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.
BibTeX
@inproceedings{
mehri2017samplernn,
title={Sample{RNN}: An Unconditional End-to-End Neural Audio Generation Model},
author={Soroush Mehri and Kundan Kumar and Ishaan Gulrajani and Rithesh Kumar and Shubham Jain and Jose Sotelo and Aaron Courville and Yoshua Bengio},
booktitle={International Conference on Learning Representations},
year={2017},
url={https://openreview.net/forum?id=SkxKPDv5xl}
}