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arXiv · 1804.01849

A Large-Scale Study of Language Models for Chord Prediction

Filip Korzeniowski·David R. W. Sears·Gerhard Widmer
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Abstract

We conduct a large-scale study of language models for chord prediction. Specifically, we compare N-gram models to various flavours of recurrent neural networks on a comprehensive dataset comprising all publicly available datasets of annotated chords known to us. This large amount of data allows us to systematically explore hyper-parameter settings for the recurrent neural networks---a crucial step in achieving good results with this model class. Our results show not only a quantitative difference between the models, but also a qualitative one: in contrast to static N-gram models, certain RNN configurations adapt to the songs at test time. This finding constitutes a further step towards the development of chord recognition systems that are more aware of local musical context than what was previously possible.

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Filip Korzeniowski, David R. W. Sears, Gerhard Widmer. 2018-04-05. A Large-Scale Study of Language Models for Chord Prediction. https://arxiv.org/abs/1804.01849

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