论文标题
大规模MIDI的作曲家分类
Large-Scale MIDI-based Composer Classification
论文作者
论文摘要
音乐分类是将音乐作品分类为类型或作曲家等标签的任务。我们建议使用基于转录的数据集Giantmidi-Piano提出大规模MIDI的作曲家分类系统。我们建议将钢琴卷，发卷和速度卷作为输入表示形式，并将深层神经网络用作分类器。据我们所知，我们是第一个研究作曲家分类问题的人，其中有100位作曲家。通过使用卷积复发性神经网络作为模型，我们的基于MIDI的作曲家分类系统可实现10个组合器和100个组合分类的精度为0.648和0.385（在30秒剪辑上进行评估）和0.739和0.489和0.489（分别在音乐上进行了评估）。我们的基于MIDI的作曲家系统优于基于音频的基线分类系统，这表明使用紧凑的MIDI表示作曲家分类具有有效性。
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.
