Subband wavenet with overlapped single-sideband filterbanks

Abstract

Compared with conventional vocoders, deep neural network-based raw audio generative models, such as WaveNet and SampleRNN, can more naturally synthesize speech signals, although the synthesis speed is a problem, especially with high sampling frequency. This paper provides subband WaveNet based on multirate signal processing for high-speed and high-quality synthesis with raw audio generative models. In the training stage, speech waveforms are decomposed and decimated into subband short waveforms with a low sampling rate, and each subband WaveNet network is trained using each subband stream. In the synthesis stage, each generated signal is up-sampled and integrated into a fullband speech signal. The results of objective and subjective experiments for unconditional WaveNet with a sampling frequency of 32 kHz indicate that the proposed subband WaveNet with a square-root Hann window-based overlapped 9-channel single-sideband filterbank can realize about four times the synthesis speed and improve the synthesized speech quality more than the conventional fullband WaveNet.