This is the scene-setting first chapter in a recently published collection Computational Music Analysis, edited by David Meredith of the University of Aalborg and published by Springer. The book provides and in-depth overview of current research in computational music analysis, bringing together seventeen chapters by the leading researchers in the field. Alan Marsden is also joint author of another chapter, with Samer Abdallah and Nicolas Gold of University College London.
Alan Marsden, ‘Music analysis by computer: ontology and epistemology’. In David Meredith (ed.) Computational Music Analysis, Springer (2016) 3–28.
Abstract: This chapter examines questions of what is to be analysed in computational music analysis, what is to be produced, and how one can have confidence in the results. These are not new issues for music analysis, but their consequences are here considered explicitly from the perspective of computational analysis. Music analysis without computers is able to operate with multiple or even indistinct conceptions of the material to be analysed because it can use multiple references whose meanings shift from context to context. Computational analysis, by contrast, must operate with definite inputs and produce definite outputs. Computational analysts must therefore face the issues of error and approximation explicitly. While computational analysis must retain contact with music analysis as it is generally practised, I argue that the most promising approach for the development of computational analysis is not systems to mimic human analysis, but instead systems to answer specific music-analytical questions. The chapter concludes with several consequent recommendations for future directions in computational music analysis.
On-line chapter: http://link.springer.com/chapter/10.1007/978–3-319–25931-4_1
Full text(author-created final version): http://www.research.lancs.ac.uk/portal/services/downloadRegister/97906437/CMAMarsden.pdf
Samer Abdallah, Nicolas Gold and Alan Marsden, ‘Analysing symbolic music with probabilistic grammars’. In David Meredith (ed.) Computational Music Analysis, Springer (2016) 157–189.
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.
Online chapter: http://link.springer.com/chapter/10.1007/978–3-319–25931-4_7
Full text (author-created final version): http://www.research.lancs.ac.uk/portal/services/downloadRegister/97906522/CMAAbdallahEtAl.pdf