Generative models in vision have seen rapid progress due to algorithmic improvements and
the availability of high-quality image datasets. In this paper, we offer contributions in both …

Recent progress in deep learning for audio synthesis opens the way to models that directly
produce the waveform, shifting away from the traditional paradigm of relying on vocoders or …

This paper introduces WaveNet, a deep neural network for generating raw audio waveforms.
The model is fully probabilistic and autoregressive, with the predictive distribution for each …

Deep generative models applied to audio have improved by a large margin the state-of-the-
art in many speech and music related tasks. However, as raw waveform modelling remains …

We present the Neural Waveshaping Unit (NEWT): a novel, lightweight, fully causal
approach to neural audio synthesis which operates directly in the waveform domain, with an …

This paper proposes a novel way of doing audio synthesis at the waveform level using
Transformer architectures. We propose a deep neural network for generating waveforms …

Efficient audio synthesis is an inherently difficult machine learning task, as human
perception is sensitive to both global structure and fine-scale waveform coherence …

An ideal music synthesizer should be both interactive and expressive, generating high-
fidelity audio in realtime for arbitrary combinations of instruments and notes. Recent neural …

Most generative models of audio directly generate samples in one of two domains: time or
frequency. While sufficient to express any signal, these representations are inefficient, as …

Most existing neural network models for music generation use recurrent neural networks.
However, the recent WaveNet model proposed by DeepMind shows that convolutional …