MelNet: A Generative Model for Audio in the Frequency Domain

Sean Vasquez, Mike Lewis

TL;DR: We introduce an autoregressive generative model for spectrograms and demonstrate applications to speech and music generation

Abstract: Capturing high-level structure in audio waveforms is challenging because a single second of audio spans tens of thousands of timesteps. While long-range dependencies are difficult to model directly in the time domain, we show that they can be more tractably modelled in two-dimensional time-frequency representations such as spectrograms. By leveraging this representational advantage, in conjunction with a highly expressive probabilistic model and a multiscale generation procedure, we design a model capable of generating high-fidelity audio samples which capture structure at timescales which time-domain models have yet to achieve. We demonstrate that our model captures longer-range dependencies than time-domain models such as WaveNet across a diverse set of unconditional generation tasks, including single-speaker speech generation, multi-speaker speech generation, and music generation.

Community Implementations: [ 4 code implementations](https://www.catalyzex.com/paper/arxiv:1906.01083/code)