Sony Interactive Entertainment Inc.

Brandon Sangston

Andrew Young

Victoria Dorn

Erik Beran

Kaoru Yamanoue

Hajime Horikoshi

Yoshimichi Kitaya

Shunsuke Saito

Yuki Karatsu

Ambisonics audio such as may be used for computer simulations such as computer games is improved by improving the emulated spread of a sound source. A demanded spread for a sound object of a computer game is received, and using the spread one or more fractional Ambisonics orders are determined. Weights such as max-rE weights are derived for the fractional Ambisonics order using polynomial interpolation. An Ambisonics representation of the sound object is encoded using the weights for the fractional Ambisonics order for providing the sound object to an Ambisonics decoder to decode and play the sound object.

Rendering ambisonics sound sources using fractional orders

Systems and methods for modifying spatial audio are described. One of the methods includes obtaining a first set of metadata for a first set of audio data and a second set of metadata for a second set of audio data. The first and second sets of metadata and the first and second sets of audio data are associated with a display of a virtual scene. The method further includes encoding the first set of audio data to output a first soundfield and the second set of audio data to output a second soundfield. The method also includes mixing the first and second soundfields to output a mixed soundfield, decoding the mixed soundfield based on at least one of the first set of metadata and the second set of metadata to provide mixed audio data, and outputting the mixed audio data as an audio output.

Systems and methods for modifying spatial audio

A method for determining an environment in which a user is located is described. The method includes receiving a plurality of sets of audio data based on sounds emitted in a plurality of environments. Each of the plurality of environments has a different combination of objects. The method further includes receiving input data regarding the plurality of environments, and training an artificial intelligence (AI) model based on the plurality of sets of audio data and the input data. The method includes applying the AI model to audio data captured from an environment surrounding the first user to determine a type of the environment.

Systems and methods for training a model to determine a type of environment surrounding a user

A technique for encoding Ambisonics audio includes inputting audio to multiple Ambisonics encoders producing respective Ambisonics soundfields. Prior to mixing the soundfields, each soundfield is weighted to mitigate artifacts from order-truncation. After weighting, the soundfields are mixed to produce Ambisonics audio.

Multi-order optimized Ambisonics encoding

Ambisonics audio such as may be used for computer simulations such as computer games is improved by using multi-order optimizations that frame an optimization problem that minimizes a cost function across a subset of Ambisonics orders for a chosen Ambisonics order 'N'. In a simple form, this cost function minimizes error across all orders (0<=n<=N), and additional weighting is applied to emphasize or de-emphasize particular orders. The cost functions and optimization criteria may be different for binaural and speaker outputs.

Multi-order optimized Ambisonics decoding

An audio signal processing apparatus in which different pieces of predetermined parameter information regarding acoustic transfer in a head of a listener are retained as preset candidates, a parameter information list including the retained preset candidates is presented, to prompt a user to select parameter information, and an audio signal for the user is generated by using the user-selected parameter information.

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San Mateo, CA, United States of America

Brandon Sangston