PATENT DOCUMENT

Publication Number: US-9886966-B2
Application Number: US-201414536269-A
Country: US
Kind Code: B2

Title: System and method for improving noise suppression using logistic function and a suppression target value for automatic speech recognition

Abstract:
Method for improving noise suppression for ASR starts with a microphone receiving an audio signal including speech signal and noise signal. In each frame for frequency band of audio signal, a noise estimator detects ambient noise level and generates noise estimate value based on estimated ambient noise level, variable noise suppression target controller generates suppression target value using noise estimate value and logistic function, a gain value calculator generates a gain value based on suppression target value and noise estimate value, and combiner enhances the audio signal by the gain value to generate a clean audio signal in each frame for all frequency bands. Logistic function models desired noise suppression level that varies based on ambient noise level. Variable level of noise suppression includes low attenuation for low noise levels and progressively higher attenuation for higher noise level. Other embodiments are also described.

Claims:
The invention claimed is: 
     
       1. A method for improving noise suppression for automatic speech recognition (ASR) comprising:
 receiving by at least one microphone an audio signal including a speech signal and a noise signal; and 
 in each frame for each frequency band of the audio signal, 
 detecting by a noise estimator a level of ambient noise and generating a noise estimate value based on the estimated level of ambient noise, 
 generating a suppression target value by a variable noise suppression target controller using the noise estimate value and a logistic function that models a desired level of noise suppression that varies based on the ambient noise level, wherein the logistic function models the desired level of noise suppression to includes low attenuation for low ambient noise levels and progressively higher attenuation for higher ambient noise level, 
 generating a gain value by a gain value calculator based on the suppression target value and the noise estimate value, and 
 enhancing by a combiner the audio signal by the gain value to generate a clean audio signal for the frequency band. 
 
     
     
       2. The method of  claim 1 , further comprising:
 receiving by a frequency analyzer the audio signal from the at least one microphone, and 
 splitting the audio signal into a plurality of frequency bands. 
 
     
     
       3. The method of  claim 2 , further comprising:
 combining by a signal synthesizer the clean audio signals in each frame from all frequency bands to generate a clean audio output signal for ASR processing. 
 
     
     
       4. The method of  claim 3 , further comprising:
 receiving by a beamformer the audio signal from at least two microphones; and 
 steering the beamformer to capture a main speech signal. 
 
     
     
       5. The method of  claim 4 , further comprising:
 outputting from the beamformer the main speech signal to an amplifier; and 
 outputting from the amplifier an amplified main speech signal to an automatic gain controller. 
 
     
     
       6. The method of  claim 1 , wherein the noise estimator is a one-channel noise estimator or a two-channel noise estimator. 
     
     
       7. A method for improving automatic speech recognition (ASR) using a logistic function that models a desired level of noise suppression that varies based on ambient noise level comprising:
 receiving by at least one microphone an audio signal including a speech signal and a noise signal; and 
 in each frame of the audio signal, 
 detecting by a noise estimator a level of ambient noise and generating a single noise estimate value from all the frames based on the estimated level of ambient noise in each frequency band, 
 generating a single suppression target value by a variable noise suppression target controller using the noise estimate value and the logistic function that models the level of noise suppression that varies based on the ambient noise level, wherein the logistic function models the desired level of noise suppression to include low attenuation for low ambient noise levels and progressively higher attenuation for higher ambient noise level, 
 generating a gain value by a gain value calculator based on the suppression target value and the noise estimate value, and 
 enhancing by a combiner the audio signal by the gain value to generate a clean audio signal in the frame. 
 
     
     
       8. The method of  claim 7 , further comprising:
 receiving by a frequency analyzer the audio signal from the at least one microphone, and 
 splitting the audio signal into a plurality of frequency bands. 
 
     
     
       9. The method of  claim 8 , further comprising:
 combining by a signal synthesizer the clean audio signals in each frame from all frequency bands to generate a clean audio output signal for ASR processing. 
 
     
     
       10. The method of  claim 9 , further comprising:
 receiving by a beamformer the audio signal from at least two microphones; 
 steering the beamformer to capture a main speech signal; 
 outputting from the beamformer the main speech signal to an amplifier; 
 outputting from the amplifier the main speech signal to an automatic gain controller; and 
 outputting from the automatic gain controller the main speech signal to the frequency analyzer. 
 
     
     
       11. The method of  claim 7 , wherein the noise estimator is a one-channel noise estimator or a two-channel noise estimator. 
     
     
       12. A non-transitory computer-readable storage medium, having stored thereon instructions, which when executed by a processor, causes the processor to perform a method for improving noise suppression for automatic speech recognition (ASR), the method comprising:
 receiving an audio signal including a speech signal and a noise signal; and 
 in each frame for each frequency band of the audio signal, 
 detecting a level of ambient noise and generating a noise estimate value based on the estimated level of ambient noise, 
 generating a suppression target value using the noise estimate value and a logistic function that models a desired level of noise suppression that varies based on the ambient noise level, wherein the logistic function models the desired level of noise suppression to includes low attenuation for low ambient noise levels and progressively higher attenuation for higher ambient noise level, 
 generating a gain value based on the suppression target value and the noise estimate value, and 
 enhancing the audio signal by the gain value to generate a clean audio signal in each frame for each frequency band. 
 
     
     
       13. The non-transitory computer-readable storage medium of  claim 12 , further comprising instructions, which when executed by the processor, causes the processor to:
 split the audio signal into a plurality of frequency bands; and 
 combining the clean audio signals in each frame from all frequency bands to generate a clean audio output signal for ASR processing. 
 
     
     
       14. A system for improving noise suppression for automatic speech recognition (ASR) comprising:
 a noise estimator 
 to estimate a level of ambient noise in each frame for each frequency band of an audio signal, and 
 to generate a noise estimate value in each frame for each frequency band of the audio signal based on the estimated level of ambient noise, wherein the audio signal includes a speech signal; 
 a variable noise suppression target controller
 to generate a suppression target value in each frame for each frequency band of the audio signal using the noise estimate value and a logistic function that models a desired level of noise suppression that varies based on the ambient noise level, 
 wherein the desired level of noise suppression includes low attenuation for low ambient noise levels and progressively higher attenuation for higher ambient noise level; 
 
 a gain calculator
 to generate a gain value in each frame for each frequency band of the audio signal based on the suppression target value and the noise estimate value; and 
 
 a combiner
 to enhance the audio signal in each frame for each frequency by the corresponding gain value to generate a clean audio output signal in each frame for each frequency. 
 
 
     
     
       15. The system of  claim 14 , further comprising:
 a frequency analyzer to receive the audio signal from at least one microphone, and to split the audio signal into a plurality of frequency bands. 
 
     
     
       16. The system of  claim 15 , further comprising:
 a signal synthesizer to combine the clean audio signals in each frame from all frequency bands to generate a clean audio output signal for ASR processing. 
 
     
     
       17. The system of  claim 16 , further comprising:
 at least one beamformer coupled to at least two microphone, respectively, to receive the audio signal from the at least two microphones, respectively, and to output a main speech signal. 
 
     
     
       18. The system of  claim 17 , further comprising:
 an amplifier coupled to the at least one beamformer and an automatic gain controller, the amplifier to receive and amplify the main speech signal from the at least one beamformer, and to output an amplified main speech signal to the automatic gain controller. 
 
     
     
       19. The system of  claim 16 , further comprising:
 an automatic speech recognition (ASR) engine to receive the clean audio output signal from the signal synthesizer and to perform ASR on the clean audio output signal. 
 
     
     
       20. The system of  claim 14 ,
 wherein the noise estimator is a one-channel noise estimator or a two-channel noise estimator.

Description:
FIELD 
     An embodiment of the invention relate generally to a method of improving the noise suppression for automatic speech recognition by using a logistic function that models a desired level of noise suppression which varies based on ambient noise level. 
     BACKGROUND 
     Currently, a number of consumer electronic devices are adapted to receive speech via microphone ports or headsets. Typical examples of consumer electronic devices include a portable telecommunications device (mobile telephone), desktop computers, laptop computers, and tablet computers. The consumer electronic devices may also include automatic speech recognition (ASR) capabilities that process the received speech. 
     When using these electronic devices, however, a common complaint is that the speech captured by the microphone port or the headset includes environmental noise such as secondary speakers in the background or other background noises. This environmental noise often renders the user&#39;s speech unintelligible and thus, degrades the accuracy of the ASR. 
     SUMMARY 
     While noise reduction techniques, such as spectral subtraction and noise suppression, are applied to microphone signals before the signals are processed by automatic speech recognition (ASR) engine (or processor or controller) in an effort to reduce the environmental noise from the audio signal, the current noise reduction techniques degrade the accuracy of the ASR at high signal-to-noise ratios (SNR) (e.g., above 12 or 15 db SNR). Generally, the present invention relates to an electronic device, system and method for improving noise suppression for automatic speech recognition (ASR) by using a logistic function that models a desired level of noise suppression, which is ambient noise level dependent. 
     In one embodiment of the invention, a method for improving noise suppression for automatic speech recognition (ASR) is using a logistic function that models a desired level of noise suppression that varies based on ambient noise level. The method starts with at least one microphone receiving an audio signal that includes a speech signal and a noise signal. In each frame or in each frequency band of the audio signal, a noise estimator may then detect a level of ambient noise and generate a noise estimate value based on the estimated level of ambient noise. In each frame or in each frequency band of the audio signal, a suppression target controller may then generate a suppression target value by using the noise estimate value as well as the logistic function that models a desired level of noise suppression that varies based on the ambient noise level. The variable level of noise suppression may include a low attenuation for low noise levels and a progressively higher attenuation for higher noise level. In each frame or in each frequency band of the audio signal, a gain value calculator may then generate a gain value based on the suppression target value and the noise estimate value. A combiner may then enhance the audio signal by multiplying with gains usually smaller than one to generate a clean audio signal for the frame or for the frequency band. 
     In another embodiment, a non-transitory computer-readable storage medium has stored thereon instructions, which when executed by a processor, causes the processor to perform a method for improving noise suppression for automatic speech recognition (ASR). The method starts by receiving an audio signal including a speech signal and a noise signal. In each frame or in each frequency band of the audio signal, the processor (i) detects a level of ambient noise and generates a noise estimate value based on the estimated level of ambient noise, (ii) generates a suppression target value using the noise estimate value and a logistic function that models a desired level of noise suppression that varies based on the ambient noise level, (iii) generates a gain value based on the suppression target value and the noise estimate value. The variable level of noise suppression may include low attenuation for low noise levels and progressively higher attenuation for higher noise level. The processor may also enhance the audio signal by the gain value to generate a clean audio signal for the frame or for the frequency band. 
     In yet another embodiment, a system for improving noise suppression for automatic speech recognition (ASR). The system comprises a noise estimator, a variable noise suppression target controller, a gain calculator, and a combiner. The noise estimator may estimate a level of ambient noise for each frame or for each frequency band of an audio signal, and may generate a noise estimate value for each frame or for each frequency band of the audio signal based on the estimated level of ambient noise. The audio signal may include a speech signal. The variable noise suppression target controller may generate a suppression target value for each frame or for each frequency band of the audio signal using the noise estimate value and a logistic function that models a desired level of noise suppression that varies based on the ambient noise level. The variable level of noise suppression includes low attenuation for low noise levels and progressively higher attenuation for higher noise level. The gain calculator may generate a gain value for each frame or for each frequency band of the audio signal based on the suppression target value and the noise estimate value. The combiner may enhance the audio signal for each frame or for each frequency by the corresponding gain value to generate a clean audio output signal for each frame or for each frequency. In some embodiments, the system further comprises a signal splitter to receive the audio signal from the at least one microphone, and to split the audio signal into a plurality of frames or plurality of frequency bands. In this embodiment, the system further comprises a signal synthesizer to combine the clean audio signals for each frame or for each frequency to generate a clean audio output signal for ASR processing. In one embodiment, the system includes at least one beamformer coupled to the at least one microphone, respectively, to receive the audio signal from the at least one microphone, respectively, and to output a main speech signal. An amplifier may be coupled to the beamformer and the signal splitter to receive and amplify the main speech signal from the beamformer, and to output an amplified main speech signal to the signal splitter. In some embodiments, the system further comprises an automatic speech recognition (ASR) controller to receive the clean audio output signal from the signal synthesizer and to perform ASR on the clean audio output signal. 
     The above summary does not include an exhaustive list of all aspects of the present invention. It is contemplated that the invention includes all systems, apparatuses and methods that can be practiced from all suitable combinations of the various aspects summarized above, as well as those disclosed in the Detailed Description below and particularly pointed out in the claims filed with the application. Such combinations may have particular advantages not specifically recited in the above summary. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The embodiments of the invention are illustrated by way of example and not by way of limitation in the figures of the accompanying drawings in which like references indicate similar elements. It should be noted that references to “an” or “one” embodiment of the invention in this disclosure are not necessarily to the same embodiment, and they mean at least one. In the drawings: 
         FIG. 1  illustrates an example of the headset in use according to one embodiment of the invention. 
         FIG. 2  illustrates a block diagram of a system for improving noise suppression for automatic speech recognition (ASR) according to one embodiment of the invention. 
         FIGS. 3A-B  illustrate block diagrams of the details of one of the noise control processing components in the system for improving noise suppression for ASR according to one embodiment of the invention ( FIG. 3A ) and the details of the noise control processing components including a common variable noise target controller component in the system for improving noise suppression for ASR according to one embodiment of the invention ( FIG. 3B ). 
         FIG. 4  illustrates a graph illustrating a logistic function that models a desired level of noise suppression (dB) that varies based on ambient noise power level (dB) according to one embodiment of the invention. 
         FIG. 5  illustrates a flow diagram of an example method for audio processing and noise reduction according to an embodiment of the invention. 
         FIG. 6  is a block diagram of exemplary components of an electronic device processing a user&#39;s voice in accordance with aspects of the present disclosure. 
     
