A Closer Look at Deep Learning Neural Networks with Low-level Spectral Periodicity Features

Bob L. Sturm, Corey Kereliuk, Aggelos Pikrakis

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

7 Citations (Scopus)

Abstract

Systems built using deep learning neural networks trained on low-level spectral periodicity features (DeSPerF) reproduced the most “ground truth” of the systems submitted to the MIREX 2013 task, “Audio Latin Genre Classification.” To answer why this was the case, we take a closer look at the behavior of a DeSPerF system we create and evaluate using the benchmark dataset BALLROOM. We find through time stretching that this DeSPerF system appears to obtain a high figure of merit on the task of music genre recognition because of a confounding of tempo with “ground truth” in BALLROOM. This observation motivates several predictions.
Original languageEnglish
Title of host publicationProceedings of 4th International Workshop on Cognitive Information Processing (CIP 2014)
EditorsLars Kai Hansen, Søren Holdt Jensen, Jan Larsen
Number of pages6
Volume1
PublisherIEEE
Publication date2014
Pages1-6
ISBN (Print)978-1-4799-3696-0
DOIs
Publication statusPublished - 2014
EventInternational Workshop on Cognitive Information Processing - Bella Sky Hotel, Copenhagen, Denmark
Duration: 26 May 201428 May 2014

Conference

ConferenceInternational Workshop on Cognitive Information Processing
LocationBella Sky Hotel
CountryDenmark
CityCopenhagen
Period26/05/201428/05/2014

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Stretching
Neural networks
Deep learning

Cite this

Sturm, B. L., Kereliuk, C., & Pikrakis, A. (2014). A Closer Look at Deep Learning Neural Networks with Low-level Spectral Periodicity Features. In L. K. Hansen, S. Holdt Jensen, & J. Larsen (Eds.), Proceedings of 4th International Workshop on Cognitive Information Processing (CIP 2014) (Vol. 1, pp. 1-6). IEEE. https://doi.org/10.1109/CIP.2014.6844511
Sturm, Bob L. ; Kereliuk, Corey ; Pikrakis, Aggelos. / A Closer Look at Deep Learning Neural Networks with Low-level Spectral Periodicity Features. Proceedings of 4th International Workshop on Cognitive Information Processing (CIP 2014). editor / Lars Kai Hansen ; Søren Holdt Jensen ; Jan Larsen. Vol. 1 IEEE, 2014. pp. 1-6
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Sturm, BL, Kereliuk, C & Pikrakis, A 2014, A Closer Look at Deep Learning Neural Networks with Low-level Spectral Periodicity Features. in LK Hansen, S Holdt Jensen & J Larsen (eds), Proceedings of 4th International Workshop on Cognitive Information Processing (CIP 2014). vol. 1, IEEE, pp. 1-6, International Workshop on Cognitive Information Processing, Copenhagen, Denmark, 26/05/2014. https://doi.org/10.1109/CIP.2014.6844511

A Closer Look at Deep Learning Neural Networks with Low-level Spectral Periodicity Features. / Sturm, Bob L.; Kereliuk, Corey; Pikrakis, Aggelos.

Proceedings of 4th International Workshop on Cognitive Information Processing (CIP 2014). ed. / Lars Kai Hansen; Søren Holdt Jensen; Jan Larsen. Vol. 1 IEEE, 2014. p. 1-6.

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

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AB - Systems built using deep learning neural networks trained on low-level spectral periodicity features (DeSPerF) reproduced the most “ground truth” of the systems submitted to the MIREX 2013 task, “Audio Latin Genre Classification.” To answer why this was the case, we take a closer look at the behavior of a DeSPerF system we create and evaluate using the benchmark dataset BALLROOM. We find through time stretching that this DeSPerF system appears to obtain a high figure of merit on the task of music genre recognition because of a confounding of tempo with “ground truth” in BALLROOM. This observation motivates several predictions.

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Sturm BL, Kereliuk C, Pikrakis A. A Closer Look at Deep Learning Neural Networks with Low-level Spectral Periodicity Features. In Hansen LK, Holdt Jensen S, Larsen J, editors, Proceedings of 4th International Workshop on Cognitive Information Processing (CIP 2014). Vol. 1. IEEE. 2014. p. 1-6 https://doi.org/10.1109/CIP.2014.6844511