  Neural vocoder using denoising diffusion probabilistic model (DDPM) has been
improved by adaptation of the diffusion noise distribution to given acoustic
features. In this study, we propose SpecGrad that adapts the diffusion noise so
that its time-varying spectral envelope becomes close to the conditioning
log-mel spectrogram. This adaptation by time-varying filtering improves the
sound quality especially in the high-frequency bands. It is processed in the
time-frequency domain to keep the computational cost almost the same as the
conventional DDPM-based neural vocoders. Experimental results showed that
SpecGrad generates higher-fidelity speech waveform than conventional DDPM-based
neural vocoders in both analysis-synthesis and speech enhancement scenarios.
Audio demos are available at wavegrad.github.io/specgrad/.
