SingIt! Singer Voice Transformation

Fuente: arXiv
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Bibliographic Details
Main Authors: Eliav, Amit, Taub, Aaron, Opochinsky, Renana, Gannot, Sharon
Format: Preprint
Published: 2024
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author Eliav, Amit
Taub, Aaron
Opochinsky, Renana
Gannot, Sharon
author_facet Eliav, Amit
Taub, Aaron
Opochinsky, Renana
Gannot, Sharon
contents In this paper, we propose a model which can generate a singing voice from normal speech utterance by harnessing zero-shot, many-to-many style transfer learning. Our goal is to give anyone the opportunity to sing any song in a timely manner. We present a system comprising several available blocks, as well as a modified auto-encoder, and show how this highly-complex challenge can be achieved by tailoring rather simple solutions together. We demonstrate the applicability of the proposed system using a group of 25 non-expert listeners. Samples of the data generated from our model are provided.
format Preprint
id arxiv_https___arxiv_org_abs_2405_04627
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle SingIt! Singer Voice Transformation
Eliav, Amit
Taub, Aaron
Opochinsky, Renana
Gannot, Sharon
Audio and Speech Processing
Sound
In this paper, we propose a model which can generate a singing voice from normal speech utterance by harnessing zero-shot, many-to-many style transfer learning. Our goal is to give anyone the opportunity to sing any song in a timely manner. We present a system comprising several available blocks, as well as a modified auto-encoder, and show how this highly-complex challenge can be achieved by tailoring rather simple solutions together. We demonstrate the applicability of the proposed system using a group of 25 non-expert listeners. Samples of the data generated from our model are provided.
title SingIt! Singer Voice Transformation
topic Audio and Speech Processing
Sound
url https://arxiv.org/abs/2405.04627