Singing voice synthesis based on convolutional neural networks

Abstract

The present paper describes a singing voice synthesis based on convolutional neural networks (CNNs). Singing voice synthesis systems based on deep neural networks (DNNs) are currently being proposed and are improving the naturalness of synthesized singing voices. In these systems, the relationship between musical score feature sequences and acoustic feature sequences extracted from singing voices is modeled by DNNs. Then, an acoustic feature sequence of an arbitrary musical score is output in units of frames by the trained DNNs, and a natural trajectory of a singing voice is obtained by using a parameter generation algorithm. As singing voices contain rich expression, a powerful technique to model them accurately is required. In the proposed technique, long-term dependencies of singing voices are modeled by CNNs. An acoustic feature sequence is generated in units of segments that consist of long-term frames, and a natural trajectory is obtained without the parameter generation algorithm. Experimental results in a subjective listening test show that the proposed architecture can synthesize natural sounding singing voices.

Singing voice samples (Japanese, English, Chinese): https://www.techno-speech.com/news-20181214a-en

References in corpus (1)

• WaveNet: A Generative Model for Raw Audio

Cited by in corpus (5)

• A Comprehensive Survey on Deep Music Generation: Multi-level Representations, Algorithms, Evaluations, and Future Directions
• HiFiSinger: Towards High-Fidelity Neural Singing Voice Synthesis
• DeepSinger: Singing Voice Synthesis with Data Mined From the Web
• Speech-to-Singing Conversion based on Boundary Equilibrium GAN
• Learn2Sing: Target Speaker Singing Voice Synthesis by learning from a Singing Teacher