PeriodGrad: Towards Pitch-Controllable Neural Vocoder Based on a Diffusion Probabilistic Model
This paper presents a neural vocoder based on a denoising diffusion probabilistic model (DDPM) incorporating explicit periodic signals as auxiliary conditioning signals. Recently, DDPM-based neural vocoders have gained prominence as non-autoregressive models that can generate high-quality waveforms....
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| Vydané v: | Proceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) s. 12782 - 12786 |
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14.04.2024
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| Abstract | This paper presents a neural vocoder based on a denoising diffusion probabilistic model (DDPM) incorporating explicit periodic signals as auxiliary conditioning signals. Recently, DDPM-based neural vocoders have gained prominence as non-autoregressive models that can generate high-quality waveforms. The neural vocoders based on DDPM have the advantage of training with a simple time-domain loss. In practical applications, such as singing voice synthesis, there is a demand for neural vocoders to generate high-fidelity speech waveforms with flexible pitch control. However, conventional DDPM-based neural vocoders struggle to generate speech waveforms under such conditions. Our proposed model aims to accurately capture the periodic structure of speech waveforms by incorporating explicit periodic signals. Experimental results show that our model improves sound quality and provides better pitch control than conventional DDPM-based neural vocoders. |
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| AbstractList | This paper presents a neural vocoder based on a denoising diffusion probabilistic model (DDPM) incorporating explicit periodic signals as auxiliary conditioning signals. Recently, DDPM-based neural vocoders have gained prominence as non-autoregressive models that can generate high-quality waveforms. The neural vocoders based on DDPM have the advantage of training with a simple time-domain loss. In practical applications, such as singing voice synthesis, there is a demand for neural vocoders to generate high-fidelity speech waveforms with flexible pitch control. However, conventional DDPM-based neural vocoders struggle to generate speech waveforms under such conditions. Our proposed model aims to accurately capture the periodic structure of speech waveforms by incorporating explicit periodic signals. Experimental results show that our model improves sound quality and provides better pitch control than conventional DDPM-based neural vocoders. |
| Author | Tokuda, Keiichi Hashimoto, Kei Nankaku, Yoshihiko Hono, Yukiya |
| Author_xml | – sequence: 1 givenname: Yukiya surname: Hono fullname: Hono, Yukiya organization: Nagoya Institute of Technology,Nagoya,Japan – sequence: 2 givenname: Kei surname: Hashimoto fullname: Hashimoto, Kei organization: Nagoya Institute of Technology,Nagoya,Japan – sequence: 3 givenname: Yoshihiko surname: Nankaku fullname: Nankaku, Yoshihiko organization: Nagoya Institute of Technology,Nagoya,Japan – sequence: 4 givenname: Keiichi surname: Tokuda fullname: Tokuda, Keiichi organization: Nagoya Institute of Technology,Nagoya,Japan |
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| Snippet | This paper presents a neural vocoder based on a denoising diffusion probabilistic model (DDPM) incorporating explicit periodic signals as auxiliary... |
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| SubjectTerms | Controllability diffusion probabilistic model neural vocoder pitch controllability Probabilistic logic Robustness Signal processing singing voice synthesis Speech processing Speech synthesis Training Vocoders |
| Title | PeriodGrad: Towards Pitch-Controllable Neural Vocoder Based on a Diffusion Probabilistic Model |
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