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In this paper we propose a novel model for unconditional audio generationbased on generating one audio sample at a time. We show that our model, whichprofits from combining memory-less modules, namely autoregressive multilayerperceptrons, and stateful recurrent neural networks in a hierarchical structureis able to capture underlying sources of variations in the temporal sequencesover very long time spans, on three datasets of different nature. Humanevaluation on the generated samples indicate that our model is preferred overcompeting models. We also show how each component of the model contributes tothe exhibited performance.

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