SketchSong: Hierarchical Song Generation with Sketch Planning and Fine-Grained Multi-Track Modeling
arXiv:2606.03169v1 Announce Type: cross Abstract: Recent song generation systems can synthesize realistic audio, yet generating complete songs remains challenging for two reasons. First, explicit song-level arrangement planning remains limited in existing methods, so models often need to organize overall arrangement development while generating low-level audio details. This often leads to incoherence in arrangements, such as weak section transitions and limited dynamic progression. Second, coarse modeling of different musical parts obscures their distinct roles and interactions, limiting arran
AI research continues to push the boundaries of generative capabilities, with current advancements focusing on overcoming limitations in complex, multi-layered outputs like music.
Improved AI song generation could significantly impact the music industry, content creation, and entertainment sectors, enabling new forms of artistic expression and automation.
The ability to generate coherent, full-length songs with sophisticated arrangements by AI models improves, moving beyond mere audio synthesis to structured musical composition.
- · AI music startups
- · Independent musicians
- · Content creators
- · Entertainment industry
- · Traditional music composers (for certain tasks)
- · Entry-level sound designers
AI models gain enhanced capabilities in hierarchical planning and fine-grained multi-track modeling for complex creative outputs.
The cost and time associated with generating original musical scores for various applications significantly decrease.
This could lead to a democratization of music creation, potentially altering the talent landscape and intellectual property debates in the music industry.
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Read at arXiv cs.LG