Evolving Musical Sight Reading Exercises Using Expert Models

Sight reading skills are widely considered to be crucial for all musicians. However, given that sight reading involves playing sheet music without having seen it before, once an exercise has been completed by a student it can no longer be used as a sight reading exercise for them. In this paper we p...

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Main Authors: Charlotte Pierce, Tim Hendtlass, Anthony Bartel, Clinton J. Woodward
Format: Article
Language:English
Published: Frontiers Media S.A. 2021-01-01
Series:Frontiers in Artificial Intelligence
Subjects:
Online Access:https://www.frontiersin.org/articles/10.3389/frai.2020.497530/full
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author Charlotte Pierce
Tim Hendtlass
Anthony Bartel
Clinton J. Woodward
author_facet Charlotte Pierce
Tim Hendtlass
Anthony Bartel
Clinton J. Woodward
author_sort Charlotte Pierce
collection DOAJ
description Sight reading skills are widely considered to be crucial for all musicians. However, given that sight reading involves playing sheet music without having seen it before, once an exercise has been completed by a student it can no longer be used as a sight reading exercise for them. In this paper we present a novel evolutionary algorithm for generating musical sight reading exercises in the Western art music tradition. Using models based on expert examples, the algorithm generates material suitable for practice which is both technically appropriate and aesthetically pleasing with respect to an instrument and difficulty level. This overcomes the resource constraint in using traditional practice exercises, which are exhausted quickly by students and teachers due to their limited quantity.
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spelling doaj.art-73618a12719b4e349c51f729deea40a42022-12-21T20:32:01ZengFrontiers Media S.A.Frontiers in Artificial Intelligence2624-82122021-01-01310.3389/frai.2020.497530497530Evolving Musical Sight Reading Exercises Using Expert ModelsCharlotte Pierce0Tim Hendtlass1Anthony Bartel2Clinton J. Woodward3Melbourne School of Engineering, The University of Melbourne, Parkville, VI, AustraliaFaculty of Science, Engineering and Technology, Swinburne University of Technology, Hawthorn, VI, AustraliaFaculty of Health, Arts and Design, Swinburne University of Technology, Hawthorn, VI, AustraliaFaculty of Science, Engineering and Technology, Swinburne University of Technology, Hawthorn, VI, AustraliaSight reading skills are widely considered to be crucial for all musicians. However, given that sight reading involves playing sheet music without having seen it before, once an exercise has been completed by a student it can no longer be used as a sight reading exercise for them. In this paper we present a novel evolutionary algorithm for generating musical sight reading exercises in the Western art music tradition. Using models based on expert examples, the algorithm generates material suitable for practice which is both technically appropriate and aesthetically pleasing with respect to an instrument and difficulty level. This overcomes the resource constraint in using traditional practice exercises, which are exhausted quickly by students and teachers due to their limited quantity.https://www.frontiersin.org/articles/10.3389/frai.2020.497530/fullexpert modelsmusical sight readingmelody representationmusic educationevolutionary algorithms
spellingShingle Charlotte Pierce
Tim Hendtlass
Anthony Bartel
Clinton J. Woodward
Evolving Musical Sight Reading Exercises Using Expert Models
Frontiers in Artificial Intelligence
expert models
musical sight reading
melody representation
music education
evolutionary algorithms
title Evolving Musical Sight Reading Exercises Using Expert Models
title_full Evolving Musical Sight Reading Exercises Using Expert Models
title_fullStr Evolving Musical Sight Reading Exercises Using Expert Models
title_full_unstemmed Evolving Musical Sight Reading Exercises Using Expert Models
title_short Evolving Musical Sight Reading Exercises Using Expert Models
title_sort evolving musical sight reading exercises using expert models
topic expert models
musical sight reading
melody representation
music education
evolutionary algorithms
url https://www.frontiersin.org/articles/10.3389/frai.2020.497530/full
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