论文标题
使用符号表示的深层作曲家分类
Deep Composer Classification Using Symbolic Representation
论文作者
论文摘要
在这项研究中，我们训练深层神经网络，以对符号领域进行分类。该模型采用二维输入，即，时间和记录的时间键表示激活，从MIDI记录转换并执行单标签分类。在Maestro数据集上进行的实验中，我们报告了13〜经典作曲家分类的F1值为0.8333。
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.
