Elastic net subspace clustering applied to pop/rock music structure analysis

Article


Panagakis, Y. and Kotropoulos, C. 2014. Elastic net subspace clustering applied to pop/rock music structure analysis. Pattern Recognition Letters. 38, pp. 46-53. https://doi.org/10.1016/j.patrec.2013.10.021
TypeArticle
TitleElastic net subspace clustering applied to pop/rock music structure analysis
AuthorsPanagakis, Y. and Kotropoulos, C.
Abstract

A novel homogeneity-based method for music structure analysis is proposed. The heart of the method is a similarity measure, derived from first principles, that is based on the matrix Elastic Net (EN) regularization and deals efficiently with highly correlated audio feature vectors. In particular, beat-synchronous mel-frequency cepstral coefficients, chroma features, and auditory temporal modulations model the audio signal. The EN induced similarity measure is employed to construct an affinity matrix, yielding a novel subspace clustering method referred to as Elastic Net subspace clustering (ENSC). The performance of the ENSC in structure analysis is assessed by conducting extensive experiments on the Beatles dataset. The experimental findings demonstrate the descriptive power of the EN-based affinity matrix over the affinity matrices employed in subspace clustering methods, attaining the state-of-the-art performance reported for the Beatles dataset.

PublisherElsevier
JournalPattern Recognition Letters
ISSN0167-8655
Publication dates
Online12 Nov 2013
Print01 Mar 2014
Publication process dates
Deposited06 Mar 2018
Accepted01 Nov 2013
Output statusPublished
Accepted author manuscript
License
Digital Object Identifier (DOI)https://doi.org/10.1016/j.patrec.2013.10.021
LanguageEnglish
Permalink -

https://repository.mdx.ac.uk/item/87847

Download files


Accepted author manuscript
  • 13
    total views
  • 4
    total downloads
  • 0
    views this month
  • 0
    downloads this month

Export as