Speech-to-Singing Conversion based on Boundary Equilibrium GAN

• URL: http://arxiv.org/abs/2005.13835v3
• Date: Wed, 5 Aug 2020 13:21:32 GMT
• Title: Speech-to-Singing Conversion based on Boundary Equilibrium GAN
• Authors: Da-Yi Wu, Yi-Hsuan Yang
• Abstract summary: This paper investigates the use of generative adversarial network (GAN)-based models for converting the spectrogram of a speech signal into that of a singing one. The proposed model generates singing voices with much higher naturalness than an existing non adversarially-trained baseline.
• Score: 42.739822506085694
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: This paper investigates the use of generative adversarial network (GAN)-based models for converting the spectrogram of a speech signal into that of a singing one, without reference to the phoneme sequence underlying the speech. This is achieved by viewing speech-to-singing conversion as a style transfer problem. Specifically, given a speech input, and optionally the F0 contour of the target singing, the proposed model generates as the output a singing signal with a progressive-growing encoder/decoder architecture and boundary equilibrium GAN loss functions. Our quantitative and qualitative analysis show that the proposed model generates singing voices with much higher naturalness than an existing non adversarially-trained baseline. For reproducibility, the code will be publicly available at a GitHub repository upon paper publication.

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