September 14, 2019
We present a deep learning method for singing voice conversion. The proposed network is not conditioned on the text or on the notes, and it directly converts the audio of one singer to the voice of another. Training is performed without any form of supervision: no lyrics or any kind of phonetic features, no notes, and no matching samples between singers. The proposed network employs a single CNN encoder for all singers, a single WaveNet decoder, and a classifier that enforces the latent representation to be singer-agnostic. Each singer is represented by one embedding vector, which the decoder is conditioned on. In order to deal with relatively small datasets, we propose a new data augmentation scheme, as well as new training losses and protocols that are based on backtranslation. Our evaluation presents evidence that the conversion produces natural signing voices that are highly recognizable as the target singer.
Written by
Lior Wolf
Eliya Nachmani
Publisher
INTERSPEECH
July 23, 2024
Llama team
July 23, 2024
June 25, 2024
Min-Jae Hwang, Ilia Kulikov, Benjamin Peloquin, Hongyu Gong, Peng-Jen Chen, Ann Lee
June 25, 2024
June 05, 2024
Robin San Romin, Pierre Fernandez, Hady Elsahar, Alexandre Deffosez, Teddy Furon, Tuan Tran
June 05, 2024
May 24, 2024
May 24, 2024
Product experiences
Foundational models
Product experiences
Latest news
Foundational models