Source: https://patents.google.com/patent/US9558755B1/en
Timestamp: 2019-12-11 23:35:31
Document Index: 344169653

Matched Legal Cases: ['Application No. 10', 'Application No. 20100001', 'Application No. 10', 'Application No. 097125481', 'Application No. 098121933', 'Application No. 10', 'Application No. 10', 'Application No. 2011', 'Application No. 20125600']

US9558755B1 - Noise suppression assisted automatic speech recognition - Google Patents
Noise suppression assisted automatic speech recognition Download PDF
US9558755B1
US9558755B1 US12/962,519 US96251910A US9558755B1 US 9558755 B1 US9558755 B1 US 9558755B1 US 96251910 A US96251910 A US 96251910A US 9558755 B1 US9558755 B1 US 9558755B1
US12/962,519
2010-05-20 Priority to US34685110P priority Critical
2010-12-07 Application filed by Knowles Electronics LLC filed Critical Knowles Electronics LLC
2011-05-02 Assigned to AUDIENCE, INC. reassignment AUDIENCE, INC. ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: LAROCHE, JEAN, MURGIA, CARLO
2017-01-31 Publication of US9558755B1 publication Critical patent/US9558755B1/en
230000001629 suppression Effects 0 abstract claims description title 69
Noise suppression information is used to optimize or improve automatic speech recognition performed for a signal. Noise suppression can be performed on a noisy speech signal using a gain value. The gain to apply to the noisy speech signal is selected to optimize speech recognition analysis of the resulting signal. The gain may be selected based on one or more features for a current sub band and time frame, as well as one or more features for other sub bands and/or time frames. Noise suppression information can be provided to a speech recognition module to improve the robustness of the speech recognition analysis. Noise suppression information can also be used to encode and identify speech.
This application claims the priority benefit of U.S. Provisional Application Ser. No. 61/346,851, titled “Noise Suppression Assisted Automatic Speech Recognition,” filed May 20, 2010, the disclosure of the aforementioned application is incorporated herein by reference.
Speech recognition systems have been used to convert spoken words into text. In medium and high noise environments, however, the accuracy of automatic speech recognition systems tends to degrade significantly. As a result, most speech recognition systems are used with audio captured in a noise-free environment.
Unlike speech recognition systems, a standard noise reduction strategy consists of strongly attenuating portions of the acoustic spectrum which are dominated by noise. Spectrum portions dominated by speech are preserved.
Strong attenuation of undesired spectrum portions is a valid strategy from the point of view of noise reduction and perceived output signal quality, it is not necessarily a good strategy for an automatic speech recognition system. In particular, the spectral regions strongly attenuated by noise suppression may have been necessary to extract features for speech recognition. As a result, the attenuation resulting from noise suppression corrupts the features of the speech signal more than the original noise signal. This corruption by the noise suppression of the speech signal, which is greater than the corruption caused by the added noise signal, causes the noise reduction algorithm to make automatic speech recognition results unusable.
The present technology may utilize noise suppression information to optimize or improve automatic speech recognition performed for a signal. Noise suppression may be performed on a noisy speech signal using a gain value. The gain to apply to the noisy signal as part of the noise suppression is selected to optimize speech recognition analysis of the resulting signal. The gain may be selected based on one or more features for a current sub band and time frame, as well as others. Noise suppression information may be provided to a speech recognition module to improve the robustness of the speech recognition analysis. Noise suppression information may also be used to encode and identify speech. Resources spent on automatic speech recognition such as a bit rate of a speech codec) may be selected based on the SNR.
An embodiment may enable processing of an audio signal. Sub-band signals may be generated from a received primary acoustic signal and a secondary acoustic signal. One or more features may be determined for a sub-band signal. Noise suppression information may be determined based the one or more features to a speech recognition module.
FIG. 1 illustrates an environment in which the present technology may be utilized.
FIG. 4 is a flow chart of an exemplary method for performing speech recognition based on noise suppression information.
FIG. 5 is a flow chart of an exemplary method for performing noise suppression on a sub band signal.
FIG. 6 is a flow chart of an exemplary method for providing noise suppression information to a speech recognition module.
The present technology may utilize noise suppression information to optimize or improve automatic speech recognition performed for a signal. Noise suppression may be performed on a noisy speech signal using a gain value. The gain to apply to the noisy signal as part of the noise suppression is selected to optimize speech recognition analysis of the resulting signal. The gain may be selected based on one or more features for a current sub band and time frame, as well as others.
Noise suppression information may be provided to a speech recognition module to improve the robustness of the speech recognition analysis. Noise suppression information may include voice activity detection (VAD) information, such as for example noise, an indication of whether a signal includes speech, an indication of a speech to noise ration (SNR) for a signal, and other information. Noise suppression information may also be used to encode and identify speech. Resources spent on automatic speech recognition such as a bit rate of a speech codec) may be selected based on the SNR.
Processor 202 may execute instructions and modules stored in a memory (not illustrated in FIG. 2) in the audio device 104 to perform functionality described herein, including noise reduction for an acoustic signal, speech recognition, and other functionality. Processor 202 may include hardware and software implemented as a processing unit, which may process floating point operations and other operations for the processor 202.
The exemplary receiver 200 may include an acoustic sensor configured to receive and transmit a signal to and from a communications network. In some embodiments, the receiver 200 may include an antenna device. The signal received may be forwarded to the audio processing system 210 to reduce noise using the techniques described herein, and provide an audio signal to the output device 206. Similarly, a signal received by one or more of primary microphone 106 and secondary microphone 108 may be processed for noise suppression and ultimately transmitted to a communications network via receiver 200. Hence, the present technology may be used in one or both of the transmit and receive paths of the audio device 104.
The audio processing system 210 is configured to receive the acoustic signals from an acoustic source via the primary microphone 106 and secondary microphone 108 (or a far-end signal via receiver 200) and process the acoustic signals. Processing may include performing noise reduction within an acoustic signal and speech recognition for an acoustic signal. The audio processing system 210 is discussed in more detail below.
The primary and secondary microphones 106, 108 may be spaced a distance apart in order to allow for detecting an energy level difference, time difference or phase difference between them. The acoustic signals received by primary microphone 106 and secondary microphone 108 may be converted into electrical signals (i.e. a primary electrical signal and a secondary electrical signal). The electrical 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 for clarity purposes, the acoustic signal received by the primary microphone 106 is herein referred to as the primary acoustic signal, while the acoustic signal received from by the secondary microphone 108 is herein referred to as the secondary acoustic signal. The primary acoustic signal and the secondary acoustic signal may be processed by the audio processing system 210 to produce a signal with an improved signal-to-noise ratio. It should be noted that embodiments of the technology described herein may be practiced utilizing only the primary microphone 106.
FIG. 3 is a block diagram of an exemplary audio processing system 210 for performing noise reduction and automatic speech recognition. In exemplary embodiments, the audio processing system 210 is embodied within a memory device within audio device 104. The audio processing system 210 may include a frequency analysis module 302, a feature extraction module 304, a source inference engine module 306, gain data store 307, mask selector module 308, noise canceller module 310, modifier module 312, reconstructor module 314, and automatic speech recognition 316. 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.
The frequency analysis module 302 receives the acoustic signals and mimics the frequency analysis of the cochlea (e.g., cochlear domain) to generate sub-band signals, simulated by a filter bank. The 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. The filter bank may be implemented by a series of cascaded, complex-valued, first-order IIR filters. Alternatively, other filters such as short-time Fourier transform (STFT), sub-band filter banks, modulated complex lapped transforms, cochlear models, wavelets, etc., can be used for the frequency analysis and synthesis. The samples of the frequency sub-band signals may be grouped sequentially into time frames (e.g. over a predetermined period of time). For example, the length of a frame may be 4 ms, 8 ms, or some other length of time. In some embodiments there may be no frame at all. The results may include sub-band signals in a fast cochlea transform (FCT) domain.
The sub-band frame signals are provided from frequency analysis module 302 to an analysis path sub-system 320 and a signal path sub-system 330. The analysis path sub-system 320 may process the signal to identify signal features, distinguish between speech components and noise components of the sub-band signals, and determine a signal modifier. The signal path sub-system 330 is responsible for modifying sub-band signals of the primary acoustic signal by reducing noise in the sub-band signals. Noise reduction can include applying a modifier, such as a multiplicative gain mask determined in the analysis path sub-system 320, or by subtracting components from the sub-band signals. The noise reduction may reduce noise and preserve the desired speech components in the sub-band signals.
Noise canceller module 310 may be implemented in a variety of ways. In some embodiments, noise canceller module 310 may be implemented with a single NPNS module. Alternatively, Noise canceller module 310 may include two or more NPNS modules, which may be arranged for example in a cascaded fashion.
The feature extraction module 304 of the analysis path sub-system 320 receives the sub-band frame signals derived from the primary and secondary acoustic signals provided by frequency analysis module 302 as well as the output of NPNS module 310. Feature extraction module 304 may compute frame energy estimations of the sub-band signals, inter-microphone level differences (ILD), inter-microphone time differences (ITD) and inter-microphones phase differences (IPD) between the primary acoustic signal and the secondary acoustic signal, self-noise estimates for the primary and second microphones, as well as other monaural or binaural features which may be utilized by other modules, such as pitch estimates and cross-correlations between microphone signals. The feature extraction module 304 may both provide inputs to and process outputs from NPNS module 310.
Source inference engine module 306 may include a noise estimate module which may receive a noise/speech classification control signal from the cluster tracker module and the output of noise canceller module 310 to estimate the noise N(t,w), wherein t is a point in time and W represents a frequency or sub-band. A speech to noise ratio (SNR) can be generated by source inference engine module 306 from the noise estimate and a speech estimate, and the SNR can be provided to other modules within the audio device, such as automatic speech recognition module 316 and mask selector 308.
Gain data store 307 may include one or more stored gain values and may communicate with mask selector 308. Each stored gain may be associated with a set of one or more features. An exemplary set of features may include a speech to noise ratio and a frequency (i.e., a center frequency for a sub band). Other feature data may also be stored in gain store 307. Each gain stored in gain data store 307 may, when applied to a sub-band signal, provide as close to a clean speech signal as possible. Though the gains provide a speech signal with a reduced amount of noise, they may not provide the perceptually most desirable sounding speech.
In some embodiments, each gain stored in gain store 307 may be optimized for a set of features, such as for example a particular frequency and speech to noise ratio. For example, to determine an optimal gain value for a particular combination of features, a known speech spectrum may be combined with noise at various speech to noise ratios. Because the energy spectrum and noise are known, a gain can be determined which suppress the combined speech-noise signal into a clean speech signal which is ideal for speech recognition. In some embodiments, the gain is configured to suppress the speech-noise signal such that noise is reduced but no portion of the speech signal is attenuated or degraded. These gains derived from the combined signals for a known SNR are stored in the gain data store for different combination of frequency and speech to noise ratio.
Mask selector 308 may receive a set of one or more features and/or other data from source inference engine 306, query gain data store 307 for a gain associated with a particular set of features and/or other data, and provide an accessed gain to modifier 312. For example, for a particular sub band, mask selector 308 may receive a particular speech to noise ratio from source inference engine 306 for the particular sub band in the current frame. Mask selector 308 may then query data store 307 for a gain that is associated with the combination of the speech to noise ratio and the current sub band center frequency. Mask selector 308 receives the corresponding gain from gain data store 307 and provides the gain to modifier 312.
