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arXiv · 2002.10266

Rhythm, Chord and Melody Generation for Lead Sheets using Recurrent Neural Networks

Cedric De Boom·Stephanie Van Laere·Tim Verbelen·Bart Dhoedt
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Abstract

Music that is generated by recurrent neural networks often lacks a sense of direction and coherence. We therefore propose a two-stage LSTM-based model for lead sheet generation, in which the harmonic and rhythmic templates of the song are produced first, after which, in a second stage, a sequence of melody notes is generated conditioned on these templates. A subjective listening test shows that our approach outperforms the baselines and increases perceived musical coherence.

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Cedric De Boom, Stephanie Van Laere, Tim Verbelen, Bart Dhoedt. 2020-02-21. Rhythm, Chord and Melody Generation for Lead Sheets using Recurrent Neural Networks. https://arxiv.org/abs/2002.10266

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