music-embedding-retrieval is a focused learning project exploring how musical ideas can be represented, compared, and retrieved using machine learning concepts.
The project sits at the intersection of music, embeddings, and retrieval systems, and is designed as a practical way to understand how modern generative music systems reason about similarity, structure, and context.
This repository documents my learning process as I study and experiment with these ideas in a transparent, hands-on way.
Generative music systems don’t start with sound generation alone — they start with representation.
Before a model can generate music, it must be able to:
- Represent musical structure numerically
- Compare musical ideas
- Retrieve relevant patterns or phrases
- Understand similarity in a meaningful way
This project focuses on those foundations.
- Symbolic representations of music (e.g. pitch, rhythm, intervals)
- Feature extraction from simple musical data (MIDI or structured sequences)
- Tradeoffs between expressive richness and computational simplicity
- Converting musical features into vector representations
- Measuring similarity between musical phrases
- Understanding why certain musical ideas cluster together
- Exploring the limitations of different representations
- Building small retrieval pipelines over embedded musical data
- Querying musical ideas by similarity
- Evaluating results qualitatively and analytically
- Understanding how retrieval supports generative systems
This project prioritizes clarity and learning over scale.
- Small, inspectable datasets
- Simple models and representations
- Clear separation between data, embeddings, and retrieval logic
- Experiments designed to reveal why something works or fails
Where appropriate, I use AI tools as learning companions — to ask questions, test ideas, review assumptions, and iterate faster — while keeping the system design, reasoning, and decisions intentional and my own.
- Curiosity about ML fundamentals applied to music
- Comfort moving between music theory and technical representation
- System-level thinking around embeddings and retrieval
- An honest, disciplined learning approach using AI as a support tool, not a shortcut
This repository is meant to show how I think and learn, not just final outputs.
🚧 In active development
Planned next steps:
- Expand musical feature representations
- Compare different similarity metrics
- Visualize embedding spaces
- