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

• URL: http://arxiv.org/abs/2104.07283v1
• Date: Thu, 15 Apr 2021 07:42:59 GMT
• Title: Towards end-to-end F0 voice conversion based on Dual-GAN with convolutional wavelet kernels
• Authors: Cl\'ement Le Moine Veillon, Nicolas Obin and Axel Roebel
• Abstract summary: A single neural network is proposed, in which a first module is used to learn F0 representation over different temporal scales. A second adversarial module is used to learn the transformation from one emotion to another.
• Score: 11.92436948211501
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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