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Deep Composer Classification Using Symbolic Representation (2010.00823v2)

Published 2 Oct 2020 in cs.SD, cs.LG, and eess.AS

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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