Melodic Phrase Attention Network for Symbolic Data-based Music Genre Classification (Student Abstract)

Authors

  • Li Li South China University of Technology
  • Rui Zhang South China University of Technology
  • Zhenyu Wang South China University of Technology

Keywords:

Music Genre Classification, Music Information Retrieval, Deep Learning

Abstract

Compared with audio data-based music genre classification, researches on symbolic data-based music are scarce. Existing methods generally utilize manually extracted features, which is very time-consuming and laborious, and use traditional classifiers for label prediction without considering specific music features. To tackle this issue, we propose the Melodic Phrase Attention Network (MPAN) for symbolic data-based music genre classification. Our model is trained in three steps: First, we adopt representation learning, instead of the traditional musical feature extraction method, to obtain a vectorized representation of the music pieces. Second, the music pieces are divided into several melodic phrases through melody segmentation. Finally, the Melodic Phrase Attention Network is designed according to music characteristics, to identify the reflection of each melodic phrase on the music genre, thereby generating more accurate predictions. Experimental results show that our proposed method is superior to baseline symbolic data-based music genre classification approaches, and has achieved significant performance improvements on two large datasets.

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Published

2021-05-18

How to Cite

Li, L., Zhang, R., & Wang, Z. (2021). Melodic Phrase Attention Network for Symbolic Data-based Music Genre Classification (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 35(18), 15825-15826. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17909

Issue

Section

AAAI Student Abstract and Poster Program