Date of Award

1-1-2018

Document Type

Masters Thesis

Degree Name

M.S.

Organizational Unit

Daniel Felix Ritchie School of Engineering and Computer Science, Computer Science

First Advisor

Mario A. Lopez, Ph.D.

Second Advisor

Ricardo Iznaola, Ph.D.

Third Advisor

Ramakrishna Thurimella, Ph.D.

Keywords

Algorithmic, Generation, Guitar, Music, Sight reading

Abstract

Autodeus is the name of the program that has been developed and was designed to aid guitar students in the attainment and betterment of musical notation sight reading skills. Its primary goal is to provide a very flexible tool that has the ability to generate virtually endless types of sight reading exercises at many various skill levels.

A complimentary 2 year-long comprehensive guitar sight-reading course syllabus can be implemented via Autodeus as it is capable of generating all the necessary exercises. It is able to generate these exercises quickly and efficiently through the use of a back tracking algorithm that utilizes modular rules. In addition to these modular rules, it allows users many input options: 9 different generation methods, the option of which strings and frets will be utilized, the ability to input types of musical intervals and their probabilities, the ability to input types of note durations and their probabilities, and the ability to input the probability of rests, among others. It also has the ability to generate two voice species counterpoint that abides by classical music rules.

A subset of Autodeus' capabilities are now available on a public URL for free. The genesis of this type of online application is highly beneficial to any guitarist looking to expand and improve their sight-reading capabilities as there is currently no system available that provides all of the developed features. It is highly encouraged by any reader to go to this website and experiment with the various available inputs and generation types.

Publication Statement

Copyright is held by the author. User is responsible for all copyright compliance.

Rights Holder

Ryan Stephen Davis

Provenance

Received from ProQuest

File Format

application/pdf

Language

en

File Size

88 p.

Discipline

Computer science, Music

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