  In this paper, a pitch-adaptive waveform generative model named
Quasi-Periodic WaveNet (QPNet) is proposed to improve the limited pitch
controllability of vanilla WaveNet (WN) using pitch-dependent dilated
convolution neural networks (PDCNNs). Specifically, as a probabilistic
autoregressive generation model with stacked dilated convolution layers, WN
achieves high-fidelity audio waveform generation. However, the pure-data-driven
nature and the lack of prior knowledge of audio signals degrade the pitch
controllability of WN. For instance, it is difficult for WN to precisely
generate the periodic components of audio signals when the given auxiliary
fundamental frequency ($F_{0}$) features are outside the $F_{0}$ range observed
in the training data. To address this problem, QPNet with two novel designs is
proposed. First, the PDCNN component is applied to dynamically change the
network architecture of WN according to the given auxiliary $F_{0}$ features.
Second, a cascaded network structure is utilized to simultaneously model the
long- and short-term dependencies of quasi-periodic signals such as speech. The
performances of single-tone sinusoid and speech generations are evaluated. The
experimental results show the effectiveness of the PDCNNs for unseen auxiliary
$F_{0}$ features and the effectiveness of the cascaded structure for speech
generation.