The accessed gain may be applied to the estimated noise subtracted sub-band signals provided, for example as a multiplicative mask, by noise canceller 310 to modifier 312. The modifier module 312 multiplies the gain masks to the noise-subtracted sub-band signals of the primary acoustic signal output by the noise canceller module 310. Applying the mask reduces energy levels of noise components in the sub-band signals of the primary acoustic signal and results in noise reduction.
Modifier module 312 receives the signal path cochlear samples from noise canceller module 310 and applies a gain mask received from mask selector 308 to the received samples. The signal path cochlear samples may include the noise subtracted sub-band signals for the primary acoustic signal. The gain mask provided by mask selector 308 may vary quickly, such as from frame to frame, and noise and speech estimates may vary between frames. To help address the variance, the upwards and downwards temporal slew rates of the mask may be constrained to within reasonable limits by modifier 312. The mask may be interpolated from the frame rate to the sample rate using simple linear interpolation, and applied to the sub-band signals by multiplicative noise suppression. Modifier module 312 may output masked frequency sub-band signals.
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 may be settable by a user. In some embodiments, the mask generator module 308 may have access to the level of comfort noise in order to generate gain masks that will suppress the noise to a level at or below the comfort noise.
Automatic speech recognition module 316 may perform a speech recognition analysis on the reconstructed signal output by reconstructor 314. Automatic speech recognition module 316 may receive a voice activity detection (VAD) signal as well as a speech to noise (SNR) ratio indication or other noise suppression information from source inference engine 306. The information received from source information engine 306, such as the VAD and SNR, may be used to optimize the speech recognition process performed by automatic speech recognition module 316. Speech recognition module 316 is discussed in more detail below.
An exemplary system which may be used to implement at least a portion of audio processing system 210 is described in U.S. patent application Ser. No. 12/832,920, titled “Multi-Microphone Robust Noise Suppression,” filed Jul. 8, 2010, the disclosure of which is incorporated herein by reference
FIG. 4 is a flow chart of an exemplary method for performing speech recognition based on noise suppression information. First, a primary acoustic signal and a secondary acoustic signal are received at step 410. The signals may be received through microphones 106 and 108 of audio device 104. Sub band signals may then be generated from the primary acoustic signal and secondary acoustic signal at step 420. The received signals may be converted to sub band signals by frequency analysis module 302.
A feature is determined for a sub band signal at step 430. Feature extractor 304 may extract features for each sub band in the current frame or the frame as a whole. Features may include a speech energy level for a particular sub band noise level, pitch, and other features. Noise suppression information is then generated from the features at step 440. The noise suppression information may be generated and output by source inference engine 306 from features received from feature extraction module 304. The noise suppression information may include an SNR ratio for each sub band in the current frame, a VAD signal for the current frame, ILD, and other noise suppression information.
Noise suppression may be performed on a sub band signal based on noise suppression information at step 450. The noise suppression may include accessing a gain value based on one or more features and applying the gain to a sub band acoustic signal. Performing noise suppression on a sub band signal is discussed in more detail below with respect to the method of FIG. 5. Additionally, noise suppression performed on a sub band signal may include performing noise cancellation by noise canceller 310 in the audio processing system of FIG. 3.
Noise suppression information may be provided to speech recognition module 316 at step 460. Speech recognition module 316 may receive noise suppression information to assist with speech recognition. Providing noise suppression information to speech recognition module 316 is discussed in more detail below with respect to FIG. 6.
Speech recognition is automatically performed based on the noise suppression information at step 470. The speech recognition process may be optimized based on the noise suppression information. Performing speech recognition based on noise suppression information may include modulating a bit rate of a speech encoder or decoder based on a speech to noise ratio for a particular frame. In some embodiments, the bit rate is decreased when the speech to noise ratio is large. In some embodiments, speech recognition based on noise suppression may include setting a node search depth level by a speech recognition module based on a speech to noise ratio for a current frame. The node search depth level, for example, may be decreased when the speech to noise ratio is large.
FIG. 5 is a flow chart of an exemplary method for performing noise suppression on a sub band signal. The method of FIG. 5 provides more detail for step 450 in the method of FIG. 4. A speech to noise ratio (SNR) for a sub band is accessed at step 510. The SNR may be received by mask selector 308 from source inference engine 306. Mask selector 308 also has access to sub band information for the sub band being considered.
A gain which corresponds to the sub band signal frequency and the signal of the noise ratio is accessed at step 520. The gain is accessed by mask selector 308 from gain data store 307 and may correspond to a particular sub band signal frequency and SNR. The accessed gain is then applied to one or more sub band frequencies at step 530. The accessed gain may be provided to modifier 312 which then applies the gain to a sub band which may or may not be have undergone noise cancellation.
FIG. 6 is a flow chart of an exemplary method for providing noise suppression information to a speech recognition module. The method of FIG. 6 may provide more detail for step 460 than the method of FIG. 4. A determination as to whether speech is detected in a primary acoustic signal based on one or more features is performed at step 610. The detection may include detecting whether speech is or is not present within the signal within the current frame. In some embodiments, an SNR for the current sub band or for all sub bands may be compared to a threshold level. If the SNR is above the threshold value, then speech may be detected to be present in the primary acoustic signal. If the SNR is not above the threshold value, then speech may be determined to not be present in the current frame.
Each of steps 620-640 describe how speech recognition may be optimized based on noise suppression or noise suppression information and may be performed in combination or separately. Hence, in some embodiments, only one of step 620-640 may be performed. In some embodiments, more than just one of steps 620-640 may be performed when providing noise suppression information to a speech recognition module.
