     An example of null processing noise subtraction performed in some embodiments by the NPNS module  310  is disclosed in U.S. application Ser. No. 12/422,917, entitled “Adaptive Noise Cancellation,” filed Apr. 13, 2009, which is incorporated herein by reference. 
     Noise cancellation may be based on null processing, which involves cancelling an undesired component in an acoustic signal by attenuating audio from a specific direction, while simultaneously preserving a desired component in an acoustic signal, e.g. from a target location such as a main speaker. The desired audio signal may be a speech signal. Null processing noise cancellation systems can determine a vector that indicates the direction of the source of an undesired component in an acoustic signal. This vector is referred to as a spatial “null” or “null vector.” Audio from the direction of the spatial null is subsequently reduced. As the source of an undesired component in an acoustic signal moves relative to the position of the microphone(s), a noise reduction system can track the movement, and adapt and/or update the corresponding spatial null accordingly. 
     An example of a multi-microphone noise cancellation system which performs null processing noise subtraction (NPNS) is described in U.S. patent application Ser. No. 12/215,980, entitled “System and Method for Providing Noise Suppression Utilizing Null Processing Noise Subtraction,” filed Jun. 30, 2008, which is incorporated by reference herein. Noise subtraction systems can operate effectively in dynamic conditions and/or environments by continually interpreting the conditions and/or environment and adapting accordingly. 
     Information from non-acoustic sensor  120  may be used to control the direction of a spatial null in a noise canceller  310 . In particular, the non-acoustic sensor information may be used to direct a null in an NPNS module or a synthetic cardioid system based on positional information provided by sensor  120 . An example of a synthetic cardioid system is described in U.S. patent application Ser. No. 11/699,732, entitled “System and Method for Utilizing Omni-Directional Microphones for Speech Enhancement,” filed Jan. 29, 2007, which is incorporated by reference herein. 
     In a two-microphone directional system, coefficients σ and α may have complex values. The coefficients may represent the transfer functions from a primary microphone signal (P) to a secondary (S) microphone signal in a two-microphone representation. However, the coefficients may also be used in an N microphone system. The goal of the σ coefficient(s) is to cancel the speech signal component captured by the primary microphone from the secondary microphone signal. The cancellation can be represented as S-σP. The output of this subtraction is then an estimate of the noise in the acoustic environment. The α coefficient is used to cancel the noise from the primary microphone signal using this noise estimate. The ideal σ and α coefficients can be derived using adaptation rules, wherein adaptation may be necessary to point the σ null in the direction of the speech source and the α null in the direction of the noise. 
     In adverse SNR conditions, it becomes difficult to keep the system working optimally, i.e. optimally cancelling the noise and preserving the speech. In general, since speech cancellation is the most undesirable behavior, the system is tuned in order to minimize speech loss. Even with conservative tuning, however, noise leakage can occur. 
     As an alternative, a spatial map of the σ (and potentially α) coefficients can be created in the form of a table, comprising one set of coefficients per valid position. Each combination of coefficients may represent a position of the microphone(s) of the communication device relative to the MRP and/or a noise source. From the full set entailing all valid positions, an optimal set of values can be created, for example using the LBG algorithm. The size of the table may vary depending on the computation and memory resources available in the system. For example, the table could contain u and a coefficients describing all possible positions of the phone around the head. The table could then be indexed using three-dimensional and proximity sensor data. 
     Analysis path sub-system  320  in  FIG. 3  includes feature extraction module  304 , source interference engine module  306 , and mask generator module  308 . Feature extraction module  304  receives the sub-band frame signals derived from the primary and secondary acoustic signals provided by frequency analysis module  302  and receives the output of NPNS module  310 . The feature extraction module  304  may compute frame energy estimations of the sub-band signals, an inter-microphone level difference (ILD) between the primary acoustic signal and the secondary acoustic signal, and self-noise estimates for the primary and second microphones. Feature extraction module  304  may also compute other monaural or binaural features for processing by other modules, such as pitch estimates and cross-correlations between microphone signals. Feature extraction module  304  may both provide inputs to and process outputs from NPNS module  310 , as indicated by a double-headed arrow in  FIG. 3 . 
     Feature extraction module  304  may compute energy levels for the sub-band signals of the primary and secondary acoustic signal and an inter-microphone level difference (ILD) from the energy levels. The ILD may be determined by feature extraction module  304 . Determining energy level estimates and inter-microphone level differences is discussed in more detail in U.S. patent application Ser. No. 11/343,524, entitled “System and Method for Utilizing Inter-Microphone Level Differences for Speech Enhancement”, which is incorporated by reference herein. 
