A Machine Learning Approach to Musically Meaningful Homogeneous Style Classification

Yoel Greenberg, William Herlands, Ricky Der, Simon Levin

Research output: Contribution to journalConference articlepeer-review


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 languageAmerican English
Pages (from-to)276-282
Number of pages7
JournalProceedings of the AAAI Conference on Artificial Intelligence
Issue number1
StatePublished - 19 Jun 2014


  • Supervised machine learning
  • information retrieval
  • Classical music
  • Mozart,
  • Haydn


Dive into the research topics of 'A Machine Learning Approach to Musically Meaningful Homogeneous Style Classification'. Together they form a unique fingerprint.

Cite this