A speech recognition module is provided with a noise signal if a speech is not detected in a current frame of a signal at step 620. For example, if the determination is made that speech is not present in the current frame of a reconstructed signal, a noise signal is provided to acoustic speech recognition module 316 in order to ensure that no false positive for speech detection occurs. The noise signal may be any type of signal that has a high likelihood of not being mistaken for speech by the speech recognition module.
A speech recognition module may be provided with an indication that speech is present in the acoustic signal at step 630. In this case, automatic speech recognition module 316 may be provided with a VAD signal provided by source inference engine 306. The VAD signal may indicate whether or not speech is present in the signal provided to automatic speech recognition module 316. Automatic speech recognition module 316 may use the VAD signal to determine whether or not to perform speech recognition on the signal.
A speech to noise ratio (SNR) signal may be provided for the current frame and/or sub band to the speech recognition module at step 640. In this case, the SNR may provide a value within a range of values indicating whether or not speech is present. This may help the automatic speech recognition module learn when to expend resources to recognize speech and when not to.
generating sub-band signals from a received primary acoustic signal and a received secondary acoustic signal;
determining two or more features for the sub-band signals, the two or more features including a speech energy level for the sub-band noise level and at least one of the following: inter-microphone level differences, inter-microphone time differences, and inter-microphone phase differences between the primary acoustic signal and the secondary acoustic signal;
suppressing a noise component in the primary acoustic signal based on the two or more features, the suppressing configured to clean the primary acoustic signal to create a cleaned speech signal optimized for accurate speech recognition processing by an automatic speech recognition processing module, the suppressing comprising:
applying a gain to a sub-band of the primary acoustic signal to provide a noise suppressed signal, the applying comprising:
determining a speech to noise ratio (SNR) for the sub-band of the primary acoustic signal;
accessing the gain, based on the frequency of the sub-band and the determined SNR for the sub-band, from a datastore, the datastore including a plurality of pre-stored gains configured to create cleaned speech signals optimized for accurate speech recognition processing by the automatic speech recognition processing module, each pre-stored gain in the plurality of pre-stored gains associated with a corresponding frequency and an SNR value; and
applying the accessed gain to the sub-band frequency; and
providing the cleaned speech signal and corresponding noise suppression information to the automatic speech recognition processing module, the noise suppression information based on the two or more features and including a voice activity detection signal.
2. The method of claim 1, further comprising determining whether the primary acoustic signal includes speech, the determination performed based on the two or more features.
3. The method of claim 2, further comprising providing a noise signal to the automatic speech recognition processing module in response to detecting that the primary acoustic signal does not include speech.
4. The method of claim 2, wherein the voice activity detection signal is generated based on the determination of whether the primary acoustic signal includes speech, and the voice activity detection signal indicating whether automatic speech recognition is to occur.
5. The method of claim 4, wherein the voice activity detection signal is a value within a range of values corresponding to the level of speech detected in the primary acoustic signal.
6. The method of claim 2, wherein the noise suppression information includes a speech to noise ratio for the current time frame and the sub-band to the automatic speech recognition processing module.
7. The method of claim 1, wherein the noise suppression information includes a speech to noise ratio, the method further comprising modulating a bit rate of a speech encoder or decoder based on the speech to noise ratio for a particular frame.
8. The method of claim 1, wherein the noise suppression information includes a speech to noise ratio, the method further comprising setting a node search depth level by the automatic speech recognition processing module based on the speech to noise ratio for a current frame.
9. A non-transitory computer readable storage medium having embodied thereon a program, the program being executable by a processor to perform a method for reducing noise in an audio signal, the method comprising:
determining two or more features for a sub-band signal, the two or more features including a speech energy level for the sub-band noise level and at least one of the following: inter-microphone level differences, inter-microphone time differences, and inter-microphone phase differences between the primary acoustic signal and the secondary acoustic signal;
providing the cleaned speech signal and corresponding noise suppression information to the automatic speech recognition processing module, the noise suppression information based on the two or more features and including a speech to noise ratio for each of the sub-band signals and a voice activity detection signal.
10. The non-transitory computer readable storage medium of claim 9, further comprising providing a noise signal to the automatic speech recognition processing module in response to detecting that the primary acoustic signal does not include speech.
11. The non-transitory computer readable storage medium of claim 9, wherein the voice activity detection signal is generated based on the determination of whether the primary acoustic signal includes speech, and the voice activity detection signal indicating whether automatic speech recognition is to occur.
12. The non-transitory computer readable storage medium of claim 9, wherein the noise suppression information includes a speech to noise ratio for the current time frame and the sub-band to the automatic speech recognition processing module.
13. The non-transitory computer readable storage medium of claim 9, wherein the noise suppression information includes a speech to noise ratio, the method further comprising modulating a bit rate of a speech encoder or decoder based on the speech to noise ratio for a particular frame.
14. The non-transitory computer readable storage medium of claim 9, wherein the noise suppression information includes a speech to noise ratio, the method further comprising setting a node search depth level by the automatic speech recognition processing module based on the speech to noise ratio for a current frame.