    
    
     DETAILED DESCRIPTION 
     In the following description, numerous specific details are set forth. However, it is understood that embodiments of the invention may be practiced without these specific details. In other instances, well-known circuits, structures, and techniques have not been shown to avoid obscuring the understanding of this description. 
       FIG. 1  illustrates an example of the headset in use according to one embodiment of the invention. In  FIG. 1 , the electronic device  10  is illustrated as a mobile communication device such as a mobile telephone device or a smart phone. However, it is understood that the electronic device  10  may be other personal consumer electronic devices such as computers, laptops, and tablet computers, as described in  FIG. 6  below. As shown in  FIG. 1 , the user may hold the electronic device  10  to his ear (i.e., at-ear position) and the microphones in the electronic device  10  may receive his speech. The microphones may be air interface sound pickup devices that convert sound into an electrical signal. In some embodiments, the user may use a headset that includes a pair of earbuds and a headset wire. The user may place one or both the earbuds into his ears and the microphones in the headset may receive his speech. In still other embodiments, for instance speakerphone use case, the device may be at a significant distance from the user: at arm&#39;s length, on a table, and so forth. As the user is using the microphones included in the electronic device  10  or in the headset to transmit his speech, environmental noise may also be present (e.g., noise sources in  FIG. 1 ). Additionally, embodiments of the invention may also use other types of headsets. 
       FIG. 2  illustrates a block diagram of a system for improving noise suppression for automatic speech recognition (ASR) according to one embodiment of the invention. The system  200  may be included in the electronic device  10 . The system  200  includes at least one of a plurality of microphones  210   1 - 210   N  (N&gt;1), an optional beamformer  213 , an automatic gain control (AGC)  215 , a frequency analyzer  220 , a plurality of noise control processing components  230   1 - 230   k  (k&gt;1), a signal synthesizer  240 , and an automatic speech recognition (ASR) engine  250 . 
     The microphones  210   1 - 210   N  are air interface sound pickup devices that convert sound into an electrical signal. The microphones  210   1 - 210   N  pick up the acoustic signals corresponding to a main speech signal (e.g., user&#39;s voice) as well as ambient noise or environmental noise. The microphones  210   1 - 210   N  may also be distributed, for instance, along the headset wire, to form one or more microphone arrays. The microphone arrays may be used to create microphone array beams (i.e., beamformers), which can be steered to a given direction by emphasizing and deemphasizing selected microphones  210   1 - 210   N . In one embodiment, the system  200  includes at least one beamformer  213  coupled to at least two microphones to receive the audio signal from the at least two microphones, and to output a main speech signal. In one embodiment, the system  200  also includes an amplifier that is coupled to the beamformer, a beramformer  213 , an automatic gain control (AGC)  215 , and a frequency analyzer  220 . The amplifier receives and amplifies the main speech signal from the at least one microphone or one beamformer. The amplifier may output an amplified main speech signal to the frequency analyzer  220 . Similarly, the microphone arrays can also exhibit or provide nulls in other given directions. Accordingly, the beamforming process, also referred to as spatial filtering, may be a signal processing technique using the microphone array for directional sound reception. 
     As shown in  FIG. 2 , the AGC  215  module is coupled to the beamformer  213 , which is coupled to the microphones  210   1 - 210   N . In another embodiment, the AGC module  215  is coupled directly to one of the microphones  210   1 - 210   N . The AGC module  215  may receive the acoustic audio signal from at least one of the microphones  210   1 - 210   N  or from the beamformer and splits the audio signal into a plurality frequency bands (band 1 -band k ) (k&gt;1) in each short frame (or window) of time. In the frequency domain, the frequency analyzer  220  may be a filter bank that uses Fast Fourier Transforms (FFT) to split the audio signal into the frequency bands (band 1 -band k ). 
     The audio signals in each frame for each of the frequency bands are received by a plurality of noise control processing components  230   1 - 230   k  (k&gt;1), respectively. In some embodiments, the noise control processing components  230   1 - 230   k  may be combined and include common components used to process the audio signals in each frame for each of the frequency bands. The noise control processing components  230   1 - 230   k  respectively process the audio signals in each of the frames for each of the frequency bands to reduce the noise therein and respectively output clean audio signals in each of the frames for each of the frequency bands. The clean audio signals in each of the frames for each of the frequency bands are received by a signal synthesizer  240  that combines the clean audio signals in each of the frames for each of the frequency bands to generate a clean output audio signal for ASR processing. In the frequency domain, the signal synthesizer  240  may be a filter bank that applies inverse FFTs to synthesize clean audio signals from all of the frequency bands into a clean output audio signal. The automatic speech recognition (ASR) engine  250  may receive the clean output audio signal from the signal synthesizer  240  and performs ASR on the clean audio output. The ASR engine  250  may translate the user&#39;s speech included in the clean audio output into text inputs. 
