Date of Award
6-2-2025
Document Type
Thesis
Publisher
Santa Clara : Santa Clara University, 2025
Department
Computer Science and Engineering
First Advisor
Ahmed Amer
Abstract
This paper presents Notewise, a cutting-edge VST3 plugin designed to promote music theory learning and compositional analysis on digital audio workstations (DAWs). Notewise bridges the gap in music composition software by delivering contextual and intelligent feedback on musical compositions in real-time. A modular Musical Instrument Digital Interface (MIDI) analysis platform drives Notewise, o↵ering versatile tools to assist users by recognizing and highlighting errors in the harmonic and rhythmic structures of their compositions. The context-aware nature and flexibility of the system makes feedback accommodating to multiple genres and styles, with composers improving their work while retaining control over the creative process. Unlike current solutions, Notewise is incorporated into the production process as a seamless component, offering dynamic yet non-obtrusive feedback. In its current form, users can choose between a recurrent neural network (RNN) or a deterministic analyzer to examine their works, offering customization and precision. With its innovative method, Notewise democratizes music theory learning by avoiding social, economic, and political obstacles, making learning materials available to everyone around the world. This thesis will discuss Notewise’s design, its innovation in music theory learning, and its potential in the future to revolutionize music composition tools.
Recommended Citation
Nilsson, Darin and Samonte, Kyle Michael, "Notewise: An Interactive AI Tool for Music Theory Education and Composition Analysis" (2025). Computer Science and Engineering Senior Theses. 328.
https://scholarcommons.scu.edu/cseng_senior/328
