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Recent progress in deep generative models has improved the quality of neuralvocoders in speech domain. However, generating a high-quality singing voiceremains challenging due to a wider variety of musical expressions in pitch,loudness, and pronunciations. In this work, we propose a hierarchical diffusionmodel for singing voice neural vocoders. The proposed method consists ofmultiple diffusion models operating in different sampling rates; the model atthe lowest sampling rate focuses on generating accurate low-frequencycomponents such as pitch, and other models progressively generate the waveformat higher sampling rates on the basis of the data at the lower sampling rateand acoustic features. Experimental results show that the proposed methodproduces high-quality singing voices for multiple singers, outperformingstate-of-the-art neural vocoders with a similar range of computational costs.

Hierarchical Diffusion Models for Singing Voice Neural Vocoder