       FIG. 3A  illustrates a block diagram of the details of one of the noise control processing components  230   1  in the system  200  for improving noise suppression for ASR according to one embodiment of the invention. It is understood that each of the plurality of noise control processing components  230   1 - 230   k  may include similar components that function similarly. As shown in  FIG. 3A , the noise control processing component  230   1  includes a noise estimator  310   1 , a variable noise target controller  320   1 , a gain controller  340   1  and a combiner  350   1 . The noise estimator  310   1  receives the audio signal in a first band (band 1 ) for each of the frames. The audio signal includes a speech signal and may include noise. The noise estimator  310   1  estimates a level of ambient noise for the current frame in the first frequency band (band 1 ) of an audio signal and generates a noise estimate value for the current frame for the first frequency band of the audio signal based on the estimated level of ambient noise.  FIG. 3B  illustrates a block diagram of the details of the noise control processing components  230   1 - 230   k  which have a common variable noise target controller  320  in the system  200  for improving noise suppression for ASR according to one embodiment of the invention. The common variable noise target controller  320  may sum or average the estimated noises from all the frequency bands. It is understood that each of the plurality of noise control processing components  230   1 - 230   k  may include similar components (e.g., noise estimator  310   1 - 310   k , gain controller  340   1 - 340   k , combiner  350   1 - 350   k ) that function similarly and that are coupled to a single common variable noise target controller  320 . In some embodiments, each of the noise control processing components  230   1 - 230   k  includes same common variable noise target controller  320  as illustrated in  FIG. 3B . As shown in  FIG. 3B , the noise control processing component  230   1 - 230   k  includes a noise estimator  310   1 - 310   k , a single (common) variable noise target controller  320  which sums or averages the noise estimates from all frequency bands, a gain controller  340   1 - 340   k  and a combiner  350   1 - 350   k . The noise estimators  310   1 - 310   k  receive the audio signal in the bands (band 1 -band k ) for each of the frames, respectively. The audio signal includes a speech signal and may include noise. The noise estimator  310   1 - 310   k  estimates a level of ambient noise for the current frame in each frequency band (band 1 -band k ) of an audio signal and generates a noise estimate value for the current frame for each of the frequency bands of the audio signal based on the estimated level of ambient noise. The noise estimators from all the frequency bands are combined into a single noise estimator in the common variable noise target controller  320 , which generates the same suppression target for all of the frequency bands. This common suppression target represents the maximum possible suppression and it is different from the actual suppression applied in each band by the gain combiner. 
     In one embodiment, the noise estimator  310   1  may be a one-channel or a two-channel noise estimator. Referring to  FIG. 2 , when only one microphone  210   1  is being used to transmit audio signals on one channel to the noise control processing component  230   1 , the noise estimator  310   1  may be a one-channel noise estimator that is a conventional single-channel or 1-mic noise estimator that is typically used with 1-mic or single-channel noise suppression systems. In such a system, the attenuation that is applied in the hope of suppressing noise (and not speech) may be viewed as a time varying filter that applies a time varying gain (attenuation) vector, to the single, noisy input channel, in the frequency domain. Typically, such a gain vector is based to a large extent on Wiener theory and is a function of the signal to noise ratio (SNR) estimate in each frequency bin. To achieve noise suppression, bins with low SNR are attenuated while those with high SNR are passed through unaltered, according to a well-known gain versus SNR curve. Such a technique tends to work well for stationary noise such as fan noise, far field crowd noise, or other relatively uniform acoustic disturbance. Non-stationary and transient noises, however, pose a significant challenge, which may be better addressed by embodiments that also include the two-channel noise estimator, which may be a more aggressive 2-mic estimator. 
