Date of Award
6-9-2025
Document Type
Thesis
Publisher
Santa Clara : Santa Clara University, 2025
Department
Electrical and Computer Engineering
First Advisor
Hoeseok Yang
Abstract
This thesis explores the application of large language models (LLMs) in music analysis, creation, and interaction, focusing on their potential to reshape traditional workflows in the music domain. The study begins by contextualizing the role of artificial intelligence in music technology, particularly emphasizing the emergence of LLMs like GPT and their unique capabilities in multimodal and musical contexts. A comprehensive survey of current research and toolsets highlights both creative and analytical implementations, ranging from text-based music generation to music information retrieval. The core contribution is an experimental framework that integrates LLMs with music processing libraries, enabling novel interactions such as natural language queries over audio feature datasets, genre classification, and music recommendation using both symbolic and audio data. Emphasis is placed on evaluating the musicality and interpretability of model outputs, as well as assessing the usability of such systems for musicians and researchers. Through prototype development and case studies, the thesis illustrates how LLMs can bridge the gap between human musical intuition and computational understanding. It concludes by discussing the implications of LLM-based music systems for the future of music technology, including their ethical, cultural, and technical challenges.
Recommended Citation
Robinson, Curtis; Yang, Allan; and Liu, Thomas, "LLM Music Creation and Recommendation Applications" (2025). Electrical and Computer Engineering Senior Theses. 108.
https://scholarcommons.scu.edu/elec_senior/108
