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

arXiv: https://arxiv.org/abs/2104.07283 · HTML: https://mlsanity.com/paper/2104.07283/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 22 references that are in the map.
- First posted on arXiv 15 Apr 2021.

## Builds on (most cited first, 5 of 5)

- [Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis](https://mlsanity.com/paper/1803.09017/) (2018)
- [Dual Supervised Learning](https://mlsanity.com/paper/1707.00415/) (2017)
- [Transforming Spectrum and Prosody for Emotional Voice Conversion with Non-Parallel Training Data](https://mlsanity.com/paper/2002.00198/) (2020)
- [Converting Anyone's Emotion: Towards Speaker-Independent Emotional Voice Conversion](https://mlsanity.com/paper/2005.07025/) (2020)
- [Att-HACK: An Expressive Speech Database with Social Attitudes](https://mlsanity.com/paper/2004.04410/) (2020)

## Built on by (most cited first, 3 of 3)

- [Emotional Voice Conversion: Theory, Databases and ESD](https://mlsanity.com/paper/2105.14762/) (2021)
- [A survey on GANs for computer vision: Recent research, analysis and taxonomy](https://mlsanity.com/paper/2203.11242/) (2022)
- [Audio Deepfake Detection Based on a Combination of F0 Information and Real Plus Imaginary Spectrogram Features](https://mlsanity.com/paper/2208.01214/) (2022)

## Abstract (arXiv)

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.