     When there are at least two microphones  210   1 - 210   N  being used, the outputs from each of the microphones  210   1 - 210   N  may respectively correspond to a channel such that noise estimator  310   1  may be a two-channel noise estimator. In other words, when there are two audio or recorded sound channels, for use by various component blocks in the system  200 , each of these channels carries the audio signal from a respective one of the microphones  210   1 - 210   N . In the embodiment where beamformers are used, a voice beamformer (not shown) and a noise beamformer (not shown) may receive both the audio signals from the microphones  210   1 - 210   N . The voice beamformer and the noise beamformer perform beamforming to combine the audio signals from the microphones  210   1 - 210   N  to generate a voice beamformer signal and a noise beamformer signal, respectively. The voice beamformer (VB) signal and the noise beamformer (NB) signal are transmitted to noise estimator  310   1 . It is noted that in embodiments where beamforming is not used, the voice beamformer and the noise beamformer are not included in the system  200  such that the audio signals from the microphones  210   1 - 210   N  are directly inputted into the noise estimator  310   1 . 
     In another embodiment, the noise estimator  310  may include both the one-channel and a two-channel noise estimator. In this embodiment, the noise estimator  310  may also include a comparator and a selector to determine whether the one-channel or the two-channel noise estimator is to be used. In one embodiment, the one-channel noise estimator receives the VB signal from the voice beamformer while the two-channel noise estimator receives both the VB signal from the voice beamformer and the NB signal from the noise beamformer. It is noted that in embodiments where beamforming is not used, the noise estimators are respectively a 2-mic noise estimator and a 1-mic noise estimator. 
     In one embodiment, the two-channel and the one-channel noise estimators included in the noise estimator  310   1  may operate in parallel and generate their respective noise estimates by processing the audio signals received. In one instance, the two-channel noise estimator is more aggressive than the one-channel noise estimator in that it is more likely to generate a greater noise estimate, while the microphones are picking up a user&#39;s speech and background acoustic noise during a mobile phone call. 
     In another embodiment, the one-channel noise estimator is primarily a stationary noise estimator, whereas the two-channel noise estimator can do both stationary and non-stationary noise estimation. The two-channel noise estimator and one-channel noise estimator may also operate in parallel, where the term “parallel” here means that the sampling intervals or frames over which the audio signals are processed have to, for the most part, overlap in terms of absolute time. In one embodiment, the noise estimates produced by the two-channel noise estimator and the one-channel noise estimator are respective noise estimate vectors, where the vectors have several spectral noise estimate components, each being a value associated with a different audio frequency bin. This is based on a frequency domain representation of the discrete time audio signal, within a given time interval or frame. 
     Referring back to  FIG. 3A , the noise estimator  310   1  estimates a level of ambient noise for the current frame for the first frequency band (band 1 ) of an audio signal and generates a noise estimate value for the current frame for the first frequency band of the audio signal based on the estimated level of ambient noise. A variable noise target controller  320   1  receives the noise estimate value for the current frame for the first frequency band of the audio signal from the noise estimator  310   1 . The variable noise target controller  320   1  may include a logistic function storage  330  that may include one or more different types of storage such as hard disk drive storage, nonvolatile memory, and volatile memory such as dynamic random access memory. The logistic function storage  330  may store a logistic function that models the level of noise suppression that varies based on the ambient noise level. Referring to  FIG. 4 , one embodiment of a graph illustrating an example of a logistic function that models a desired level of noise suppression (dB) that varies based on the ambient noise power level, either logarithmic [dB] or linear is illustrated. 
     As shown in  FIG. 4 , the variable level of noise suppression includes low attenuation for low noise levels and progressively higher attenuation for higher noise level. In one embodiment, the inflection point is in the middle (12 dB) of the maximum suppression target specified (e.g. 24 dB). The logistic function may also model the variable multiplication factor for spectral subtraction. In other embodiments, in lieu of the logistic function, a linear function or a look up table approximation a different function may be used to provide very low attenuation for low noise levels and a progressively higher attenuation for higher noise levels. 
     The variable noise suppression target controller  320   1  may receive the noise estimate value (e.g., x-axis in  FIG. 4 ) from the noise estimator  310   1  and uses the logistic function to determine the suppression target value (e.g., y-axis in  FIG. 4 ). The variable noise suppression target controller  320   1  thus generates the suppression target value the current frame for the first frequency band (band 1 ) of the audio signal and transmits the suppression target value to the gain controller  340   1 . 