US12/962,519 2010-05-20 2010-12-07 Noise suppression assisted automatic speech recognition Active 2033-12-18 US9558755B1 (en)
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ID=57867636
US12/962,519 Active 2033-12-18 US9558755B1 (en) 2010-05-20 2010-12-07 Noise suppression assisted automatic speech recognition
US (1) US9558755B1 (en)
WO2018148095A1 (en) * 2017-02-13 2018-08-16 Knowles Electronics, Llc Soft-talk audio capture for mobile devices
JPH0553587A (en) 1991-08-26 1993-03-05 Nec Corp Device noise cancellation system
JPH07248793A (en) 1994-03-08 1995-09-26 Mitsubishi Electric Corp Noise suppressing voice analysis device, noise suppressing voice synthesizer and voice transmission system
US20020156624A1 (en) 2001-04-09 2002-10-24 Gigi Ercan Ferit Speech enhancement device
JP2002542689A (en) 1999-04-12 2002-12-10 テレフオンアクチーボラゲット エル エム エリクソン（パブル） Dual microphone due to signal noise reduction methods and apparatus using the spectral subtraction
JP2002366200A (en) 2001-06-06 2002-12-20 Mitsubishi Electric Corp Noise restricting device
US20040185804A1 (en) 2002-11-18 2004-09-23 Takeo Kanamori Microphone device and audio player
US6804651B2 (en) 2001-03-20 2004-10-12 Swissqual Ag Method and device for determining a measure of quality of an audio signal
US20050261894A1 (en) 2001-10-02 2005-11-24 Balan Radu V Method and apparatus for noise filtering
US20060136201A1 (en) 2004-12-22 2006-06-22 Motorola, Inc. Hands-free push-to-talk radio
JP2006337415A (en) 2005-05-31 2006-12-14 Nec Corp Method and apparatus for suppressing noise
WO2007001068A1 (en) 2005-06-28 2007-01-04 Matsushita Electric Industrial Co., Ltd. Sound classification system and method capable of adding and correcting a sound type
US20070033032A1 (en) 2005-07-22 2007-02-08 Kjell Schubert Content-based audio playback emphasis
TWI279776B (en) 2003-12-29 2007-04-21 Nokia Corp Method and device for speech enhancement in the presence of background noise
US20070110263A1 (en) 2003-10-16 2007-05-17 Koninklijke Philips Electronics N.V. Voice activity detection with adaptive noise floor tracking
JP2008015443A (en) 2006-06-07 2008-01-24 Nippon Telegr & Teleph Corp <Ntt> Apparatus, method and program for estimating noise suppressed voice quality
US20080059163A1 (en) 2006-06-15 2008-03-06 Kabushiki Kaisha Toshiba Method and apparatus for noise suppression, smoothing a speech spectrum, extracting speech features, speech recognition and training a speech model
US7376558B2 (en) 2004-05-14 2008-05-20 Loquendo S.P.A. Noise reduction for automatic speech recognition
US20080170703A1 (en) 2007-01-16 2008-07-17 Matthew Zivney User selectable audio mixing
US20080273476A1 (en) 2007-05-02 2008-11-06 Menachem Cohen Device Method and System For Teleconferencing
US20090177464A1 (en) 2000-05-19 2009-07-09 Mindspeed Technologies, Inc. Speech gain quantization strategy
US20100138220A1 (en) 2008-11-28 2010-06-03 Fujitsu Limited Computer-readable medium for recording audio signal processing estimating program and audio signal processing estimating device
US20100177916A1 (en) 2009-01-14 2010-07-15 Siemens Medical Instruments Pte. Ltd. Method for Determining Unbiased Signal Amplitude Estimates After Cepstral Variance Modification
US20110101654A1 (en) 2009-10-29 2011-05-05 Tk Holdings Inc. Steering wheel system with audio input
US20110261150A1 (en) 2010-04-23 2011-10-27 Ashish Goyal Selective Audio Combination for a Conference
US8081878B1 (en) 2004-08-18 2011-12-20 Qualcomm Atheros, Inc. Remote control capture and transport
US20120063609A1 (en) 2009-06-02 2012-03-15 Koninklijke Philips Electronics N.V. Acoustic multi-channel cancellation
US8155953B2 (en) 2005-01-12 2012-04-10 Samsung Electronics Co., Ltd. Method and apparatus for discriminating between voice and non-voice using sound model
US20120087514A1 (en) 2010-10-07 2012-04-12 Motorola, Inc. Method and apparatus for remotely switching noise reduction modes in a radio system
US20120220347A1 (en) 2011-02-28 2012-08-30 Nokia Corporation Handling a voice communication request
US20130024190A1 (en) 2010-04-27 2013-01-24 James Christopher Fairey Systems and methods for speech processing
US20130196715A1 (en) 2012-01-30 2013-08-01 Research In Motion Limited Adjusted noise suppression and voice activity detection
US20130268280A1 (en) 2010-12-03 2013-10-10 Friedrich-Alexander-Universitaet Erlangen-Nuernberg Apparatus and method for geometry-based spatial audio coding
EP1536660B1 (en) 2003-11-27 2013-10-23 Motorola Solutions, Inc. Communication system, communication units, and method of ambience listening thereto
US20140098964A1 (en) 2012-10-04 2014-04-10 Siemens Corporation Method and Apparatus for Acoustic Area Monitoring by Exploiting Ultra Large Scale Arrays of Microphones
WO2014131054A2 (en) 2013-02-25 2014-08-28 Audience, Inc. Dynamic audio perspective change during video playback
US8880396B1 (en) * 2010-04-28 2014-11-04 Audience, Inc. Spectrum reconstruction for automatic speech recognition
US20140337016A1 (en) 2011-10-17 2014-11-13 Nuance Communications, Inc. Speech Signal Enhancement Using Visual Information
US20150030163A1 (en) 2013-07-25 2015-01-29 DSP Group Non-intrusive quality measurements for use in enhancing audio quality
