Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Structure Detection for Popular Music
IEEE MultiMedia
Automatic transcription of melody, bass line, and chords in polyphonic music
Computer Music Journal
Music structure analysis using a probabilistic fitness measure and a greedy search algorithm
IEEE Transactions on Audio, Speech, and Language Processing
IEEE Transactions on Audio, Speech, and Language Processing
Structural Segmentation of Musical Audio by Constrained Clustering
IEEE Transactions on Audio, Speech, and Language Processing
Enhanced Sparse Imputation Techniques for a Robust Speech Recognition Front-End
IEEE Transactions on Audio, Speech, and Language Processing
On the $O(1/n)$ Convergence Rate of the Douglas-Rachford Alternating Direction Method
SIAM Journal on Numerical Analysis
Robust Recovery of Subspace Structures by Low-Rank Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
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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.