SingIt! Singer Voice Transformation
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866914788257300480 |
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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 |