  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.
