Audience, Inc.

Amazon Technologies, Inc.

Knowles Electronics, Inc.

Invensense, Inc.

Ludger Solbach

Carlo Murgia

Carlos M Avendano

Mark Every

Ye Jiang

Michael M Goodwin

Jean Laroche

Mohamed Farouk Mansour

Shobha Devi Kuruba Buchannagari

Amit Singh Chhetri

Robert Ayrapetian

Trausti Thor Kristjansson

Ke Li

Kuan-Chieh Yen

Tarun Pruthi

Mark Robert Every

Brian D Clark

Lloyd Watts

Carlos Renato Nakagawa

Shailesh Sakri

Madhuri Saraf

Alex Kanaris

Harinarayanan Erumbi Vallabhan

Andrew Jackson Stockton, X

Techniques for improving microphone noise suppression are provided. When a playback signal is available, a device may use the playback signal as a reference while performing Acoustic Echo Cancellation (AEC) processing. When the playback signal is not available, the device may instead use microphone audio signal(s) as the reference while performing Adaptive Interference Cancellation (AIC) processing. For example, the device may use first audio data generated by first microphones as the reference while performing AIC processing on the first audio data and second audio data generated by second microphones. As the adaptive filter used to perform AIC processing is updated at a slower rate, the reference signal only cancels stationary portions of the first audio data (e.g., noise), leaving a representation of transient sounds such as speech. In some examples, the device may perform AIC processing and AEC processing in series.

Microphone reference echo cancellation

An enhancement equalizer (EEQ) can be configured to compensate for hearing loss. An app or application can assist a user with measuring hearing loss at various frequencies (e.g., threshold sensitivity versus normal hearing). Using these measurements, the system may compute a set of filters for an EEQ that can boost different frequencies by different amounts corresponding to the user's sensitivity to that frequency. The measurement and resulting EEQ may be earphone specific (e.g., both the measurement and the filter computation may depend on the particular type/model of earphone used). In some implementations, the system may allow the user to select correction strength that controls an amount of correction applied (e.g., 25%, 50%, or 75% of full correction). In some implementations, the system may adjust the EEQ and/or correction strength according to the volume of playback (e.g., by applying less correction at higher playback volumes to avoid triggering earphone limiters).

Enhancement equalizer for hearing loss

A beamformer system includes an in-ear audio device, such as an earbud, that has three microphones. Two microphones may be disposed on an external face of the audio-device; one microphone may be disposed in or near the ear canal of a user. Data from two microphones is phase-adjusted and combined; a reference signal is generated by phase-adjusting and removing data from the two microphones from data from a third microphone. The combined data is filtered using the reference signal to remove any residual echo, and the resulting data may be used for communications, speech processing, or other uses.

Beamforming using an in-ear audio device

This disclosure describes, in part, techniques to process audio signals to lessen the impact that wind and/or other environmental noise has upon the resulting quality of these audio signals. For example, the techniques may determine a level of wind and/or other noise in an environment and may determine how best to process the signals to lessen the impact of the noise, such that one or more users that hear audio based on output of the signals hear higher-quality audio.

Processing audio to account for environmental noise

A device that determines an echo latency estimate by subsampling reference audio data. The device may determine the echo latency corresponding to an amount of time between sending reference audio data to loudspeaker(s) and microphone audio data corresponding to the reference audio data being received. The device may generate subsampled reference audio data by selecting only portions of the reference audio data that have a magnitude above a desired percentile. For example, the device may compare a magnitude of an individual reference audio sample to a percentile estimate value and sample only the reference audio samples that exceed the percentile estimate value. The device generate cross-correlation data between the subsampled reference audio data and the microphone audio data and may estimate the echo latency based on an earliest significant peak represented in the cross-correlation data.

