Google Inc.

Zalán Borsos

Marco Tagliasacchi

Neil Zeghidour

Matthew Nirvan Sharifi

Beat Gfeller

Jesse Engel

Adam Roberts

Christian Frank

Mauro Verzetti

Antoine Caillon

Timo Immanuel Denk

Andrea Agostinelli

Yunpeng Li

Dominik Roblek

David G Grangier

Karolis Misiunas

Félix De Chaumont Quitry

Hannah Muckenhirn

Raphaël Marinier

Olivier Teboul

Victor Ungureanu

Oleg Rybakov

Example implementations of the present disclosure relate to machine learning for microphone style transfer, for example, to facilitate augmentation of audio data such as speech data to improve robustness of machine learning models trained on the audio data. Systems and methods for microphone style transfer can include one or more machine-learned microphone models trained to obtain and augment signal data to mimic characteristics of signal data obtained from a target microphone. The systems and methods can include a speech enhancement network for enhancing a sample before the style transfer. The augmentation output can then be utilized for a variety of downstream tasks.

Machine learning for microphone style transfer

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