Generative AI for Music and Audio
This work addresses the challenge of democratizing audio content creation for various media, but it appears incremental as it builds on existing generative AI trends without specifying breakthrough results.
The dissertation tackles the problem of using generative AI to create music and audio content, aiming to lower barriers for professionals and amateurs, with research focused on multitrack music generation, assistive tools, and multimodal learning.
Generative AI has been transforming the way we interact with technology and consume content. In the next decade, AI technology will reshape how we create audio content in various media, including music, theater, films, games, podcasts, and short videos. In this dissertation, I introduce the three main directions of my research centered around generative AI for music and audio: 1) multitrack music generation, 2) assistive music creation tools, and 3) multimodal learning for audio and music. Through my research, I aim to answer the following two fundamental questions: 1) How can AI help professionals or amateurs create music and audio content? 2) Can AI learn to create music in a way similar to how humans learn music? My long-term goal is to lower the barrier of entry for music composition and democratize audio content creation