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Evolving Musical Sight Reading Exercises Using Expert Models.

Charlotte Pierce¹, Tim Hendtlass², Anthony Bartel³

¹Melbourne School of Engineering, The University of Melbourne, Parkville, VI, Australia.

Frontiers in Artificial Intelligence

|March 18, 2021

Summary

This study introduces an evolutionary algorithm to generate unlimited musical sight-reading exercises. This approach ensures practice material remains effective and engaging for musicians, overcoming traditional limitations.

Keywords:

evolutionary algorithms expert models melody representation music education musical sight reading

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Area of Science:

Music Education
Computational Musicology
Artificial Intelligence

Background:

Sight-reading is essential for musicians but traditional exercises are quickly exhausted.
Limited availability of novel sight-reading material hinders continuous practice and skill development.

Purpose of the Study:

To present a novel evolutionary algorithm for generating musical sight-reading exercises.
To create technically appropriate and aesthetically pleasing practice material for musicians.
To overcome the resource constraints of traditional sight-reading exercises.

Main Methods:

Development of an evolutionary algorithm for musical exercise generation.
Utilizing models based on expert musical examples.
Generation of material tailored to specific instruments and difficulty levels.

Main Results:

The algorithm successfully generates novel sight-reading exercises.
Generated exercises are technically appropriate and aesthetically pleasing.
The system provides a scalable solution to the scarcity of practice material.

Conclusions:

The evolutionary algorithm offers a sustainable method for creating musical sight-reading exercises.
This approach enhances practice resources for musicians and educators.
Automated generation of musical exercises can support skill development in music education.