Apple Inc.

Matthew S Connolly

Christopher T Eubank

Stephen E Pinto

Carlos M Avendano

Sean Anthony Ramprashad

Tomlinson M Holman

Robert Denton Silfvast

Symeon Delikaris Manias

Mehrez Souden

Ante Jukic

Sabine Webel

Miquel Espi Marques

Ronald J Guglielmone, Jr

Lance Jabr

David Thall

Pedro M De Sa Carvahlo Corvo

Bharathidasan Venkatesan

Jared King

Edward L Stein

An application programming interface, API, and its implementing functions provide software code that can be incorporated into a simulated reality application and that determine a total calibration gain that is to be applied during run time of the application, to the audio signal of a particular digital audio asset that has been selected by the author of the simulated reality application. The total calibration gain includes at least a loudness correction gain that is determined based on a user-specified sound level parameter indicated through the API by the author. Other aspects are also described and claimed.

Calibration gain for real world sounds

Playback of an audio signal is simulated from a playback position to a listening position. The simulation is performed with respect to a model of a listening area. The resulting loudness of the audio, perceived at the listening position, is rendered to a display. Other aspects are described and claimed.

Audio level metering for listener position and object position

An audio asset library containing audio assets formatted in accordance with a file format for spatial audio includes asset metadata that enables simulated reality (SR) application developers to compose sounds for use in SR applications. The audio assets are formatted to include audio data encoding a sound capable of being composed into a SR application along with asset metadata describing not only how the sound was encoded, but also how a listener in SR environment experiences the sound. A SR developer platform is configured so that developers can compose sound for SR objects using audio assets stored in the audio library, including editing the asset metadata to include transformation parameters that support dynamic transformation of the asset metadata in the SR environment to alter how the SR listener experiences the composed sound. Other embodiments are also described and claimed.

File format for spatial audio

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

Channels of audio data in a spatial audio object are associated with any one or more of a direction and a location of one or more recorded sounds, which channels are to be reproduced as spatial sound. A visualized spatial sound object represents a snapshot/thumbnail of the spatial sound. To preview the spatial sound (by experiencing its snapshot or thumbnail), a user manipulates the orientation of the visualized spatial sound object, and a weighted downmix of the channels is rendered for output as a spatial preview sound, e.g., a single output audio signal is provided to a spatial audio renderer; one or more of the channels that are oriented toward the user are emphasized in the preview sound, more than channels that are oriented away from the user. Other aspects are also described and claimed.

Spatial audio downmixing

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

Spatial audio upmixing enables mixing spatial sound on a more granular per-channel level than is possible using conventional upmixing. A spatial bed is a multi-channel audio content that represents a complete sound field description, e.g., a virtual sphere of sound, for example surrounding a simulated reality listener in a simulated reality environment. A new spatial bed is generated by combining sections of at least two of such spatial beds. Other embodiments are also described and claimed.

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San Jose, CA, United States of America

Matthew S Connolly