     Non-acoustic sensor information may be used to configure a gain of a microphone signal as processed, for example by feature extraction module  304 . Specifically, in multi-microphone systems that use ILD as a source discrimination cue, the level of the main speech decreases as the distance from the primary microphone to the MRP increases. If the distance from all microphones to the MRP increases, the ILD of the main speech decreases, resulting in less discrimination between the main speech and the noise sources. Such corruption of the ILD cue typically leads to undesirable speech loss. Increasing the gain of the primary microphone modifies the ILD in favor of the primary microphone. This results in less noise suppression, but improves positional robustness. 
     Another part of analysis path sub-system  320  is source inference engine module  306 , which may process frame energy estimations to compute noise estimates, and which may derive models of the noise and speech in the sub-band signals. The frame energy estimate processed in module  306  may include the energy estimates of the output of the frequency analysis  302  and of the noise canceller  310 . Source inference engine module  306  adaptively estimates attributes of the acoustic sources. The energy estimates may be used in conjunction with the speech models, noise models, and other attributes estimated in module  306  to generate a multiplicative mask in mask generator module  308 . 
     Source inference engine module  306  in  FIG. 3  may receive the ILD from feature extraction module  304  and track the ILD-probability distributions or “clusters” of audio source  102 , noise  110  and optionally echo. When ignoring echo, without any loss of generality, when the source and noise ILD-probability distributions are non-overlapping, it is possible to specify a classification boundary or dominance threshold between the two distributions. The classification boundary or dominance threshold is used to classify an audio signal as speech if the ILD is sufficiently positive or as noise if the ILD is sufficiently negative. The classification may be determined per sub-band and time frame and used to form a dominance mask as part of a cluster tracking process. 
     The classification may additionally be based on features extracted from one or more non-acoustic sensors, and as a result, the audio processing system may exhibit improved positional robustness. Source interference engine module  306  performs an analysis of sensor data  325 , depending on which system parameters are intended to be modified based on the non-acoustic sensor data. 
     Source interference engine module  306  may provide the generated classification to NPNS module  310 , and may utilize the classification to estimate noise in NPNS output signals. A current noise estimate along with locations in the energy spectrum where the noise may be located are provided for processing a noise signal within audio processing system  210 . Tracking clusters is described in U.S. patent application Ser. No. 12/004,897, entitled “System and method for Adaptive Classification of Audio Sources,” filed on Dec. 21, 2007, the disclosure of which is incorporated herein by reference. 
     Source inference engine module  306  may generate an ILD noise estimator and a stationary noise estimate. In one embodiment, the noise estimates are combined with a max( ) operation, so that the noise suppression performance resulting from the combined noise estimate is at least that of the individual noise estimates. The ILD noise estimate is derived from the dominance mask and the output of NPNS module  310 . 
     For a given normalized ILD, sub-band, and non-acoustical sensor information, a corresponding equalization function may be applied to the normalized ILD signal to correct distortion. The equalization function may be applied to the normalized ILD signal by either the source inference engine  306  or mask generator  308 . Using non-acoustical sensor information to apply an equalization function is discussed in more detail with respect to  FIG. 4 . 
     Mask generator module  308  of analysis path sub-system  320  may receive models of the sub-band speech components and/or noise components as estimated by source inference engine module  306 . Noise estimates of the noise spectrum for each sub-band signal may be subtracted out of the energy estimate of the primary spectrum to infer a speech spectrum. Mask generator module  308  may determine a gain mask for the sub-band signals of the primary acoustic signal and provide the gain mask to modifier module  312 . Modifier module  312  multiplies the gain masks and the noise-subtracted sub-band signals of the primary acoustic signal output by the NPNS module  310 , as indicated by the arrow from NPNS module  310  to modifier module  312 . Applying the mask reduces the energy levels of noise components in the sub-band signals of the primary acoustic signal and thus accomplishes noise reduction. 
     Values of the gain mask output from mask generator module  308  may be time-dependent and sub-band-signal-dependent, and may optimize noise reduction on a per sub-band basis. Noise reduction may be subject to the constraint that the speech loss distortion complies with a tolerable threshold limit. The threshold limit may be based on many factors. Noise reduction may be less than substantial when certain conditions, such as unacceptably high speech loss distortion, do not allow for more noise reduction. In various embodiments, the energy level of the noise component in the sub-band signal may be reduced to less than a residual noise target level. In some embodiments, the residual noise target level is the same for each sub-band signal. 
