Adversarial Audio Synthesis Using a Harmonic-Percussive Discriminator

In this paper, we propose a discriminator design scheme for generative adversarial network (GAN)-based audio signal generation.

Unlike conventional discriminators which take an entire signal as input, our discriminator design separates the audio signal into harmonic and percussive components and analyzes each component independently.
The rationale behind this idea is that conventional discriminators cannot reliably capture subtle distortions in general audio signals, which have complicated time-frequency characteristics.

By considering the time-frequency resolution of audio signals, our proposed method encourages the generator to better reconstruct harmonic and percussive features, which are critical for the quality of the generated signals.

Listening tests show that our framework significantly enhances the stability of pitches and generates clearer audio compared to a baseline.