Catch-A-Waveform: Learning to Generate Audio from a Single Short Example

TL;DR

This paper introduces a GAN-based model capable of learning from as little as 20 seconds of a single audio example to generate diverse, semantically similar audio samples, enabling various creative and restorative audio applications.

Contribution

The paper presents a novel single-example audio generation model that does not require pre-training or external supervision, achieving state-of-the-art results with minimal training data.

Findings

Effective with as little as 20 seconds of training audio

Can generate diverse, semantically similar audio samples

Enables applications like inpainting, super-resolution, and creative remixing

Abstract

Models for audio generation are typically trained on hours of recordings. Here, we illustrate that capturing the essence of an audio source is typically possible from as little as a few tens of seconds from a single training signal. Specifically, we present a GAN-based generative model that can be trained on one short audio signal from any domain (e.g. speech, music, etc.) and does not require pre-training or any other form of external supervision. Once trained, our model can generate random samples of arbitrary duration that maintain semantic similarity to the training waveform, yet exhibit new compositions of its audio primitives. This enables a long line of interesting applications, including generating new jazz improvisations or new a-cappella rap variants based on a single short example, producing coherent modifications to famous songs (e.g. adding a new verse to a Beatles song based solely on the original recording), filling-in of missing parts (inpainting), extending the bandwidth of a speech signal (super-resolution), and enhancing old recordings without access to any clean training example. We show that in all cases, no more than 20 seconds of training audio commonly suffice for our model to achieve state-of-the-art results. This is despite its complete lack of prior knowledge about the nature of audio signals in general.