Show simple item record

dc.contributor.advisorEgozy, Eran
dc.contributor.advisorJaco, Wasalu
dc.contributor.authorArora, Ajay
dc.date.accessioned2024-10-09T18:31:09Z
dc.date.available2024-10-09T18:31:09Z
dc.date.issued2024-09
dc.date.submitted2024-10-07T14:34:28.000Z
dc.identifier.urihttps://hdl.handle.net/1721.1/157249
dc.description.abstractWe 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.
dc.publisherMassachusetts Institute of Technology
dc.rightsIn Copyright - Educational Use Permitted
dc.rightsCopyright retained by author(s)
dc.rights.urihttps://rightsstatements.org/page/InC-EDU/1.0/
dc.titleSongGen: Framework for Controllable AI Song Generation through Interactive Songwriting and Artist Emulation
dc.typeThesis
dc.description.degreeM.Eng.
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
mit.thesis.degreeMaster
thesis.degree.nameMaster of Engineering in Electrical Engineering and Computer Science


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record