Title: Classical Music Clustering Based on Acoustic Features,
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.