SongGen: Framework for Controllable AI Song Generation through Interactive Songwriting and Artist Emulation
Name
arora-ajarora-meng-eecs-2024-thesis.pdf
Description
Thesis PDF
Size
4.08 MB
Format
Adobe PDF
Checksum (MD5)
58f284535e3b4698d26d0ec88c85d71f
Author(s)
Arora, Ajay
Advisor(s)
Egozy, Eran
Jaco, Wasalu
Date Issued
September 2024
Publisher
Massachusetts Institute of Technology
Abstract
We propose SongGen, an AI-based song-writing and song co-creation framework. Building upon existing AI tools like Suno.ai, SongGen features a chat interface with a trained AI songwriter assistant, emulating the traditional back-and-forth of human collaboration. The system offers enhanced capabilities for greater control over the songwriting process, including concept ideation, lyric generation and editing, real-time song generation, and granular instrumental specification. Comparative evaluations demonstrate SongGen’s superiority in key metrics such as steerability, expressiveness, personalization, and user satisfaction. We also present an extension of the SongGen framework for artist emulation and on-demand song generation. Future development aims to incorporate voice-based interaction and real-time voice conversion, enabling music artists to guide fans in creating personalized songs.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
In Copyright - Educational Use Permitted
Copyright retained by author(s)
Persistent DSpace Link
