Date of Award
3-2024
Document Type
Masters Thesis
Degree Name
M.A.
Organizational Unit
College of Arts Humanities and Social Sciences, Lamont School of Music
First Advisor
Mitchell Ohriner
Second Advisor
Kristin Taavola
Third Advisor
Sean Friar
Keywords
Artificial intelligence (AI), Electronic dance music (EDM), Hisaishi, Minimalism, Music, Transcription
Abstract
This thesis presents research in the domains of music theory and music technology. The topics of the three chapters are as follows: (1) the technical and artistic limitations of artificial intelligence when generating music, (2) the transcription of electronic dance music (EDM) using a hybrid notation system, and (3) the effective convergence of jazz and minimalism in the music of Joe Hisaishi from Studio Ghibli. Each topic investigates a different aspect of harmony and the modern ways in which humans communicate in terms of harmony. The first chapter surveys the latest research projects in AI-generated music and proposes ideas for future applications of music generation using machine learning. The second chapter presents a hybrid notation system for the transcription of electronic dance music. The final chapter identifies progressive composition techniques embodied in the works of Japanese composer Joe Hisaishi, who borrows musical influence from around the world.
Copyright Date
3-2024
Copyright Statement / License for Reuse
All Rights Reserved.
Publication Statement
Copyright is held by the author. User is responsible for all copyright compliance.
Rights Holder
Trevor Freed
Provenance
Received from ProQuest
File Format
application/pdf
Language
English (eng)
Extent
79 pgs
File Size
5.3 MB
Recommended Citation
Freed, Trevor, "Harmonic Data: AI Music, EDM Transcription, & Minimalist Jazz" (2024). Electronic Theses and Dissertations. 2368.
https://digitalcommons.du.edu/etd/2368
Discipline
Music theory, Music, Musical composition