Full-band General Audio Synthesis with Score-based Diffusion
Recent works have shown the capability of deep generative models to tackle
general audio synthesis from a single label, producing a variety of impulsive,
tonal, and environmental sounds. Such models operate on band-limited signals
and, as a result of an autoregressive approach, they are typically conformed by
pre-trained latent encoders and/or several cascaded modules. In this work, we
propose a diffusion-based generative model for general audio synthesis, named
DAG, which deals with full-band signals end-to-end in the waveform domain.
Results show the superiority of DAG over existing label-conditioned generators
in terms of both quality and diversity. More specifically, when compared to the
state of the art, the band-limited and full-band versions of DAG achieve
relative improvements that go up to 40 and 65%, respectively. We believe DAG is
flexible enough to accommodate different conditioning schemas while providing
good quality synthesis.