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

Code

• github.com/soroushmehr/sampleRNN_ICLR2017
  OfficialIn papertorch★ 0
• github.com/cchinchristopherj/Concert-of-Whales
  none★ 0
• github.com/deepsound-project/samplernn-pytorch
  pytorch★ 0
• github.com/dada-bots/dadabots_sampleRNN
  none★ 0

Abstract

In this paper we propose a novel model for unconditional audio generation based on generating one audio sample at a time. We show that our model, which profits from combining memory-less modules, namely autoregressive multilayer perceptrons, and stateful recurrent neural networks in a hierarchical structure is able to capture underlying sources of variations in the temporal sequences over very long time spans, 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.

Tasks

Benchmark Results

Reproductions