     In one embodiment, different types of functions (or different logistic functions) may be used for different types of noises estimated in each frame or in each frequency band. In this embodiment, the variable noise suppression target controller  320   1  includes a noise frame classification component that determines the type of noise estimate and associates a specific type of function (or a specific logistic function) with the type of noise estimate determined. Based on the type of noise estimate, the variable noise suppression target controller  320   1  applies the associated type of function or logistic function to obtain the suppression value. 
     Referring back to  FIG. 3 , the gain controller  340   1  receives the suppression target value for the current frame for the first frequency band (band 1 ) from the variable noise target controller  340   1  and the noise estimate value for the current frame for the first frequency band (band 1 ) from the noise estimator  310   1 . The gain controller  340   1  generates a gain value for the current frame for the first frequency band (band 1 ) of the audio signal based on the suppression target value and the noise estimate value. The combiner  350  receives for the current frame the first frequency band (band 1 ) of the audio signal from the frequency analyzer  220  and the gain value for the current frame for the first frequency band (band 1 ) from the gain controller  340   1 . The combiner  350  enhances the audio signal in the current frame for the first frequency band (band 1 ) by the gain value from the gain controller  340   1  to generate a clean audio output signal for the current frame for first frequency band (band 1 ). Referring back to  FIG. 2 , the outputs of each of the noise control processing components  230   1 - 230   k  are clean audio output signal for each of the current frame for each of the frequency bands (band 1 -band k ), respectively. 
     In one embodiment, the noise control processing component  230   1  applies noise reduction methods based on spectral subtraction. In this embodiment, the noise control processing component  230   1  applies a variable multiplication factor to the estimated noise to be subtracted. The variable multiplication factor may be between 0 and 1 and may be based on the level of ambient noise estimated by the spectral subtraction method. 
     In one embodiment, the noise control processing component  230   1  includes a memory device and a processor. The memory device may include one or more different types of storage such as hard disk drive storage, nonvolatile memory, and volatile memory such as dynamic random access memory. The processor may be a microprocessor, a microcontroller, a digital signal processor, or a central processing unit. The term “processor” may refer to a device having two or more processing units or elements, e.g. a CPU with multiple processing cores. The processor may be used to control the operations of the noise estimator  310 , variable noise target controller  320 , gain controller  340  and combiner  350  by executing software instructions or code stored in the memory device. 
     Moreover, the following embodiments of the invention may be described as a process, which is usually depicted as a flowchart, a flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed. A process may correspond to a method, a procedure, etc. 
       FIG. 5  illustrates a flow diagram of an example method  500  for improving noise suppression for automatic speech recognition (ASR) according to an embodiment of the invention. The method  500  starts with at least one microphone receiving an audio signal including a speech signal and a noise signal at Block  501 . In each frame for each frequency band of the audio signal, a noise estimator detects a level of ambient noise and generates a noise estimate value based on the estimated level of ambient noise at Block  502 . The noise estimator may be a one-channel noise estimator or a two-channel noise estimator. In each frame for each frequency band of the audio signal, at Block  503 , a variable noise suppression target controller generates a suppression target value using the noise estimate value and a logistic function that models a desired level of noise suppression that varies based on the ambient noise level. The variable level of noise suppression includes low attenuation for low noise levels and progressively higher attenuation for higher noise level. At Block  504 , in each frame for each frequency band of the audio signal, a gain value calculator generates a gain value based on the suppression target value and the noise estimate value. At Block  505 , in each frame for each frequency band of the audio signal, a combiner enhances the audio signal by the gain value to generate a clean audio signal for the frame or for the frequency band. 
     In one embodiment, a frequency analyzer receives the audio signal from the at least one microphone, and splits the audio signal for each frame into a plurality of frequency bands. In this embodiment, a signal synthesizer combines the clean audio signals for each frame from all frequency bands to generate a clean audio output signal for ASR processing. 
