Classical Music Clustering Based on Acoustic Features
Information Retrieval
2017-06-28 v1 Machine Learning
Sound
Abstract
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.
Keywords
Cite
@article{arxiv.1706.08928,
title = {Classical Music Clustering Based on Acoustic Features},
author = {Xindi Wang and Syed Arefinul Haque},
journal= {arXiv preprint arXiv:1706.08928},
year = {2017}
}