  Music classification is a task to classify a music piece into labels such as
genres or composers. We propose large-scale MIDI based composer classification
systems using GiantMIDI-Piano, a transcription-based dataset. We propose to use
piano rolls, onset rolls, and velocity rolls as input representations and use
deep neural networks as classifiers. To our knowledge, we are the first to
investigate the composer classification problem with up to 100 composers. By
using convolutional recurrent neural networks as models, our MIDI based
composer classification system achieves a 10-composer and a 100-composer
classification accuracies of 0.648 and 0.385 (evaluated on 30-second clips) and
0.739 and 0.489 (evaluated on music pieces), respectively. Our MIDI based
composer system outperforms several audio-based baseline classification
systems, indicating the effectiveness of using compact MIDI representations for
composer classification.
