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title: README
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MAAP LAB 🎵 — Music AI & Audio Research

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

Focus areas:
🎼 Audio Generation · 🏷️ Music Tagging · 🗣️ Voice Conversion · 🧠 Transformers · 💨 Diffusion

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.
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🧰 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 ✅
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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] ✉️