Non-Negative Tensor Factorization Applied to Music Genre Classification

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Non-Negative Tensor Factorization Applied to Music Genre Classification
Music genre classification techniques are typically applied to the data matrix whose columns are the feature vectors extracted from music recordings. In this paper, a feature vector is extracted using a texture window of one sec, which enables the representation of any 30 sec long music recording as a time sequence of feature vectors, thus yielding a feature matrix. Consequently, by stacking the feature matrices associated to any dataset recordings, a tensor is created, a fact which necessitates studying music genre classification using tensors. First, a novel algorithm for non-negative tensor factorization (NTF) is derived that extends the non-negative matrix factorization. Several variants of the NTF algorithm emerge by employing different cost functions from the class of Bregman divergences. Second, a novel supervised NTF classifier is proposed, which trains a basis for each class separately and employs basis orthogonalization. A variety of spectral, temporal, perceptual, energy, an...
Emmanouil Benetos, Constantine Kotropoulos
Added 21 May 2011
Updated 21 May 2011
Type Journal
Year 2010
Authors Emmanouil Benetos, Constantine Kotropoulos
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