  Neural source-filter (NSF) models are deep neural networks that produce
waveforms given input acoustic features. They use dilated-convolution-based
neural filter modules to filter sine-based excitation for waveform generation,
which is different from WaveNet and flow-based models. One of the NSF models,
called harmonic-plus-noise NSF (h-NSF) model, uses separate pairs of source and
neural filters to generate harmonic and noise waveform components. It is close
to WaveNet in terms of speech quality while being superior in generation speed.
  The h-NSF model can be improved even further. While h-NSF merges the harmonic
and noise components using pre-defined digital low- and high-pass filters, it
is well known that the maximum voice frequency (MVF) that separates the
periodic and aperiodic spectral bands are time-variant. Therefore, we propose a
new h-NSF model with time-variant and trainable MVF. We parameterize the
digital low- and high-pass filters as windowed-sinc filters and predict their
cut-off frequency (i.e., MVF) from the input acoustic features. Our experiments
demonstrated that the new model can predict a good trajectory of the MVF and
produce high-quality speech for a text-to-speech synthesis system.
