Classical Music Clustering Based on Acoustic Features

TL;DR

This paper presents a method for clustering classical music pieces using acoustic features, spectral clustering, and musical signatures to distinguish different eras and styles.

Contribution

It introduces a novel approach combining shingling and chord trajectory matrices for music clustering based on musical note sequences.

Findings

Clusters correspond to different classical music eras.

Clustering reveals stylistic differences among composers.

Method effectively distinguishes musical periods.

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.