  This paper introduces GlOttal-flow LPC Filter (GOLF), a novel method for
singing voice synthesis (SVS) that exploits the physical characteristics of the
human voice using differentiable digital signal processing. GOLF employs a
glottal model as the harmonic source and IIR filters to simulate the vocal
tract, resulting in an interpretable and efficient approach. We show it is
competitive with state-of-the-art singing voice vocoders, requiring fewer
synthesis parameters and less memory to train, and runs an order of magnitude
faster for inference. Additionally, we demonstrate that GOLF can model the
phase components of the human voice, which has immense potential for rendering
and analysing singing voices in a differentiable manner. Our results highlight
the effectiveness of incorporating the physical properties of the human voice
mechanism into SVS and underscore the advantages of signal-processing-based
approaches, which offer greater interpretability and efficiency in synthesis.
Audio samples are available at https://yoyololicon.github.io/golf-demo/.

Audio and Speech Processing;Sound