A machine learning approach to musically meaningful homogeneous style classification

William Herlands, Yoel Greenberg, Ricky Der, Simon Asher Levin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations

Abstract

Recent literature has demonstrated the difficulty of classifying between composers who write in extremely similar styles (homogeneous style). Additionally, machine learning studies in this field have been exclusively of technical import with little musicological interpretability or significance. We present a supervised machine learning system which addresses the difficulty of differentiating between stylistically homogeneous composers using foundational elements of music, their complexity and interaction. Our work expands on previous style classification studies by developing more complex features as well as introducing a new class of musical features which focus on local irregularities within musical scores. We demonstrate the discriminative power of the system as applied to Haydn and Mozart's string quartets. Our results yield interpretable musicological conclusions about Haydn's and Mozart's stylistic differences while distinguishing between the composers with higher accuracy than previous studies in this domain.

Original languageEnglish (US)
Title of host publicationProceedings of the 28th AAAI Conference on Artificial Intelligence and the 26th Innovative Applications of Artificial Intelligence Conference and the 5th Symposium on Educational Advances in Artificial Intelligence
PublisherAI Access Foundation
Pages276-282
Number of pages7
Volume1
ISBN (Electronic)9781577356776
StatePublished - Jan 1 2014
Event28th AAAI Conference on Artificial Intelligence, AAAI 2014, 26th Innovative Applications of Artificial Intelligence Conference, IAAI 2014 and the 5th Symposium on Educational Advances in Artificial Intelligence, EAAI 2014 - Quebec City, Canada
Duration: Jul 27 2014Jul 31 2014

Other

Other28th AAAI Conference on Artificial Intelligence, AAAI 2014, 26th Innovative Applications of Artificial Intelligence Conference, IAAI 2014 and the 5th Symposium on Educational Advances in Artificial Intelligence, EAAI 2014
CountryCanada
CityQuebec City
Period7/27/147/31/14

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence

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    Herlands, W., Greenberg, Y., Der, R., & Levin, S. A. (2014). A machine learning approach to musically meaningful homogeneous style classification. In Proceedings of the 28th AAAI Conference on Artificial Intelligence and the 26th Innovative Applications of Artificial Intelligence Conference and the 5th Symposium on Educational Advances in Artificial Intelligence (Vol. 1, pp. 276-282). AI Access Foundation.