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arXiv · 2010.00823

Deep Composer Classification Using Symbolic Representation

Sunghyeon Kim·Hyeyoon Lee·Sunjong Park·Jinho Lee·Keunwoo Choi
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Abstract

In this study, we train deep neural networks to classify composer on a symbolic domain. The model takes a two-channel two-dimensional input, i.e., onset and note activations of time-pitch representation, which is converted from MIDI recordings and performs a single-label classification. On the experiments conducted on MAESTRO dataset, we report an F1 value of 0.8333 for the classification of 13~classical composers.

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Sunghyeon Kim, Hyeyoon Lee, Sunjong Park, Jinho Lee, Keunwoo Choi. 2020-10-02. Deep Composer Classification Using Symbolic Representation. https://arxiv.org/abs/2010.00823

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