Noorzad, Pardis

Sturm, Bob L.

English

A recent system combining sparse representation classification (SRC) and a perceptually-based acoustic feature (ATM) \cite{Panagakis2009,Panagakis2009b,Panagakis2010c},outperforms by a significant margin the state of the art in music genre recognition, e.g., \cite{Bergstra2006}.With genre so difficult to define, and seemingly based on factors more broad than acoustics,this remarkable result motivates investigation into, among other things, why it works and what it means for how humans organize music.In this paper, we review the application of SRC and ATM to recognizing genre, and attempt to reproduce the results of \cite{Panagakis2009}.First, we find that classification results are consistent for features extracted from different analyses.Second, we find that SRC accuracy improveswhen we pose the sparse representation problem with inequality constraints.Finally, we find that only when we reduce the number of classes by halfdo we see the high accuracies reported in \cite{Panagakis2009}

On Automatic Music Genre Recognition by Sparse Representation Classification using Auditory Temporal Modulations

https://vbn.aau.dk/ws/files/75169177/CMMR2012ProcceedingsFinal.pdf

On Automatic Music Genre Recognition by Sparse Representation Classification using Auditory Temporal Modulations

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