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Music Genre Classification: Genre-Specific Characterization and Pairwise Evaluation

Published: 12 September 2018 Publication History

Abstract

In this paper, we report our initial investigations on the genre classification problem in Music Information Retrieval. Each music genre has its unique characteristics, which distinguish it from other genres. We adapt association analysis and use it to capture those characteristics using acoustic features, i.e., each genre's characteristics are represented by a set of features and their corresponding values. In addition, we consider that each candidate genre should have its own chance to be singled out, and compete for a new piece to be classified. Therefore, we conduct genre classification based on a pairwise dichotomy-like strategy. We compare the differences of the characteristics of two genres in a symmetric manner and use them to classify music genres. The effectiveness of our approach is demonstrated through empirical experiments on one benchmark music dataset. The results are presented and discussed. Various related issues, such as potential future work along the same direction, are examined.

References

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Ritesh Ajoodha, Benjamin Rosman, and Richard Klein. 2015. Single-labelled music genre classification using content-based features. In Proceedings Pattern Recognition Association of South Africa and Robotics and Mechatronics International Conference. 66--71.
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Tom Arjannikov and John Zhang. 2015. An Empirical Study on Structured Dichotomies in Music Genre Classification. In Proceedings International Conference on Machine Learning and Applications. 493--496.
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Dhanith Chathuranga and Lakshman Jayaratne. 2012. Musical Genre Classification Using Ensemble of Classifiers. In Proceedings International Conference on Computational Intelligence, Modelling and Simulation. 237--242.
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Carlos N. Silla, Alessandro L. Koerich, and Celso A. A. Kaestner. 2008. A Machine Learning Approach to Automatic Music Genre Classification. Journal of the Brazilian Computer Society 14, 3 (2008), 7--18.
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cover image ACM Other conferences
AM '18: Proceedings of the Audio Mostly 2018 on Sound in Immersion and Emotion
September 2018
252 pages
ISBN:9781450366090
DOI:10.1145/3243274
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Association for Computing Machinery

New York, NY, United States

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Published: 12 September 2018

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AM'18
AM'18: Sound in Immersion and Emotion
September 12 - 14, 2018
Wrexham, United Kingdom

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Overall Acceptance Rate 177 of 275 submissions, 64%

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