US20150100311A1 (en) 2013-10-07 2015-04-09 Honeywell International Inc. System and method for correcting accent induced speech in an aircraft cockpit utilizing a dynamic speech database
2010-12-07 US US12/962,519 patent/US9558755B1/en active Active
EP1232496A1 (en) 1999-11-15 2002-08-21 Nokia Corporation Noise suppression
JP2003514473A (en) 1999-11-15 2003-04-15 ノキア コーポレイション Noise suppression
US20160027451A1 (en) 2006-01-30 2016-01-28 Audience, Inc. System and Method for Providing Noise Suppression Utilizing Null Processing Noise Subtraction
US20160066089A1 (en) 2006-01-30 2016-03-03 Audience, Inc. System and method for adaptive intelligent noise suppression
TW200910793A (en) 2007-07-06 2009-03-01 Audience Inc System and method for adaptive intelligent noise suppression
US20120179462A1 (en) 2007-07-06 2012-07-12 David Klein System and Method for Adaptive Intelligent Noise Suppression
KR101461141B1 (en) 2007-07-06 2014-11-13 오디언스 인코포레이티드 System and method for adaptively controlling a noise suppressor
WO2009008998A1 (en) 2007-07-06 2009-01-15 Audience, Inc. System and method for adaptive intelligent noise suppression
TWI463817B (en) 2007-07-06 2014-12-01 Audience Inc System and method for adaptive intelligent noise suppression
JP2010532879A (en) 2007-07-06 2010-10-14 オーディエンス，インコーポレイテッド Adaptive intelligent noise suppression system and method
KR20100041741A (en) 2007-07-06 2010-04-22 오디언스 인코포레이티드 System and method for adaptive intelligent noise suppression
FI124716B (en) 2007-07-06 2014-12-31 Audience Inc System and method for adaptive intelligent noise reduction
KR101610656B1 (en) 2008-06-30 2016-04-08 노우레스 일렉트로닉스, 엘엘시 System and method for providing noise suppression utilizing null processing noise subtraction
TW201009817A (en) 2008-06-30 2010-03-01 Audience Inc System and method for providing noise suppression utilizing null processing noise subtraction
JP5762956B2 (en) 2008-06-30 2015-08-12 オーディエンス，インコーポレイテッド System and method for providing noise suppression utilizing nulling denoising
TWI488179B (en) 2008-06-30 2015-06-11 Audience Inc System and method for providing noise suppression utilizing null processing noise subtraction
FI20100431A (en) 2008-06-30 2010-12-30 Audience Inc System and method for enabling interference cancellation using interference reduction processing
KR20110038024A (en) 2008-06-30 2011-04-13 오디언스 인코포레이티드 System and method for providing noise suppression utilizing null processing noise subtraction
JP2011527025A (en) 2008-06-30 2011-10-20 オーディエンス，インコーポレイテッド System and method for providing noise suppression utilizing nulling denoising
JP2013517531A (en) 2010-01-19 2013-05-16 オーディエンス，インコーポレイテッド Distortion measurement for noise suppression systems
WO2011091068A1 (en) 2010-01-19 2011-07-28 Audience, Inc. Distortion measurement for noise suppression system
KR20120116442A (en) 2010-01-19 2012-10-22 오디언스 인코포레이티드 Distortion measurement for noise suppression system
US20140112496A1 (en) 2012-10-19 2014-04-24 Carlo Murgia Microphone placement for noise cancellation in vehicles
Advisory Action, Jul. 29, 2016, U.S. Appl. No. 13/363,362, filed Jan. 31, 2012.
Advisory Action, Jul. 3, 2012, U.S. Appl. No. 12/215,980, filed Jun. 30, 2008.
Advisory Action, May 14, 2013, U.S. Appl. No. 12/868,622, filed Aug. 25, 2010.
Allowance mailed May 21, 2014 in Finland Patent Application 20100001, filed Jan. 4, 2010.
Bach et al., "Learning Spectral Clustering with application to spech separation", Journal of machine learning research, 2006.
Fazel et al., "An overview of statistical pattern recognition techniques for speaker verification," IEEE, May 2011.
Final Office Action, Apr. 10, 2012, U.S. Appl. No. 12/286,995, filed Oct. 2, 2008.
Final Office Action, Apr. 24, 2012, U.S. Appl. No. 12/215,980, filed Jun. 30, 2008.
Final Office Action, Apr. 28, 2011, U.S. Appl. No. 11/825,563, filed Jul. 6, 2007.
Final Office Action, Aug. 30, 2016, U.S. Appl. No. 14/838,133, filed Aug. 27, 2015.
Final Office Action, Dec. 15, 2015, U.S. Appl. No. 14/189,817, filed Feb. 25, 2014.
Final Office Action, Dec. 3, 2014, U.S. Appl. No. 13/859,186, filed Apr. 9, 2013.
Final Office Action, Dec. 30, 2013, U.S. Appl. No. 11/825,563, filed Jul. 6, 2007.
Final Office Action, Dec. 31, 2012, U.S. Appl. No. 13/426,436, filed Mar. 21, 2012.
Final Office Action, Feb. 22, 2013, U.S. Appl. No. 12/868,622, filed Aug. 25, 2010.
Final Office Action, Jan. 12, 2016, U.S. Appl. No. 12/959,994, filed Dec. 3, 2010.
Final Office Action, Jul. 11, 2014, U.S. Appl. No. 12/215,980, filed Jun. 30, 2008.
Final Office Action, Jun. 17, 2016, U.S. Appl. No. 13/396,568, filed Feb. 14, 2012.
Final Office Action, Mar. 16, 2016, U.S. Appl. No. 14/628,109, filed Feb. 20, 2015.
Final Office Action, May 5, 2016, U.S. Appl. No. 13/363,362, filed Jan. 31, 2012.
Final Office Action, Nov. 28, 2012, U.S. Appl. No. 13/424,189, filed Mar. 19, 2012.
Final Office Action, Oct. 23, 2013, U.S. Appl. No. 13/492,780, filed Jun. 8, 2012.
Final Office Action, Sep. 12, 2014, U.S. Appl. No. 13/363,362, filed Jan. 31, 2012.
Final Office Action, Sep. 23, 2014, U.S. Appl. No. 13/396,568, filed Feb. 14, 2012.
Final Office Action, Sep. 4, 2012, U.S. Appl. No. 13/424,189, filed Mar. 19, 2012.
