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The problem of automatic music mood classification is addressed by resorting to low-rank representation of slow auditory spectro-temporal modulations. Recently, it has been shown that if each data class is linearly spanned by a subspace of unknown dimensions and the data are noiseless, the lowest-rank representation (LRR) of a set of test vector samples with respect to a set of training vector samples...
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