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Analysing Symbolic Music with Probabilistic Grammars

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Computational Music Analysis

Abstract

Recent developments in computational linguistics offer ways to approach the analysis of musical structure by inducing probabilistic models (in the form of grammars) over a corpus of music. These can produce idiomatic sentences from a probabilistic model of the musical language and thus offer explanations of the musical structures they model. This chapter surveys historical and current work in musical analysis using grammars, based on computational linguistic approaches. We outline the theory of probabilistic grammars and illustrate their implementation in Prolog using PRISM. Our experiments on learning the probabilities for simple grammars from pitch sequences in two kinds of symbolic musical corpora are summarized. The results support our claim that probabilistic grammars are a promising framework for computational music analysis, but also indicate that further work is required to establish their superiority over Markov models.

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Authors and Affiliations

  1. Department of Computer Science, University College London, London, UK

    Samer Abdallah & Nicolas Gold

  2. Lancaster Institute for the Contemporary Arts, Lancaster University, Lancaster, UK

    Alan Marsden

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  1. Samer Abdallah
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  2. Nicolas Gold
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  1. Dept. of Arch., Design & Media Tech, Aalborg University, Aalborg, Denmark

    David Meredith

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Abdallah, S., Gold, N., Marsden, A. (2016). Analysing Symbolic Music with Probabilistic Grammars. In: Meredith, D. (eds) Computational Music Analysis. Springer, Cham. https://doi.org/10.1007/978-3-319-25931-4_7

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