BacHMMachine: An Interpretable and Scalable Model for Algorithmic Harmonization for Four-part Baroque Chorales

• URL: http://arxiv.org/abs/2109.07623v1
• Date: Wed, 15 Sep 2021 23:39:45 GMT
• Title: BacHMMachine: An Interpretable and Scalable Model for Algorithmic Harmonization for Four-part Baroque Chorales
• Authors: Yunyao Zhu, Stephen Hahn, Simon Mak, Yue Jiang, Cynthia Rudin
• Abstract summary: BacHMMachine employs a "theory-driven" framework guided by music composition principles. It provides a probabilistic framework for learning key modulations and chordal progressions from a given melodic line. It results in vast decreases in computational burden and greater interpretability.
• Score: 23.64897650817862
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Algorithmic harmonization - the automated harmonization of a musical piece given its melodic line - is a challenging problem that has garnered much interest from both music theorists and computer scientists. One genre of particular interest is the four-part Baroque chorales of J.S. Bach. Methods for algorithmic chorale harmonization typically adopt a black-box, "data-driven" approach: they do not explicitly integrate principles from music theory but rely on a complex learning model trained with a large amount of chorale data. We propose instead a new harmonization model, called BacHMMachine, which employs a "theory-driven" framework guided by music composition principles, along with a "data-driven" model for learning compositional features within this framework. As its name suggests, BacHMMachine uses a novel Hidden Markov Model based on key and chord transitions, providing a probabilistic framework for learning key modulations and chordal progressions from a given melodic line. This allows for the generation of creative, yet musically coherent chorale harmonizations; integrating compositional principles allows for a much simpler model that results in vast decreases in computational burden and greater interpretability compared to state-of-the-art algorithmic harmonization methods, at no penalty to quality of harmonization or musicality. We demonstrate this improvement via comprehensive experiments and Turing tests comparing BacHMMachine to existing methods.

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