Echo latency estimation

In a method of adaptive buffering in a mobile device having a host processor and a sensor processor coupled with the host processor, the sensor processor is used to buffer data received from a sensor that is operated by the sensor processor. The data is buffered by the sensor processor into a circular data buffer. Responsive to the sensor processor detecting triggering data within the received data: a first adaptive data buffering action is initiated with respect to the data received from the sensor operated by the sensor processor; a second adaptive data buffering action is initiated with respect to second data received from a second sensor of the mobile device; and a command is sent from the sensor processor to a second processor.

Adaptive buffering of data received from a sensor

In a method of adaptive buffering in a mobile device having a host processor and a sensor processor coupled with the host processor, the sensor processor is used to buffer data received from a sensor that is operated by the sensor processor, wherein the data is buffered by the sensor processor into a circular data buffer. In response to the sensor processor detecting triggering data within the received data: the sensor processor sets a start-end marker in the circular data buffer; and a command is sent from the sensor processor to a second processor.

A speech-capturing device that can modulate its output audio data volume based on environmental sound conditions at the location of a user speaking to the device. The device detects the sound pressure of a spoken utterance at the device location and determines the distance of the user from the device. The device also detects the sound pressure of noise at the device and uses information about the location of the noise source and user to determine the sound pressure of noise at the location of the talker. The device can then adjust the gain for output audio (such as a spoken response to the utterance) to ensure that the output audio is at a certain desired sound pressure when it reaches the location of the user.

Volume adjustment for listening environment

Systems and methods for improving performance of a directional audio capture system are provided. An example method includes correlating phase plots of at least two audio inputs, with the audio inputs being captured by at least two microphones. The method can further include generating, based on the correlation, estimates of salience at different directional angles to localize a direction of a source of sound. The method can allow providing cues to the directional audio capture system based on the estimates. The cues include attenuation levels. A rate of change of the levels of attenuation is controlled by attack and release time constants to avoid sound artifacts. The method also includes determining a mode based on an absence or presence of one or more peaks in the estimates of salience. The method also provides for configuring the directional audio capture system based on the determined mode.

Directional audio capture

Systems and methods for controlling adaptivity of noise cancellation are presented. One or more audio signals are received by one or more corresponding microphones. The one or more signals may be decomposed into frequency sub-bands. Noise cancellation consistent with identified adaptation constraints is performed on the one or more audio signals. The one or more audio signals may then be reconstructed from the frequency sub-bands and outputted via an output device.

Adaptive noise cancellation

A robust noise reduction system may concurrently reduce noise and echo components in an acoustic signal while limiting the level of speech distortion. The system may receive acoustic signals from two or more microphones in a close-talk, hand-held or other configuration. The received acoustic signals are transformed to frequency domain sub-band signals and echo and noise components may be subtracted from the sub-band signals. Features in the acoustic sub-band signals are identified and used to generate a multiplicative mask. The multiplicative mask is applied to the noise subtracted sub-band signals and the sub-band signals are reconstructed in the time domain.

Multi-microphone robust noise suppression

The present technology provides a robust noise suppression system that may concurrently reduce noise and echo components in an acoustic signal while limiting the level of speech distortion. A time-domain acoustic signal may be received and be transformed to frequency-domain sub-band signals. Features, such as pitch, may be identified and tracked within the sub-band signals. Initial speech and noise models may be then be estimated at least in part from a probability analysis based on the tracked pitch sources. Speech and noise models may be resolved from the initial speech and noise models and noise reduction may be performed on the sub-band signals. An acoustic signal may be reconstructed from the noise-reduced sub-band signals.

Monaural noise suppression based on computational auditory scene analysis

A robust noise suppression system may concurrently reduce noise and echo components in an acoustic signal while limiting the level of speech distortion. The system may receive acoustic signals from two or more microphones in a close-talk, hand-held or other configuration. The received acoustic signals are transformed to cochlea domain sub-band signals and echo and noise components may be subtracted from the sub-band signals. Features in the acoustic sub-band signals are identified and used to generate a multiplicative mask. The multiplicative mask is applied to the noise subtracted sub-band signals and the sub-band signals are reconstructed in the time domain.

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Mountain View, CA, United States of America

Ludger Solbach