Google Inc.

Zalán Borsos

Marco Tagliasacchi

Neil Zeghidour

Matthew Nirvan Sharifi

Jesse Engel

Adam Roberts

Christian Frank

Mauro Verzetti

Antoine Caillon

Timo Immanuel Denk

Andrea Agostinelli

Dominik Roblek

Beat Gfeller

David G Grangier

Karolis Misiunas

Hannah Muckenhirn

Félix De Chaumont Quitry

Victor Ungureanu

Raphaël Marinier

Yunpeng Li

Olivier Teboul

Oleg Rybakov

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a prediction of an audio signal. One of the methods includes receiving a request to generate an audio signal conditioned on an input; processing the input using an embedding neural network to map the input to one or more embedding tokens; generating a semantic representation of the audio signal; generating, using one or more generative neural networks and conditioned on at least the semantic representation and the embedding tokens, an acoustic representation of the audio signal; and processing at least the acoustic representation using a decoder neural network to generate the prediction of the audio signal.

Generating audio using auto-regressive generative neural networks

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for processing an input audio waveform using a generator neural network to generate an output audio waveform. In one aspect, a method comprises: receiving an input audio waveform; processing the input audio waveform using an encoder neural network to generate a set of feature vectors representing the input audio waveform; and processing the set of feature vectors representing the input audio waveform using a decoder neural network to generate an output audio waveform that comprises a respective output audio sample for each of a plurality of output time steps.

Generating audio waveforms using encoder and decoder neural networks

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a prediction of an audio signal. One of the methods includes receiving a request to generate an audio signal; obtaining a semantic representation of the audio signal; generating, using one or more generative neural networks and conditioned on at least the semantic representation, an acoustic representation of the audio signal; and processing at least the acoustic representation using a decoder neural network to generate the prediction of the audio signal.

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Zurich, Switzerland

Zalán Borsos