Date of Award
Daniel Felix Ritchie School of Engineering and Computer Science, Computer Science
Mario A. Lopez, Ph.D.
Ricardo Iznaola, Ph.D.
Ramakrishna Thurimella, Ph.D.
Algorithmic, Generation, Guitar, Music, Sight reading
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.
Copyright is held by the author. User is responsible for all copyright compliance.
Ryan Stephen Davis
Received from ProQuest
Davis, Ryan Stephen, "Algorithmic Music Generation for Pedagogy of Sight Reading" (2018). Electronic Theses and Dissertations. 1536.
Computer science, Music