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Self-Supervised VQ-VAE for One-Shot Music Style Transfer

Ondřej Cífka 1, 2, 3, 4 Alexey Ozerov 1 Umut Simsekli 2, 3, 4, 5, 6 Gael Richard 2, 3, 4
2 S2A - Signal, Statistique et Apprentissage
LTCI - Laboratoire Traitement et Communication de l'Information
6 SIERRA - Statistical Machine Learning and Parsimony
DI-ENS - Département d'informatique de l'École normale supérieure, CNRS - Centre National de la Recherche Scientifique, Inria de Paris
Abstract : Neural style transfer, allowing to apply the artistic style of one image to another, has become one of the most widely showcased computer vision applications shortly after its introduction. In contrast, related tasks in the music audio domain remained, until recently, largely untackled. While several style conversion methods tailored to musical signals have been proposed, most lack the 'one-shot' capability of classical image style transfer algorithms. On the other hand, the results of existing one-shot audio style transfer methods on musical inputs are not as compelling. In this work, we are specifically interested in the problem of one-shot timbre transfer. We present a novel method for this task, based on an extension of the vector-quantized variational autoencoder (VQ-VAE), along with a simple self-supervised learning strategy designed to obtain disentangled representations of timbre and pitch. We evaluate the method using a set of objective metrics and show that it is able to outperform selected baselines.
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Contributor : Ondřej Cífka <>
Submitted on : Thursday, June 10, 2021 - 5:20:01 PM
Last modification on : Tuesday, July 13, 2021 - 3:09:01 AM


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Ondřej Cífka, Alexey Ozerov, Umut Simsekli, Gael Richard. Self-Supervised VQ-VAE for One-Shot Music Style Transfer. ICASSP 2021 - IEEE International Conference on Acoustics, Speech and Signal Processing, Jun 2021, Toronto / Virtual, Canada. ⟨10.1109/ICASSP39728.2021.9414235⟩. ⟨hal-03132940⟩



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