Audience, Inc.

Knowles Electronics, Inc.

David Klein

Carlo Murgia

Sridhar Krishna Nemala

Alex Afshar

Michael M Goodwin

Lloyd Watts

Carlos M Avendano

Jean Laroche

John Woodruff

Ye Jiang

Mark Every

Eitan Medina

Peter Santos

Umit Yapanel

Deborah Kathleen Vitus

Bernard Mont-Reynaud

Stephen Malinowski

Hong You

Systems and methods for performing personalized operations of a mobile device are provided. An example method includes determining a sensor signature has been received. The sensor signature may be a combination of acoustic and non-acoustic inputs. A classification or recognition or matching score for the received sensor signature may be calculated. A context associated with one or more of the operations of the mobile device may be determined. A confidence level or weight may be determined for the acoustic and non-acoustic inputs based on the context. Based on the confidence level or weight, a determination may be made whether to perform the one or more operations. The determination whether to perform the one or more operations may be further based on a combination of the confidence level or weight and the score. Determining the context can include identifying the type and level of noise associated with the mobile device's environment.

Personalized operation of a mobile device using sensor signatures

Systems and methods for voice authentication are provided. An example method starts with dynamically generating authentication information and a prompt associated therefor. The authentication information is generated to emphasize differences between an authorized user and others. The authentication information may be generated based on at least one of a user profile, an acoustic environment in which a mobile device is located, and a history of interactions with the mobile device. The authentication information may be an answer to a question which the authorized user would uniquely be able to provide, or it may be a distinctive password. The prompt, for the authentication information, is provided to a user attempting to use the mobile device. The method proceeds with capturing an acoustic sound of a speech of the user and detecting the authentication information in the speech. Based on the detection, a confidence score is determined. Based on the confidence score and context, the method performs an authentication of the user or providing the confidence score, the context, and a transcription of the speech to a next stage of authentication.

Shared secret voice authentication

Methods for voice sensing and keyword analysis are provided. An example method allows for causing a mobile device to transition to a second power mode, from a first power mode, in response to a first acoustic signal. The method includes authenticating a user based at least in part on a second acoustic signal. While authenticating the user, the second acoustic signal is compared to a spoken keyword. The spoken keyword is analyzed for authentication strength based on the length of the spoken keyword, quality of a series of phonemes used to represent the spoken keyword, and likelihood of the series of phonemes to be detected by a voice sensing. While receiving the first and second acoustic signals, a signal to noise ratio (SNR) is determined. The SNR is used to adjust sensitivity of a detection threshold of a voice sensing.

Voice sensing and keyword analysis

Provided are systems and methods for generating clean speech from a speech signal representing a mixture of a noise and speech. The clean speech may be generated from synthetic speech parameters. The synthetic speech parameters are derived based on the speech signal components and a model of speech using auditory and speech production principles. The modeling may utilize a source-filter structure of the speech signal. One or more spectral analyzes on the speech signal are performed to generate spectral representations. The feature data is derived based on a spectral representation. The features corresponding to the target speech according to a model of speech are grouped and separated from the feature data. The synthetic speech parameters, including spectral envelope, pitch data and voice classification data are generated based on features corresponding to the target speech.

Speech signal separation and synthesis based on auditory scene analysis and speech modeling

Systems and methods for adaptive power control are provided. A minimum power level of a primary acoustic signal is estimated. The minimum power level may then be compared to at least one power threshold. Subsequently, a large power consuming system is controlled based on the comparison of the minimum power level to the power threshold.

Adaptive power control

System and method for adaptive power control

Systems and methods for adaptive intelligent noise suppression are provided. In exemplary embodiments, a primary acoustic signal is received. A speech distortion estimate is then determined based on the primary acoustic signal. The speech distortion estimate is used to derive control signals which adjust an enhancement filter. The enhancement filter is used to generate a plurality of gain masks, which may be applied to the primary acoustic signal to generate a noise suppressed signal.

System and method for adaptive intelligent noise suppression

Systems and methods for adaptive power control are provided. In exemplary embodiments, a primary signal is received. A noise power level of the primary signal is then estimated. The noise power level may then be compared to at least one power threshold. Subsequently, a large power consuming system is controlled based on the comparison of the noise power level to the power threshold.

A method, system, and computer program for enhancing a signal are presented. The signal is received, and energy estimates of the signal may be determined. At least one characteristic of the signal may be inferred based on the energy estimates. A mask may be generated based, in part, on the at least one characteristic. In turn, the mask may be applied to the signal to produce an enhanced signal, which may be outputted.

System and method for enhancing a monaural audio signal

Systems and methods for envelope-based acoustic echo cancellation in a communication device are provided. In exemplary embodiments, a primary acoustic signal is received via a microphone of the communication device, and a far-end signal is received via a receiver. Frequency analysis is performed on the primary acoustic signal and the far-end acoustic signal to obtain frequency sub-bands. An echo gain mask based on magnitude envelopes of the primary and far-end acoustic signals for each frequency sub-band is generated. A noise gain mask based on at least the primary acoustic signal for each frequency sub-band may also be generated. A combination of the echo gain mask and noise gain mask may then be applied to the primary acoustic signal to generate a masked signal. The masked signal is then output.

System and method for envelope-based acoustic echo cancellation

Systems and methods for modification of an audio input signal are provided. In exemplary embodiments, an adaptive multiple-model optimizer is configured to generate at least one source model parameter for facilitating modification of an analyzed signal. The adaptive multiple-model optimizer comprises a segment grouping engine and a source grouping engine. The segment grouping engine is configured to group simultaneous feature segments to generate at least one segment model. The at least one segment model is used by the source grouping engine to generate at least one source model, which comprises the at least one source model parameter. Control signals for modification of the analyzed signal may then be generated based on the at least one source model parameter.

Systems and methods for audio signal analysis and modification

Systems and methods for blind subband acoustic echo cancellation postfiltering in a communication device are provided. In exemplary embodiments, an acoustic signal is received via a microphone of the communication device. Acoustic echo cancellation (AEC) is applied to the acoustic signal to obtain an AEC masked signal. Because residual echo may still exist in the AEC masked signal, blind subband AEC postfiltering on the AEC masked signal may be performed to obtain an echo-free acoustic signal. The echo-free signal may then be output.

System and method for blind subband acoustic echo cancellation postfiltering

A method of removing reverberation from audio signals is disclosed. The method comprises spectro-temporally analyzing the first audio signal and the second audio signal to derive an energy function of time for a plurality of frequency bands. The method further comprises determining a delay stability between the energy function of time for the first audio signal and the second audio signal in each band, determining a gain function in each band based on the delay stability, adjusting the energy of the first audio signal and the second audio signal using the gain function within each band, and resynthesizing audio signals from the energy in each band of the first audio signal and the second audio signal.

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Los Altos, CA, United States of America

David Klein