Audience, Inc.

Creative Technology Corporation

Apple Inc.

Knowles Electronics, Inc.

Carlos M Avendano

Michael M Goodwin

Carlo Murgia

Ludger Solbach

Martin Wolters

Ramkumar Sridharan

Jean-Marc Jot

Miquel Espi Marques

Jonathan J Huang

Mark Every

Jean Laroche

Daniel C Klingler

John Woodruff

Ye Jiang

Mark Robert Every

Hyung-Suk Kim

Sean Anthony Ramprashad

Hassan Taherian

Christopher T Eubank

Robert Denton Silfvast

Mehrez Souden

Lloyd Watts

Matthew S Connolly

Peter Santos

Andreas Koutrouvelis

Lance Jabr

Karim Younes

Dana C Massie

David Klein

Shailesh Sakri

Marios Athineos

Harinarayanan Erumbi Vallabhan

Stephen Malinowski

Kevin M Durand

David Findlay

Vasudha Kowtha

Yichi Zhang

Michael Mandel

Implementations of the subject technology provide systems and methods for providing audio source separation for audio input, such as for audio devices having limited power and/or computing resources. The subject technology may allow an audio device to leverage processing and/or power resources of a companion device that is communicatively coupled to the audio device. The companion device may identify a noise condition of the audio device, select a source separation model based on the noise condition, and provide the source separation model to the audio device. In this way, the audio device can provide audio source separation functionality using a relatively small footprint source separation model that is specific to the noise condition in which the audio device is operated.

Audio source separation for audio devices

A method performed by an electronic device in a room. The method performs an enrollment process in which a spatial profile of a location of an artificial sound source is created and performs an identification process that determines whether a sound event within the room is produced by the artificial sound source by 1) capturing the sound event using a microphone array and 2) determining a likelihood that the sound event occurred at the location of the artificial sound source.

Method and system for detecting sound event liveness using a microphone array

The subject disclosure provides systems and methods for providing locally trained models for detecting individual sounds using electronic devices. Local detection of individual sounds with a detection model at an electronic device can be provided by obtaining training samples for the detection model with the electronic device, and generating additional negative and positive training samples based on the obtained training samples. A two-stage detection process may be provided, in which a trigger model at a device compares an audio input to a reference sound to trigger a detection model at the device. The detection of individual sounds with a detection model at an electronic device can also leverage audio capture capabilities of multiple devices in an acoustic scene to capture multiple concurrent training samples.

Sound detection for electronic devices

An electronic device has one or more microphones that pick up a sound. At least one feature extractor processes the audio signals from the microphones, that contain the picked up the sound, to determine several features for the sound. The electronic device also includes a classifier that has a machine learning model which is configured to determine a sound classification, such as artificial versus natural for the sound, based upon at least one of the determined features. Other aspects are also described and claimed.

Systems and methods for classifying sounds

An electronic device includes a processor, and a memory containing instructions that, when executed by the processor, cause the electronic device to learn a sound emitted by a legacy device and to issue an output when the electronic device subsequently hears the sound. For example, the electronic device can receive a training input and extract a compact representation of a sound in the training input, which the device stores. The device can receive an audio signal corresponding to an observed acoustic scene and extract a representation of the observed acoustic scene from the audio signal. The electronic device can determine whether the sound is present in the observed acoustic scene at least in part from a comparison of the representation of the observed acoustic scene with the representation of the sound. The electronic device emits a selected output responsive to determining that the sound is present in the acoustic scene.

Systems and methods for identifying an acoustic source based on observed sound

A first device obtains, from the array, several audio signals and processes the audio signals to produce a speech signal and one or more ambient signals. The first device processes the ambient signals to produce a sound-object sonic descriptor that has metadata describing a sound object within an acoustic environment. The first device transmits, over a communication data link, the speech signal and the descriptor to a second electronic device that is configured to spatially reproduce the sound object using the descriptor mixed with the speech signal, to produce several mixed signals to drive several speakers.

Audio bandwidth reduction

Embodiments of the present disclosure can provide systems, methods, and computer-readable medium for providing audio and/or video effects based at least in part on facial features and/or voice feature characteristics of the user. For example, video and/or an audio signal of the user may be recorded by a device. Voice audio features and facial feature characteristics may be extracted from the voice audio signal and the video, respectively. The facial features of the user may be used to modify features of a virtual avatar to emulate the facial feature characteristics of the user. The extracted voice audio features may modified to generate an adjusted audio signal or an audio signal may be composed from the voice audio features. The adjusted/composed audio signal may simulate the voice of the virtual avatar. A preview of the modified video/audio may be provided at the user's device.

Techniques for providing audio and video effects

A method for restoring distorted speech components of an audio signal distorted by a noise reduction or a noise cancellation includes determining distorted frequency regions and undistorted frequency regions in the audio signal. The distorted frequency regions include regions of the audio signal in which a speech distortion is present. Iterations are performed using a model to refine predictions of the audio signal at distorted frequency regions. The model is configured to modify the audio signal and may include deep neural network trained using spectral envelopes of clean or undamaged audio signals. Before each iteration, the audio signal at the undistorted frequency regions is restored to values of the audio signal prior to the first iteration; while the audio signal at distorted frequency regions is refined starting from zero at the first iteration. Iterations are ended when discrepancies of audio signal at undistorted frequency regions meet pre-defined criteria.

Systems and methods for restoration of speech components

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

Growing community of inventors

Campbell, CA, United States of America

Carlos M Avendano