Deep Composer Classification Using Symbolic Representation

• URL: http://arxiv.org/abs/2010.00823v2
• Date: Mon, 26 Oct 2020 14:03:26 GMT
• Title: Deep Composer Classification Using Symbolic Representation
• Authors: Sunghyeon Kim, Hyeyoon Lee, Sunjong Park, Jinho Lee, Keunwoo Choi
• Abstract summary: In this study, we train deep neural networks to classify composer on a symbolic domain. The model takes a two-channel two-dimensional input, 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 13classical composers.
• Score: 6.656753488329095
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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