Dolby Laboratories Licensing Corporation

Dolby International Ab

Other

Fraunhofer-gesellschaft Zur Foerderung Der Angewandten Forschung E.v.

Grant Allen Davidson

Kuan-Chieh Yen

Mark Stuart Vinton

Louis Dunn Fielder

Zhiwei Shuang

Dirk Jeroen Breebaart

Matthew Conrad Fellers

Rhonda J Wilson

David Matthew Cooper

Vinay Melkote

Xiguang Zheng

Michael Mead Truman

Mark Franklin Davis

Charles Quito Robinson

Vivek Kumar

Robert Loring Andersen

Michael Schug

Craig C Todd

Arijit Biswas

Kenneth James Gundry

Rongshan Yu

Stephen Decker Vernon

Roy M Fejgin

Chih-Wei Wu

Alan Jeffrey Seefeldt

Martin Dietz

Brett Graham Crockett

Regunathan Radhakrishnan

Karlheinz Brandenburg

Claus Bauer

Oliver Kunz

Cong Zhou

Uwe Gbur

Brian David Link

Anil Wamanrao Ubale

Matthew Aubrey Watson

Marina Bosi-Goldberg

Michael B Antill

Eric Benjamin

Marina Bosi

Eric M Benjamin

Computer-implemented methods for training a neural network, as well as for implementing audio encoders and decoders via trained neural networks, are provided. The neural network may receive an input audio signal, generate an encoded audio signal and decode the encoded audio signal. A loss function generating module may receive the decoded audio signal and a ground truth audio signal, and may generate a loss function value corresponding to the decoded audio signal. Generating the loss function value may involve applying a psychoacoustic model. The neural network may be trained based on the loss function value. The training may involve updating at least one weight of the neural network.

Perceptually-based loss functions for audio encoding and decoding based on machine learning

The present disclosure relates to the field of audio coding, in particular, it relates to a method for encoding audio signals through a masking model based on a hearing threshold of frequency intervals of the audio signal and a measured energy of the audio signal for the corresponding frequency intervals. The disclosure further relates to an encoder that is capable of carrying out the audio encoding method.

Psychoacoustic model for audio processing

Methods and systems for designing binaural room impulse responses (BRIRs) for use in headphone virtualizers, and methods and systems for generating a binaural signal in response to a set of channels of a multi-channel audio signal, including by applying a BRIR to each channel of the set, thereby generating filtered signals, and combining the filtered signals to generate the binaural signal, where each BRIR has been designed in accordance with an embodiment of the design method. Other aspects are audio processing units configured to perform any embodiment of the inventive method. In accordance with some embodiments, BRIR design is formulated as a numerical optimization problem based on a simulation model (which generates candidate BRIRs) and at least one objective function (which evaluates each candidate BRIR), and includes identification of a best one of the candidate BRIRs as indicated by performance metrics determined for the candidate BRIRs by each objective function.

Methods and systems for designing and applying numerically optimized binaural room impulse responses

The present disclosure relates to reverberation generation for headphone virtualization. A method of generating one or more components of a binaural room impulse response (BRIR) for headphone virtualization is described. In the method, directionally-controlled reflections are generated, wherein directionally-controlled reflections impart a desired perceptual cue to an audio input signal corresponding to a sound source location. Then at least the generated reflections are combined to obtain the one or more components of the BRIR. Corresponding system and computer program products are described as well.

Reverberation generation for headphone virtualization

In some embodiments, virtualization methods for generating a binaural signal in response to channels of a multi-channel audio signal, which apply a binaural room impulse response (BRIR) to each channel including by using at least one feedback delay network (FDN) to apply a common late reverberation to a downmix of the channels. In some embodiments, input signal channels are processed in a first processing path to apply to each channel a direct response and early reflection portion of a single-channel BRIR for the channel, and the downmix of the channels is processed in a second processing path including at least one FDN which applies the common late reverberation. Typically, the common late reverberation emulates collective macro attributes of late reverberation portions of at least some of the single-channel BRIRs. Other aspects are headphone virtualizers configured to perform any embodiment of the method.

Generating binaural audio in response to multi-channel audio using at least one feedback delay network

Growing community of inventors

Burlingame, CA, United States of America

Grant Allen Davidson