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Analysing symbolic music with probabilistic grammars

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Analysing symbolic music with probabilistic grammars. / Abdallah, Samer; Gold, Nicolas; Marsden, Alan.

Computational music analysis. ed. / David Meredith. Springer, 2016. p. 157-189.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNChapter (peer-reviewed)

Harvard

Abdallah, S, Gold, N & Marsden, A 2016, Analysing symbolic music with probabilistic grammars. in D Meredith (ed.), Computational music analysis. Springer, pp. 157-189. https://doi.org/10.1007/978-3-319-25931-4_7

APA

Abdallah, S., Gold, N., & Marsden, A. (2016). Analysing symbolic music with probabilistic grammars. In D. Meredith (Ed.), Computational music analysis (pp. 157-189). Springer. https://doi.org/10.1007/978-3-319-25931-4_7

Vancouver

Abdallah S, Gold N, Marsden A. Analysing symbolic music with probabilistic grammars. In Meredith D, editor, Computational music analysis. Springer. 2016. p. 157-189 https://doi.org/10.1007/978-3-319-25931-4_7

Author

Abdallah, Samer ; Gold, Nicolas ; Marsden, Alan. / Analysing symbolic music with probabilistic grammars. Computational music analysis. editor / David Meredith. Springer, 2016. pp. 157-189

Bibtex

@inbook{19502f506e044579948d2dffbfc1ebbc,
title = "Analysing symbolic music with probabilistic grammars",
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.",
author = "Samer Abdallah and Nicolas Gold and Alan Marsden",
year = "2016",
month = jan,
day = "1",
doi = "10.1007/978-3-319-25931-4_7",
language = "English",
isbn = "9783319259291",
pages = "157--189",
editor = "David Meredith",
booktitle = "Computational music analysis",
publisher = "Springer",

}

RIS

TY - CHAP

T1 - Analysing symbolic music with probabilistic grammars

AU - Abdallah, Samer

AU - Gold, Nicolas

AU - Marsden, Alan

PY - 2016/1/1

Y1 - 2016/1/1

N2 - 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.

AB - 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.

U2 - 10.1007/978-3-319-25931-4_7

DO - 10.1007/978-3-319-25931-4_7

M3 - Chapter (peer-reviewed)

SN - 9783319259291

SP - 157

EP - 189

BT - Computational music analysis

A2 - Meredith, David

PB - Springer

ER -