Learning the Beauty in Songs: Neural Singing Voice Beautifier

Jinglin Liu, Chengxi Li, Yi Ren, Zhiying Zhu, Zhou Zhao


Abstract
We are interested in a novel task, singing voice beautification (SVB). Given the singing voice of an amateur singer, SVB aims to improve the intonation and vocal tone of the voice, while keeping the content and vocal timbre. Current automatic pitch correction techniques are immature, and most of them are restricted to intonation but ignore the overall aesthetic quality. Hence, we introduce Neural Singing Voice Beautifier (NSVB), the first generative model to solve the SVB task, which adopts a conditional variational autoencoder as the backbone and learns the latent representations of vocal tone. In NSVB, we propose a novel time-warping approach for pitch correction: Shape-Aware Dynamic Time Warping (SADTW), which ameliorates the robustness of existing time-warping approaches, to synchronize the amateur recording with the template pitch curve. Furthermore, we propose a latent-mapping algorithm in the latent space to convert the amateur vocal tone to the professional one. To achieve this, we also propose a new dataset containing parallel singing recordings of both amateur and professional versions. Extensive experiments on both Chinese and English songs demonstrate the effectiveness of our methods in terms of both objective and subjective metrics. Audio samples are available at https://neuralsvb.github.io. Codes: https://github.com/MoonInTheRiver/NeuralSVB.
Anthology ID:
2022.acl-long.549
Volume:
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
7970–7983
Language:
URL:
https://aclanthology.org/2022.acl-long.549
DOI:
10.18653/v1/2022.acl-long.549
Bibkey:
Cite (ACL):
Jinglin Liu, Chengxi Li, Yi Ren, Zhiying Zhu, and Zhou Zhao. 2022. Learning the Beauty in Songs: Neural Singing Voice Beautifier. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 7970–7983, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
Learning the Beauty in Songs: Neural Singing Voice Beautifier (Liu et al., ACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.acl-long.549.pdf
Code
 moonintheriver/neuralsvb +  additional community code