     Reconstructor module  314  converts the masked frequency sub-band signals from the cochlea domain back into the time domain. The conversion may include applying gains and phase shifts to the masked frequency sub-band signals adding the resulting signals. Once conversion to the time domain is completed, the synthesized acoustic signal may be provided to the user via output device  206  and/or provided to a codec for encoding. 
     In some embodiments, additional post-processing of the synthesized time domain acoustic signal may be performed. For example, comfort noise generated by a comfort noise generator may be added to the synthesized acoustic signal prior to providing the signal to the user. Comfort noise may be a uniform constant noise that is not usually discernable 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/or may be settable by a user. 
     The audio processing system of  FIG. 3  may process several types of signals in a communication device. The system may process signals, such as a digital Rx signal, received through an antenna or other connection. The system may also process sensor data from one or more non-acoustic sensors, such as a motion sensor, a light sensor, a proximity sensor, a gyroscope, a level sensor, a compass, a GPS unit, or an accelerometer. A non-acoustic sensor  120  is shown as part of communication device  104  in  FIG. 2 . By including non-acoustic sensor data  325  ( FIG. 3 ) as input to analysis path sub-system  320 , any of the modules contained therein may benefit and improve its efficiency and/or the quality of its outputs. Several examples of (audio processing) system parameter selection and/or modification in response to non-acoustic sensor information are presented below. 
     In some embodiments, noise may be reduced in acoustic signals received by audio processing system  210  by a system that adapts over time. Audio processing system  210  may perform noise suppression and noise cancellation using initial values of parameters, which may be adapted over time based on information received from non-acoustic sensor  120 , processing of the acoustic signal, and a combination of sensor  120  information and acoustic signal processing. 
     Non-acoustic sensor  120  may provide information to control application of an equalization function to ILD sub-band signals.  FIG. 4  is a chart  400  illustrating equalization curves for signal modification. When a system uses ILD information per sub-band to distinguish between desired and undesired components in an acoustic signal, ILD equalization per sub-band may be used to correct ILD distortion introduced by the acoustic characteristics of the head of the user providing the (desired) main speech. After equalization, the ILD for the main speech is ideally a known positive value. Regularized equalization improves the quality of the classification of main speech and undesired components in an acoustic signal. 
     The curves illustrated in  FIG. 4  may be associated with different detected positions, each curve representing a different equalization to apply to a normalized ILD. The usual position of a communication device and its microphones relative to the mouth of the user (or “Mouth Reference Point” or MRP) is called the nominal position (which could for example be defined by the axis going from the “Ear Reference Point” or ERP to the MRP). Two common ways to change the nominal position are rotating the communication device around the user&#39;s ear (i.e. around the ear point), along the vertical plane next to the user&#39;s head, and, secondly, tilting the microphone(s) of the communication device away from the user&#39;s mouth by pivoting around the user&#39;s ear. This rotation increases the distance from the MRP to the device&#39;s microphones, but does not increase the distance from the user&#39;s ear to the device&#39;s speaker significantly. 
       FIG. 4  illustrates exemplary ILD equalization (EQ) curves for five positions of the MRP relative to the device&#39;s microphones. The ILD EQ chart plots normalized ILD (y-axis) vs. frequency sub-bands (x-axis) as used in the cochlear domain. In  FIG. 4 , the legend at the bottom of the chart labels five positions ( 410 ,  420 ,  430 ,  440 , and  450 ) as: nominal position, rotated 30 degrees positive, rotated 30 degrees negative, pivoted 30 degrees positive, and pivoted 30 degrees negative respectively. Curve  415  is associated with position  410 , curve  425  with position  420 , curve  435  with position  430 , curve  445  with position  440 , and curve  455  with position  450 . When the communication device is moved from its nominal position, different EQ curves may thus be used for optimal correction of ILD distortion. Hence, for a given normalized ILD, sub-band, and positional information, a corresponding equalization function may be applied to the normalized ILD signal to correct distortion. The equalization function may be applied to the normalized ILD signal by either the source inference engine  306  or mask generator  308 . In one embodiment, positional information from non-acoustic sensors that include a relative spatial position, such as an angle of rotation or pivot, can be used to select the most appropriate curve from a plurality of ILD equalization arrays. 
     As discussed above with respect to source inference engine  306 , non-acoustic sensor information may be used to configure a gain of a microphone signal as processed, for example, by feature extraction module  304 . Specifically, in multi-microphones systems that use ILD as a source discrimination cue, the level of the main speech decreases as the distance from the primary microphone to the MRP increases. ILD cue corruption typically leads to undesirable speech loss. Increasing the gain of the primary microphone modifies the ILD in favor of the primary microphone. 
