Deep Composer Classification Using Symbolic Representation
Sound
2020-10-27 v2 Machine Learning
Audio and Speech Processing
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.
Keywords
Cite
@article{arxiv.2010.00823,
title = {Deep Composer Classification Using Symbolic Representation},
author = {Sunghyeon Kim and Hyeyoon Lee and Sunjong Park and Jinho Lee and Keunwoo Choi},
journal= {arXiv preprint arXiv:2010.00823},
year = {2020}
}