MAAP LAB 🎵 — Music AI & Audio Research

“Why are there so few labs in Korea dedicated to Music AI? We built one.”

Mission

Advance the foundations of Music AI through practical research in tagging, generation, and dataset-centric methods — then share our results openly with the community. ✨

Open Science

We aim to publish at top venues (e.g., ICASSP, ISMIR, AAAI) and release code, models, and datasets whenever possible. 📢

Latest News 🗞️

• ✅ First project achieved! Submitted 2 papers to a NeurIPS Workshop based on our Music Tagging pipeline and dataset work.
  _{Links to be added 🔗}
• 🧰 GPU resources via university support: NVIDIA A100, A6000, RTX 4090 ⚙️

Our Activities 🎯

Project 1 — Music Tagging (Completed) 🏷️

• Built a tagging & augmentation pipeline with CLAP, Beam Search, Stable Audio
• Focus: dataset augmentation/creation for future work
• Targets: short-term word generation → long-term sentence generation with LLMs
• Outcome: 2 NeurIPS Workshop submissions ✅
  _{Links: to be added 🔗}

Project 2 — Efficient Music Generation (In Progress) 🎶

• Exploring Diffusion & DiT (e.g., Flux)
• LoRA/Adapters to avoid full fine-tuning
• Goal: robust generation for data-scarce genres/instruments/domains
• Roadmap: dataset curation → baseline reproduction → adapter experiments → ablations → release

Publications & Submissions 📚

• NeurIPS Workshop Submission #1 — (TBD) ✍️
• NeurIPS Workshop Submission #2 — (TBD) ✍️

Get Involved 🤝

Interested in collaborating on Music AI? We welcome discussions on datasets, evaluation, and model design.
Contact: [email protected] ✉️

_{© 2025 MAAP LAB • Built with ❤️ for music & AI.}