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Towards end-to-end F0 voice conversion based on Dual-GAN with convolutional wavelet kernels

2021-04-15 07:42:59
Clément Le Moine Veillon, Nicolas Obin, Axel Roebel
arXiv_SD
           
Abstract

This paper presents a end-to-end framework for the F0 transformation in the context of expressive voice conversion. A single neural network is proposed, in which a first module is used to learn F0 representation over different temporal scales and a second adversarial module is used to learn the transformation from one emotion to another. The first module is composed of a convolution layer with wavelet kernels so that the various temporal scales of F0 variations can be efficiently encoded. The single decomposition/transformation network allows to learn in a end-to-end manner the F0 decomposition that are optimal with respect to the transformation, directly from the raw F0 signal.

Abstract (translated)

URL

https://arxiv.org/abs/2104.07283

PDF

https://arxiv.org/pdf/2104.07283.pdf

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