Deep Composer Classification Using Symbolic Representation
Sunghyeon Kim, Hyeyoon Lee, Sunjong Park, Jinho Lee, Keunwoo Choi · arXiv (Cornell University) · 2020
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.