Intel Corporation

Google Inc.

Apple Inc.

Sony Corporation

Other

Sony Electronics Inc

Meta Platforms Technologies, LLC

Jonathan J Huang

Hector A Cordourier Maruri

Lama Nachman

Paulo Lopez Meyer

Rahul C Shah

Willem M Beltman

Peter Bradshaw

Giuseppe Beppe Raffa

Samuel Kvaalen

Carlos M Avendano

Jose Rodrigo Camacho Perez

Jennifer Anne Healey

Julio Cesar Zamora Esquivel

Tobias Bocklet

Chieh-Yih Wan

Junaith Ahemed Shahabdeen

Hong Lu

Glen J Anderson

Rita H Wouhaybi

Jennifer Anne Lees

Hassan Taherian

Bran Ferren

Debargha Mukherjee

Narayan Biswal

Omesh Tickoo

Gokcen Cilingir

Lei Yang

David Gomez Gutierrez

Lenitra M Durham

Chen Wu

Rafael De La Guardia Gonzalez

Sangita R Sharma

Paul J Stewart

John Woodruff

Jose I Parra Vilchis

Vered Bar Bracha

Wendy March

Vijnan Shastri

Leobardo E Campos Macias

Rodrigo Aldana Lopez

Mehrez Souden

Douglas L Gabel

Binuraj K Ravindran

Munir Georges

Alexander Essaian

Sam Kvaalen

Paul Allan Covell

Yinxiao Li

Sylvia Downing

Omar Florez

John Cross Neumann

David Bird

Ze'ev Rivlin

Aleksander Magi

Michael F Cohen

Andrew Garrod Bosworth

Brian Keith Cabral

Deepak Shashidhar Vembar

Yen-kuang Chen

Adrian Wong

Anthony George LaMarca

John D Perreault

Mandar S Joshi

Jonathon Shlens

Barret Zoph

Cory J Booth

Justin S Legakis

Daniel C Klingler

Wei Hua

Andrea Colaco

Neil Hendin

Trond Thomas Wuellner

Gaurav Shah

Adam Kupryjanow

Joseph LaPenna

Andrew D Berkheimer

Patrick Llull

Timo Juhani Ahonen

Alejandro Ibarra Von Borstel

Kevin Patrick Murphy

Jamie Sherman

Jia Li

David Pearce

Douglas P Bogia

Ryan Scott Brotman

Chenxi Liu

Ashwini Asokan

Miquel Espi Marques

Julio Zamora Esquivel

Philip Muse

Saurav Sahay

Omar Ulises Florez Choque

Fei-Fei Li

Jinwon Lee

Ben S Hayden

Alain Ayoub

Karol Duzinkiewicz

Mei Lu

Alexander Schiffhauer

Yi Sun Huang

A method includes receiving video data that includes a series of frames of image data. Here, the video data is representative of an actor performing an activity. The method also includes processing the video data to generate a spatial input stream including a series of spatial images representative of spatial features of the actor performing the activity, a temporal input stream representative of motion of the actor performing the activity, and a pose input stream including a series of images representative of a pose of the actor performing the activity. Using at least one neural network, the method also includes processing the temporal input stream, the spatial input stream, and the pose input stream. The method also includes classifying, by the at least one neural network, the activity based on the temporal input stream, the spatial input stream, and the pose input stream.

Pose empowered RGB-flow net

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

Methods, apparatus, systems, and articles of manufacture to detect the location of sound sources external to computing devices are disclosed. An apparatus, to determine a direction of a source of a sound relative to a computing device, includes a cross-correlation analyzer to generate a vector of values corresponding to a cross-correlation of first and second audio signals corresponding to the sound. The first audio signal is received from a first microphone of the computing device. The second audio signal is received from a second microphone of the computing device. The apparatus also includes a location analyzer to use a machine learning model and a set of the values of the vector to determine the direction of the source of the sound.

Methods and apparatus to detect the location of sound sources external to computing devices

System and techniques for tracking caloric expenditure using sensor driven fingerprints are described herein. A set of outputs may be obtained from a plurality of sensors. A fingerprint may be generated using the set of outputs. The fingerprint may correspond to an activity observed by the plurality of sensors. The generated fingerprint may be compared to a set of fingerprints stored in a database. Each fingerprint of the set of fingerprints may correspond to a respective caloric expenditure. A caloric expenditure may be calculated for the activity based on the comparison. An exercise profile of a user may be updated using the caloric expenditure.

Tracking caloric expenditure using sensor driven fingerprints

Hearing protection and communication apparatus using vibration sensors are disclosed. An example wearable electronic device includes means for transducing vibrations associated with speech into a first signal; means for transducing sound associated with ambient noise into a second signal; and means for processing to cause a speaker to output a signal to reduce the ambient noise; detect an identifier in the speech; and cause audio data representative of the speech to be transmitted to a second device associated with the identifier.

Hearing protection and communication apparatus using vibration sensors

An embodiment of a spoken intent detection device includes technology to detect a phrase in an electronic representation of an audio stream based on a pre-defined vocabulary, associate a time stamp with the detected phrase, and classify a spoken intent based on a sequence of detected phrases and the respective associated time stamps. Other embodiments are disclosed and claimed.

Time asynchronous spoken intent detection

In one embodiment, a computing system may receive, from a second computing system, video streams of a scene, the video streams including at least a first image and a second image that are simultaneously captured by a first camera and a second camera of the second computing system, respectively. The system may determine, using a sensor system, a viewpoint of a viewer with respect to a display region of a monoscopic display associated with the first computing system. The system may generate an output image of the scene by blending, according to blending proportions computed using the viewpoint of the viewer, corresponding portions of the first image and the second image. The system may display the output image in the display region of the monoscopic display.

Videotelephony with parallax effect

A system, article, and method of acoustic angle of arrival detection uses both same-time and delayed-time audio signal value comparisons in a time domain that are input to a classifier neural network.

Method and system of acoustic angle of arrival detection

Various systems and methods for implementing distribution of a neural network workload are described herein. A discovery message is encoded that includes a latency requirement and requested resources for a workload of a neural network. A discovery response, from a proximate resource and in response to the discovery message, is decoded and includes available resources of the proximate resource available for the workload based on the requested resources for the workload. The proximate resource is selected to execute the workload based on the available resources of the proximate resource. In response to the discovery response, an offload request is encoded that includes a description of the workload. The description of the workload identifies the node to execute at the proximate resource. In response to the offload request, an input is provided to a ADAS system based on the result.

Proximity-based distributed sensor processing

Growing community of inventors

Pleasanton, CA, United States of America

Jonathan J Huang