     Some of the scenarios in which the present technology may advantageously be leveraged are: detecting when a communication device is passed from a first user to a second user, detecting proximity variations due to a user&#39;s lip, jaw, and cheek motion and correlating that motion to active speech, leveraging a GPS sensor, and distinguishing speech vs. noise based on correlating accelerometer cues to distant sound sources while the communication device is in close proximity to the MRP. 
       FIG. 5A  illustrates orientation-dependent receptivity of a communication device in a vertical orientation. Devices  505  and  525  are shown using different viewing angles of a similar device having the shape of a rectangular prism (a.k.a a rectangle). Microphones  520  and  540  are the primary microphones located on the front of a device. Microphones  510  and  530  are the secondary microphones located on the back of a device. Device  505  is shown vertically from the side, whereas device  525  is shown vertically from the front, such that microphone  530  is obscured from view by the body of device  525 . Cone  506  indicates the area of highest receptivity for the position of device  505 , and extends in the third dimension (perpendicular to the page) by rotating cone  506  around the center of device  505 , creating a torus extending horizontally around device  505 . Similarly, for device  525 , its area of highest receptivity is indicated by cone  526 , which extends in the third dimension towards the reader, rotated horizontally, perpendicular to the page, around device  525 , creating a torus. When device  505  or  525  is thus positioned vertically, moving the MRP from its nominal position from left to right or vice-versa effects the processing of the received acoustic signal differently than moving the MRP up or down from its nominal position. Sensor information from non-acoustic sensors may be used to counter such effects, or counter the change of a device from horizontal to vertical orientation or vice-versa. 
       FIG. 5B  illustrates orientation-dependent receptivity of a communication device in a horizontal orientation. Devices  555  and  575  are positioned sideways, for example as if devices  505  and  525  in  FIG. 5A  were rotated by 90 degrees towards the reader (in the third dimension, off the page) and anti-clockwise respectively. Device  555  and  575  are shown using different viewing angles of a similar device having the shape of a rectangular prism. Microphones  570  and  590  are the primary microphones located on the front of a device. Microphones  560  and  580  are the secondary microphones located on the back of a device. Device  555  is shown horizontally from the top, whereas device  575  is shown horizontally from the front, such that microphone  580  is obscured from view by the body of device  575 . Cone  556  indicates the area of highest receptivity for the position of device  555 , and extends in the third dimension (perpendicular to the page) as if the torus around device  505  were rotated by 90 degrees towards the reader (in the third dimension, off the page). Similarly, for device  575 , its area of highest receptivity is indicated by cone  576 , as if the torus around device  525  were rotated by 90 degrees anti-clockwise. When device  555  or  575  is thus positioned horizontally, moving the MRP from its nominal position from left to right or vice-versa effects the processing of the received acoustic signal differently than moving the MRP up or down from its nominal position. Sensor information from non-acoustic sensors may be used to counter such effects, or counter the change of a device from horizontal to vertical orientation or vice-versa. 
       FIG. 6  illustrates a flow chart of an exemplary method  600  for audio processing. An acoustic signal is received from a microphone at step  610 , which may be performed by microphone  106  ( FIG. 1 ) providing a signal to audio processing system  210  ( FIG. 3 ). The received acoustic signal is optionally transformed to the cochlear domain at step  620 . The transformation may be performed by frequency analysis module  302  in audio processing system  210  ( FIG. 3 ). Non-acoustic sensor information is received at step  630 , where the information may be provided by non-acoustic sensor  120  ( FIG. 2 ), and received as sensor data  325  in  FIG. 3  by analysis path sub-system  320 . The received, and optionally transformed, acoustic signal is modified based on an analysis of the received, and optionally transformed, acoustic signal and the received non-acoustic sensor information at step  640 , wherein the analysis and modification may be performed in conjunction by analysis path sub-system  320  and signal path sub-system  330  ( FIG. 3 ) in general, or any of the (sub-) modules included therein respectively. Adjustments of some system parameters such as gain may be performed outside of analysis path sub-system  320  and signal path sub-system  330 , but still within communication device  104 . 
     The present technology is described above with reference to exemplary embodiments. It will be apparent to those skilled in the art that various modifications may be made and other embodiments can be used without departing from the broader scope of the present technology. For example, embodiments of the present invention may be applied to any system (e.g., non speech enhancement system) utilizing acoustic echo cancellation (AEC). Therefore, these and other variations upon the exemplary embodiments are intended to be covered by the present invention.