Elastic Net subspace clustering applied to pop/rock music structure analysis
نویسندگان
چکیده
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, beatsynchronous 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. Corresponding author: Yannis Panagakis, Dept. Informatics, Aristotle University of Thessaloniki, Box 451 Thessaloniki, GR-54124, GREECE, Tel. +30-697-40-21-752, Fax. +30-231-099-8453 emails: [email protected]; [email protected] Preprint submitted to Pattern Recognition Letters October 3, 2013
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ورودعنوان ژورنال:
- Pattern Recognition Letters
دوره 38 شماره
صفحات -
تاریخ انتشار 2014