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arXiv · 1811.08045

Coupled Recurrent Models for Polyphonic Music Composition

John Thickstun·Zaid Harchaoui·Dean P. Foster·Sham M. Kakade
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Abstract

This paper introduces a novel recurrent model for music composition that is tailored to the structure of polyphonic music. We propose an efficient new conditional probabilistic factorization of musical scores, viewing a score as a collection of concurrent, coupled sequences: i.e. voices. To model the conditional distributions, we borrow ideas from both convolutional and recurrent neural models; we argue that these ideas are natural for capturing music's pitch invariances, temporal structure, and polyphony. We train models for single-voice and multi-voice composition on 2,300 scores from the KernScores dataset.

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BibTeXRIS

John Thickstun, Zaid Harchaoui, Dean P. Foster, Sham M. Kakade. 2018-11-20. Coupled Recurrent Models for Polyphonic Music Composition. https://arxiv.org/abs/1811.08045

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