2017/06/27 by Xindi Wang, Wang, Xindi, Syed Arefinul Haque +1
Computer Science · #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Machine Learning (cs.LG) #Music Technology and Sound Studies #Music and Audio Processing #Sound (cs.SD) #Speech and Audio Processing
paper · pdf · doi:10.48550/arxiv.1706.08928
openalex publication_date 2017/06/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this paper we cluster 330 classical music pieces collected from MusicNet database based on their musical note sequence. We use shingling and chord trajectory matrices to create signature for each music piece and performed spectral clustering to find the clusters. Based on different resolution, the output clusters distinctively indicate composition from different classical music era and different composing style of the musicians.