Apple Inc.

Ronald J Guglielmone, Jr

Christopher T Eubank

Grant H Mulliken

Symeon Delikaris Manias

Mehrez Souden

Izzet B Yildiz

Matthew S Connolly

Ante Jukic

Sterling R Crispin

Sabine Webel

Mary A Pyc

Mikaela D Estep

A method performed by a programmed processor of an electronic device. The device obtains an audio signal, obtains, using one or more microphones, a microphone signal that includes audio of an environment in which the electronic device is located. The device determines a context of the device, and selects a volume compensation model from several models based on the determined context. The device processes the audio signal according to the selected volume compensation model and the microphone signal, and uses the processed audio signal to drive one or more speakers of the device.

Method and system for context-dependent automatic volume compensation

A method performed by a first electronic device that includes a first speaker, the method includes, receiving, via a network, a representation of audio content, while a second electronic device is playing back the audio content through a second speaker, determining that the first electronic device is moving away from the second electronic device, and, in response to determining that the first electronic device is moving away from the second electronic device, using the representation of audio to play back the audio content through the first speaker.

Method and system for audio bridging with an output device

Some implementations disclosed herein present a computer-generated reality (CGR) environment in which a user participates in an activity, identify a cognitive state of the user (e.g., working, learning, resting, etc.) based on data regarding the user's body (e.g., facial expressions, hand movements, physiological data, etc.), and update the environment with a surrounding environment that promotes a cognitive state of the user for the activity.

Creation of optimal working, learning, and resting environments on electronic devices

An audio renderer can have a machine learning model that jointly processes audio and visual information of an audiovisual recording. The audio renderer can generate output audio channels. Sounds captured in the audiovisual recording and present in the output audio channels are spatially mapped based on the joint processing of the audio and visual information by the machine learning model. Other aspects are described.

Audio renderer based on audiovisual information

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Redwood City, CA, United States of America

Ronald J Guglielmone, Jr