  The present paper describes 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. As singing voices represent a rich form of
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 for each segment that consists
of long-term frames, and a natural trajectory is obtained without the parameter
generation algorithm. Furthermore, a computational complexity reduction
technique, which drives the DNNs in different time units depending on type of
musical score features, is proposed. Experimental results show that the
proposed method can synthesize natural sounding singing voices much faster than
the conventional method.