     A general description of suitable electronic devices for performing these functions is provided below with respect to  FIG. 6 . Specifically,  FIG. 6  is a block diagram depicting various components that may be present in electronic devices suitable for use with the present techniques. The electronic device may be in the form of a computer, a handheld portable electronic device, and/or a computing device having a tablet-style form factor. These types of electronic devices, as well as other electronic devices providing comparable speech recognition capabilities may be used in conjunction with the present techniques. 
     Keeping the above points in mind,  FIG. 6  is a block diagram illustrating components that may be present in one such electronic device  10 , and which may allow the device  10  to function in accordance with the techniques discussed herein. The various functional blocks shown in  FIG. 6  may include hardware elements (including circuitry), software elements (including computer code stored on a computer-readable medium, such as a hard drive or system memory), or a combination of both hardware and software elements. It should be noted that  FIG. 6  is merely one example of a particular implementation and is merely intended to illustrate the types of components that may be present in the electronic device  10 . For example, in the illustrated embodiment, these components may include a display  12 , input/output (I/O) ports  14 , input structures  16 , one or more processors  18 , memory device(s)  20 , non-volatile storage  22 , expansion card(s)  24 , RF circuitry  26 , and power source  28 . 
     In the embodiment of the electronic device  10  in the form of a computer, the embodiment include computers that are generally portable (such as laptop, notebook, tablet, and handheld computers), as well as computers that are generally used in one place (such as conventional desktop computers, workstations, and servers). 
     The electronic device  10  may also take the form of other types of devices, such as mobile telephones, media players, personal data organizers, handheld game platforms, cameras, and/or combinations of such devices. For instance, the device  10  may be provided in the form of a handheld electronic device that includes various functionalities (such as the ability to take pictures, make telephone calls, access the Internet, communicate via email, record audio and/or video, listen to music, play games, connect to wireless networks, and so forth). 
     In another embodiment, the electronic device  10  may also be provided in the form of a portable multi-function tablet computing device. In certain embodiments, the tablet computing device may provide the functionality of media player, a web browser, a cellular phone, a gaming platform, a personal data organizer, and so forth. 
     An embodiment of the invention may be a machine-readable medium having stored thereon instructions which program a processor to perform some or all of the operations described above. A machine-readable medium may include any mechanism for storing or transmitting information in a form readable by a machine (e.g., a computer), such as Compact Disc Read-Only Memory (CD-ROMs), Read-Only Memory (ROMs), Random Access Memory (RAM), and Erasable Programmable Read-Only Memory (EPROM). In other embodiments, some of these operations might be performed by specific hardware components that contain hardwired logic. Those operations might alternatively be performed by any combination of programmable computer components and fixed hardware circuit components. In one embodiment, the machine-readable medium includes instructions stored thereon, which when executed by a processor, causes the processor to perform the method for improving noise suppression for automatic speech recognition (ASR) by using a logistic function that models a desired level of noise suppression that varies based on ambient noise level as described above. 
     While the invention has been described in terms of several embodiments, those of ordinary skill in the art will recognize that the invention is not limited to the embodiments described, but can be practiced with modification and alteration within the spirit and scope of the appended claims. The description is thus to be regarded as illustrative instead of limiting. There are numerous other variations to different aspects of the invention described above, which in the interest of conciseness have not been provided in detail. Accordingly, other embodiments are within the scope of the claims.

Metadata:
Filing Date: 20141107
Publication Date: 20180206
Grant Date: 20180206
Priority Date: 20141107
Inventors: DUSAN SORIN V.
LINDAHL ARAM M.
Assignee: APPLE INC
CPC Classifications: [{"code": "B25J13/00", "inventive": false, "first": false, "tree": "[]"}, {"code": "G10L15/20", "inventive": true, "first": false, "tree": "[]"}, {"code": "G10L21/0208", "inventive": true, "first": true, "tree": "[]"}, {"code": "H04N7/15", "inventive": false, "first": false, "tree": "[]"}, {"code": "G10L21/0208", "inventive": true, "first": true, "tree": "[]"}, {"code": "G10L15/20", "inventive": true, "first": false, "tree": "[]"}, {"code": "B25J13/00", "inventive": false, "first": false, "tree": "[]"}, {"code": "H04N7/15", "inventive": false, "first": false, "tree": "[]"}]
Family ID: 55912720