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

Panagakis, Yannis and Kotropoulos, Constantine (2014) Elastic net subspace clustering applied to pop/rock music structure analysis. Pattern Recognition Letters, 38 . pp. 46-53. ISSN 0167-8655

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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.

Item Type: Article
Research Areas: A. > School of Science and Technology > Computer Science
Item ID: 23762
Useful Links:
Depositing User: Yannis Panagakis
Date Deposited: 06 Mar 2018 17:22
Last Modified: 07 Dec 2018 08:34
URI: http://eprints.mdx.ac.uk/id/eprint/23762

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