Classical Music Clustering Based on Acoustic Features
This work addresses the problem of music analysis for researchers or enthusiasts by providing an incremental method to categorize classical music based on acoustic features.
The authors tackled the problem of clustering classical music pieces by era and composer style using acoustic features, achieving clusters that distinctively indicate composition from different classical music eras and styles based on spectral clustering applied to 330 pieces from the MusicNet database.
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.