Convex non-negative matrix factorization for automatic music structure identification

We propose a novel and fast approach to discover structure in western popular music by using a specific type of matrix factorization that adds a convex constrain to obtain a decomposition that can be interpreted as a set of weighted cluster centroids. We show that these centroids capture the differe...

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Veröffentlicht in:Proceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) S. 236 - 240
Hauptverfasser: Nieto, Oriol, Jehan, Tristan
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.05.2013
Schlagworte:
Clustering algorithms
Conferences
Feature extraction
Matrix decomposition
matrix factorization
Music information retrieval
music structure analysis
segmentation
Sparse matrices
Vectors
ISSN:1520-6149
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Zusammenfassung:We propose a novel and fast approach to discover structure in western popular music by using a specific type of matrix factorization that adds a convex constrain to obtain a decomposition that can be interpreted as a set of weighted cluster centroids. We show that these centroids capture the different sections of a musical piece (e.g. verse, chorus) in a more consistent and efficient way than classic non-negative matrix factorization. This technique is capable of identifying the boundaries of the sections and then grouping them into different clusters. Additionally, we evaluate this method on two different datasets and show that it is competitive compared to other music segmentation techniques, outperforming other matrix factorization methods.
ISSN:1520-6149
DOI:10.1109/ICASSP.2013.6637644