Skip to Main content Skip to Navigation
Conference papers

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

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.
Complete list of metadata

https://hal.archives-ouvertes.fr/hal-03239583
Contributor : Clément Le Moine Connect in order to contact the contributor
Submitted on : Thursday, May 27, 2021 - 3:27:53 PM
Last modification on : Tuesday, March 15, 2022 - 3:22:04 AM
Long-term archiving on: : Saturday, August 28, 2021 - 7:40:31 PM

File

Towards_end-to-end_F0_voice_co...
Files produced by the author(s)

Identifiers

  • HAL Id : hal-03239583, version 1

Citation

Clément Le Moine, Nicolas Obin, Axel Roebel. Towards end-to-end F0 voice conversion based on Dual-GAN with convolutional wavelet kernels. EUSIPCO, 2021, Dublin (virtual ), Ireland. ⟨hal-03239583⟩

Share

Metrics

Record views

49

Files downloads

54