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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Published in:Proceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) pp. 236 - 240
Main Authors: Nieto, Oriol, Jehan, Tristan
Format: Conference Proceeding
Language:English
Published: IEEE 01.05.2013
Subjects:
Clustering algorithms
Conferences
Feature extraction
Matrix decomposition
matrix factorization
Music information retrieval
music structure analysis
segmentation
Sparse matrices
Vectors
ISSN:1520-6149
Online Access:Get full text
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Abstract 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.
AbstractList 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.
Author Nieto, Oriol
Jehan, Tristan
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  givenname: Tristan
  surname: Jehan
  fullname: Jehan, Tristan
  email: tristan@echonest.com
  organization: Echo Nest, USA
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Snippet 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...
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StartPage 236
SubjectTerms Clustering algorithms
Conferences
Feature extraction
Matrix decomposition
matrix factorization
Music information retrieval
music structure analysis
segmentation
Sparse matrices
Vectors
Title Convex non-negative matrix factorization for automatic music structure identification
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