Goodwin, Michael M. et al., "Key Click Suppression", U.S. Appl. No. 14/745,176, filed Jun. 19, 2015, 25 pages.
Hu et al., "Robust Speaker's Location Detection in a Vehicle Environment Using GMM Models," IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, vol. 36, No. 2, Apr. 2006, pp. 403-412.
International Search Report & Written Opinion dated Jul. 15, 2014 in Patent Cooperation Treaty Application No. PCT/US2014/018443, filed Feb. 25, 2014.
International Search Report & Written Opinion dated Mar. 18, 2014 in Patent Cooperation Treaty Application No. PCT/US2013/065752, filed Oct. 18, 2013.
International Search Report & Written Opinion dated Nov. 27, 2015 in Patent Cooperation Treaty Application No. PCT/US2015/047263, filed Aug. 27, 2015.
International Search Report and Written Opinion dated Aug. 27, 2009 in Patent Cooperation Treaty Application No. PCT/US2009/003813.
International Search Report and Written Opinion dated Oct. 1, 2008 in Patent Cooperation Treaty Application No. PCT/US2008/008249.
Kato et al., "Noise Suppression with High Speech Quality Based on Weighted Noise Estimation and MMSE STSA" Proc. IWAENC [Online] 2001, pp. 183-186.
Kim et al., "Improving Speech Intelligibility in Noise Using Environment-Optimized Algorithms," IEEE Transactions on Audio, Speech, and Language Processing, vol. 18, No. 8, Nov. 2010, pp. 2080-2090.
Klautau et al., "Discriminative Gaussian Mixture Models a Comparison with Kernel Classifiers," ICML, 2003.
Mokbel et al., "Automatic Word Recognition in Cars," IEEE Transactions of Speech and Audio Processing, vol. 3, No. 5, Sep. 1995, pp. 346-356.
Non-Final Office Action, Apr. 17, 2015, U.S. Appl. No. 13/888,796, filed May 7, 2013.
Non-Final Office Action, Apr. 19, 2016, U.S. Appl. No. 14/046,551, filed Oct. 4, 2013.
Non-Final Office Action, Apr. 24, 2013, U.S. Appl. No. 11/825,563, filed Jul. 6, 2007.
Non-Final Office Action, Apr. 8, 2016, U.S. Appl. No. 14/838,133, filed Aug. 27, 2015.
Non-Final Office Action, Aug. 1, 2012, U.S. Appl. No. 12/860,043, filed Aug. 20, 2010.
Non-Final Office Action, Aug. 17, 2012, U.S. Appl. No. 12/868,622, filed Aug. 25, 2010.
Non-Final Office Action, Aug. 18, 2010, U.S. Appl. No. 11/825,563, filed Jul. 6, 2007.
Non-Final Office Action, Dec. 4, 2013, U.S. Appl. No. 13/396,568, filed Feb. 14, 2012.
Non-Final Office Action, Dec. 8, 2014, U.S. Appl. No. 12/215,980, filed Jun. 30, 2008.
Non-Final Office Action, Feb. 19, 2013, U.S. Appl. No. 12/944,659, filed Nov. 11, 2010.
Non-Final Office Action, Jul. 14, 2015, U.S. Appl. No. 14/046,551, filed Oct. 4, 2013.
Non-Final Office Action, Jul. 15, 2015, U.S. Appl. No. 14/058,059, filed Oct. 18, 2013.
Non-Final Office Action, Jul. 28, 2011, U.S. Appl. No. 12/072,931, filed Feb. 29, 2008.
Non-Final Office Action, Jul. 7, 2015, U.S. Appl. No. 13/859,186, filed Apr. 9, 2013.
Non-Final Office Action, Jun. 10, 2015, U.S. Appl. No. 14/628,109, filed Feb. 20, 2015.
Non-Final Office Action, Jun. 26, 2015, U.S. Appl. No. 14/262,489, filed Apr. 25, 2014.
Non-Final Office Action, Jun. 26, 2015, U.S. Appl. No. 14/626,489, filed Apr. 25, 2014.
Non-Final Office Action, Jun. 7, 2012, U.S. Appl. No. 13/426,436, filed Mar. 21, 2012.
Non-Final Office Action, Mar. 11, 2014, U.S. Appl. No. 12/215,980, filed Jun. 30, 2008.
Non-Final Office Action, May 11, 2012, U.S. Appl. No. 13/424,189, filed Mar. 19, 2012.
Non-Final Office Action, May 21, 2015, U.S. Appl. No. 14/189,817, filed Feb. 25, 2014.
Non-Final Office Action, May 23, 2014, U.S. Appl. No. 13/859,186, filed Apr. 9, 2013.
Non-Final Office Action, May 31, 2016, U.S. Appl. No. 14/874,329, filed Oct. 2, 2015.
Non-Final Office Action, May 6, 2016, U.S. Appl. No. 14/495,550, filed Sep. 24, 2014.
Non-Final Office Action, May 8, 2013, U.S. Appl. No. 13/492,780, filed Jun. 8, 2012.
Non-Final Office Action, Nov. 14, 2011, U.S. Appl. No. 12/215,980, filed Jun. 30, 2008.
Non-Final Office Action, Nov. 15, 2011, U.S. Appl. No. 12/286,995, filed Oct. 2, 2008.
Non-Final Office Action, Nov. 2013, U.S. Appl. No. 13/363,362, filed Jan. 31, 2012.
Non-Final Office Action, Nov. 5, 2015, U.S. Appl. No. 13/396,568, filed Feb. 14, 2012.
Non-Final Office Action, Nov. 7, 2012, U.S. Appl. No. 13/492,780, filed Jun. 8, 2012.
Non-Final Office Action, Oct. 28, 2015, U.S. Appl. No. 13/363,362, filed Jan. 31, 2012.
