  A vocoder is a conditional audio generation model that converts acoustic
features such as mel-spectrograms into waveforms. Taking inspiration from
Differentiable Digital Signal Processing (DDSP), we propose a new vocoder named
SawSing for singing voices. SawSing synthesizes the harmonic part of singing
voices by filtering a sawtooth source signal with a linear time-variant finite
impulse response filter whose coefficients are estimated from the input
mel-spectrogram by a neural network. As this approach enforces phase
continuity, SawSing can generate singing voices without the phase-discontinuity
glitch of many existing vocoders. Moreover, the source-filter assumption
provides an inductive bias that allows SawSing to be trained on a small amount
of data. Our experiments show that SawSing converges much faster and
outperforms state-of-the-art generative adversarial network and diffusion-based
vocoders in a resource-limited scenario with only 3 training recordings and a
3-hour training time.
