Fast Estimation of Optimal Sparseness of Music Signals
نویسنده
چکیده
We want to use a variety of sparseness measured applied to ‘the minimal `1 norm representation’ of a music signal in an over-complete dictionary as features for automatic classification of music. Unfortunately, the process of computing the optimal `1 norm representation is rather slow, and we therefore investigate the use of matching pursuit, alternating projection, and Moore-Penrose inverse for estimating the result of applying two different sparseness measures to ‘the minimal `1 norm representation’ without actually computing this representation.
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