no code implementations • 8 Oct 2023 • Ryuichi Yamamoto, Reo Yoneyama, Lester Phillip Violeta, Wen-Chin Huang, Tomoki Toda
This paper presents our systems (denoted as T13) for the singing voice conversion challenge (SVCC) 2023.
2 code implementations • 28 Oct 2022 • Ryuichi Yamamoto, Reo Yoneyama, Tomoki Toda
This paper describes the design of NNSVS, an open-source software for neural network-based singing voice synthesis research.
1 code implementation • 28 Oct 2022 • Reo Yoneyama, Ryuichi Yamamoto, Kentaro Tachibana
Neural audio super-resolution models are typically trained on low- and high-resolution audio signal pairs.
no code implementations • 27 Oct 2022 • Reo Yoneyama, Yi-Chiao Wu, Tomoki Toda
Our previous work, the unified source-filter GAN (uSFGAN) vocoder, introduced a novel architecture based on the source-filter theory into the parallel waveform generative adversarial network to achieve high voice quality and pitch controllability.
no code implementations • 12 May 2022 • Reo Yoneyama, Yi-Chiao Wu, Tomoki Toda
To improve the source excitation modeling and generated sound quality, a new source excitation generation network separately generating periodic and aperiodic components is proposed.
1 code implementation • 10 Apr 2021 • Reo Yoneyama, Yi-Chiao Wu, Tomoki Toda
We propose a unified approach to data-driven source-filter modeling using a single neural network for developing a neural vocoder capable of generating high-quality synthetic speech waveforms while retaining flexibility of the source-filter model to control their voice characteristics.