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

• A novel homogeneity-based method for music structure analysis is proposed.
• An Elastic Net (EN) induced similarity measure of audio features is proposed.
• The EN similarity measure is the heart of a novel subspace clustering method.
• The novel subspace clustering method is referred to as ENSC.
• Extensive experiments on the Beatles dataset demonstrate the power of the ENSC.

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.