SampleRNN: An Unconditional End-to-End Neural Audio Generation Model

Soroush Mehri, Kundan Kumar, Ishaan Gulrajani, Rithesh Kumar, Shubham Jain, Jose Sotelo, Aaron Courville, Yoshua Bengio

Abstract: In this paper we propose a novel model for unconditional audio generation task that generates one audio sample at a time. We show that our model which profits from combining memory-less modules, namely autoregressive multilayer perceptron, and stateful recurrent neural networks in a hierarchical structure is de facto powerful to capture the underlying sources of variations in temporal domain for very long time on three datasets of different nature. Human evaluation on the generated samples indicate that our model is preferred over competing models. We also show how each component of the model contributes to the exhibited performance.

TL;DR: Novel model for unconditional audio generation task using hierarchical multi-scale RNNs and autoregressive MLP.

Keywords: Speech, Deep learning, Unsupervised Learning, Applications

Conflicts: umontreal.ca, iitk.ac.in

Community Implementations: [ 2 code implementations](https://www.catalyzex.com/paper/arxiv:1612.07837/code)