Transforming Musical Notations for Universal Access

Brown, Silas S.; Robinson, Peter

doi:10.1007/978-0-85729-372-5_13

Cited by 1 publication

(1 citation statement)

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“…To the best of our knowledge, the present programme of work, alluded to in [21][22][23] and detailed here, is the first to apply ideas systematically from AOP to music representation and composition. An experiment in music notation and composition that appears to be very loosely inspired by AOP is mentioned in passing in [4].…”

Section: Introductionmentioning

confidence: 99%

Symmetric composition of musical concerns

Hill

Holland

Laney

2006

Proceedings of the 5th International Conference on Aspect-Oriented Software Development

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Aspect-oriented programming (AOP) describes a range of techniques that enable the separation, organisation and composition of various programming concerns that cannot be adequately encapsulated using the principal decomposition mechanisms available to modern programming languages.Naturally, most AOP-related research is focussed on its application to the development of computer software. However, we believe that it is worthwhile considering whether AOP and cognate techniques might be usefully adapted as a means for an end-user to organise, represent and compose information in computer systems that support application domains in which scattering and tangling are present.Music is notoriously rich in deeply tangled relationships. Moreover, there is no universally accepted representation of music that simultaneously represents all dimensions of interest to the composer.In this paper we describe Aspect Oriented Music Representation, an approach to the organisation, representation and composition of musical materials based on MDSOC. Our approach extends MDSOC by adding a dynamic hyperspace and by allowing users to write detailed composition expressions using an extensible set of compositors. We introduce the concept of composition history, enabling symmetric composition to be related to joinpoints, demonstrating a way to combine symmetric and asymmetric aspect approaches.

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Section: Introductionmentioning

confidence: 99%