  Neural source-filter (NSF) waveform models generate speech waveforms by
morphing sine-based source signals through dilated convolution in the time
domain. Although the sine-based source signals help the NSF models to produce
voiced sounds with specified pitch, the sine shape may constrain the generated
waveform when the target voiced sounds are less periodic. In this paper, we
propose a more flexible source signal called cyclic noise, a quasi-periodic
noise sequence given by the convolution of a pulse train and a static random
noise with a trainable decaying rate that controls the signal shape. We further
propose a masked spectral loss to guide the NSF models to produce periodic
voiced sounds from the cyclic noise-based source signal. Results from a
large-scale listening test demonstrated the effectiveness of the cyclic noise
and the masked spectral loss on speaker-independent NSF models in
copy-synthesis experiments on the CMU ARCTIC database.
