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

Bob L. Sturm, Pardis Noorzad

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Resumé

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 improves
when we pose the sparse representation problem
with inequality constraints.
Finally, we find that only when we reduce the number of classes by half
do we see the high accuracies reported in \cite{Panagakis2009}.
OriginalsprogEngelsk
TitelProceedings of the 9th International Symposium on Computer Music Modeling and Retrieval
Udgivelses stedLondon
Publikationsdato2012
Sider379-394
StatusUdgivet - 2012
BegivenhedComputer music modeling and retrieval - London, Storbritannien
Varighed: 19 jun. 201222 jun. 2012

Konference

KonferenceComputer music modeling and retrieval
LandStorbritannien
ByLondon
Periode19/06/201222/06/2012

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Modulation
Automatic teller machines
Acoustics

Citer dette

Sturm, B. L., & Noorzad, P. (2012). On Automatic Music Genre Recognition by Sparse Representation Classification using Auditory Temporal Modulations. I Proceedings of the 9th International Symposium on Computer Music Modeling and Retrieval (s. 379-394). London.
Sturm, Bob L. ; Noorzad, Pardis. / On Automatic Music Genre Recognition by Sparse Representation Classification using Auditory Temporal Modulations. Proceedings of the 9th International Symposium on Computer Music Modeling and Retrieval. London, 2012. s. 379-394
@inproceedings{a7c2e0cc5c51463fb71f26501f6f16aa,
title = "On Automatic Music Genre Recognition by Sparse Representation Classification using Auditory Temporal Modulations",
abstract = "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}.",
author = "Sturm, {Bob L.} and Pardis Noorzad",
year = "2012",
language = "English",
pages = "379--394",
booktitle = "Proceedings of the 9th International Symposium on Computer Music Modeling and Retrieval",

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Sturm, BL & Noorzad, P 2012, On Automatic Music Genre Recognition by Sparse Representation Classification using Auditory Temporal Modulations. i Proceedings of the 9th International Symposium on Computer Music Modeling and Retrieval. London, s. 379-394, London, Storbritannien, 19/06/2012.

On Automatic Music Genre Recognition by Sparse Representation Classification using Auditory Temporal Modulations. / Sturm, Bob L.; Noorzad, Pardis.

Proceedings of the 9th International Symposium on Computer Music Modeling and Retrieval. London, 2012. s. 379-394.

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

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AB - 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}.

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BT - Proceedings of the 9th International Symposium on Computer Music Modeling and Retrieval

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Sturm BL, Noorzad P. On Automatic Music Genre Recognition by Sparse Representation Classification using Auditory Temporal Modulations. I Proceedings of the 9th International Symposium on Computer Music Modeling and Retrieval. London. 2012. s. 379-394