Non-Final Office Action, Sep. 1, 2011, U.S. Appl. No. 12/286,909, filed Oct. 2, 2008.
Non-Final Office Action, Sep. 12, 2013, U.S. Appl. No. 13/426,436, filed Mar. 21, 2012.
Non-Final Office Action, Sep. 14, 2011, U.S. Appl. No. 12/004,897, filed Dec. 21, 2007.
Notice of Allowance dated Feb. 24, 2016 in Korean Application No. 10-2011-7000440, filed Jun. 26, 2009.
Notice of Allowance dated Oct. 10, 2014 in Finland Application No. 20100001, filed Jul. 3, 2008.
Notice of Allowance dated Sep. 16, 2014 in Korean Application No. 10-2010-7000194, filed Jul. 3, 2008.
Notice of Allowance dated Sep. 29, 2014 in Taiwan Application No. 097125481, filed Jul. 4, 2008.
Notice of Allowance mailed Feb. 10, 2015 in Taiwan Patent Application No. 098121933, filed Jun. 29, 2009.
Notice of Allowance mailed Jun. 2, 2015 in Japan Patent Application 2011-516313, filed Jun. 26, 2009.
Notice of Allowance, Apr. 28, 2016, U.S. Appl. No. 13/859,186, filed Apr. 9, 2013.
Notice of Allowance, Aug. 4, 2011, U.S. Appl. No. 13/016,916, filed Jan. 28, 2011.
Notice of Allowance, Feb. 28, 2012, U.S. Appl. No. 12/286,909, filed Oct. 2, 2008.
Notice of Allowance, Jan. 18, 2013, U.S. Appl. No. 12/860,043, filed Aug. 22, 2010.
Notice of Allowance, Jan. 27, 2012, U.S. Appl. No. 12/004,897, filed Dec. 21, 2007.
Notice of Allowance, Jul. 16, 2014, U.S. Appl. No. 13/426,436, filed Mar. 21, 2012.
Notice of Allowance, Jul. 7, 2015, U.S. Appl. No. 12/215,980, filed Jun. 30, 2008.
Notice of Allowance, Mar. 1, 2012, U.S. Appl. No. 12/072,931, filed Feb. 29, 2008.
Notice of Allowance, Mar. 1, 2012, U.S. Appl. No. 12/080,115, filed Mar. 31, 2008.
Notice of Allowance, Mar. 13, 2014, U.S. Appl. No. 12/286,995, filed Oct. 2, 2008.
Notice of Allowance, Mar. 25, 2014, U.S. Appl. No. 11/825,563, filed Jul. 6, 2007.
Notice of Allowance, Mar. 7, 2013, U.S. Appl. No. 13/424,189, filed Mar. 19, 2012.
Notice of Allowance, May 1, 2014, U.S. Appl. No. 12/868,622, filed Aug. 25, 2010.
Notice of Allowance, May 20, 2015, U.S. Appl. No. 13/888,796, filed May 7, 2013.
Notice of Allowance, May 25, 2011, U.S. Appl. No. 13/016,916, filed Jan. 28, 2011.
Notice of Allowance, Nov. 24, 2014, U.S. Appl. No. 13/492,780, filed Jun. 8, 2012.
Notice of Allowance, Oct. 3, 2013, U.S. Appl. No. 13/157,238, filed Jun. 9, 2011.
Office Action mailed Apr. 15, 2014 in Japan Patent Application 2010-514871, filed Jul. 3, 2008.
Office Action mailed Apr. 16, 2015 in Korean Patent Application No. 10-2011-7000440, filed Jun. 26, 2009.
Office Action mailed Dec. 9, 2013 in Finland Patent Application 20100431, filed Jun. 26, 2009.
Office Action mailed Jan. 20, 2014 in Finland Patent Application 20100001, filed Jul. 3, 2008.
Office Action mailed Jul. 21, 2015 in Japanese Patent Application 2012-542167 filed Dec. 1, 2010.
Office Action mailed Jun. 17, 2015 in Japan Patent Application 2013-519682 filed May 19, 2011.
Office Action mailed Jun. 27, 2014 in Korean Patent Application No. 10-2010-7000194, filed Jan. 6, 2010.
Office Action mailed Jun. 9, 2015 in Japan Patent Application 2014-165477 filed Jul. 3, 2008.
Office Action mailed Mar. 24, 2015 in Japan Patent Application No. 2011-516313, filed Jun. 26, 2009.
Office Action mailed May 2, 2014 in Taiwan Patent Application 098121933, filed Jun. 29, 2009.
Office Action mailed Oct. 14, 2013 in Taiwan Patent Application 097125481, filed Jul. 4, 2008.
Office Action mailed Oct. 17, 2013 in Taiwan Patent Application 097125481, filed Jul. 4, 2008.
Office Action mailed Oct. 29, 2013 in Japan Patent Application 2011-516313, filed Jun. 26, 2009.
Office Action mailed Oct. 31, 2014 in Finnish Patent Application No. 20125600, filed Jun. 1, 2012.
Office Action mailed Sep. 29, 2015 in Finnish Patent Application 20125600, filed Dec. 1, 2010.
Soon et al., "Low Distortion Speech Enhancement", Proc. Inst. Elect. Eng. [Online] 2000, vol. 147, pp. 247-253.
Sundaram et al., "Discriminating Two Types of Noise Sources Using Cortical Representation and Dimension Reduction Technique," IEEE, 2007.
Temko et al., "Classifiation of Acoustic Events Using SVM-Based Clustering Schemes," Pattern Recognition 39, No. 4, 2006, pp. 682-694.
Tognieri et al., "A Comparison of the LBG, LVQ, MLP, SOM and GMM Algorithms for Vector Quantisation and Clustering Analysis," University of Western Australia, 1992.
JP2006314080A (en) 2006-11-16 Audio enhancement system and method
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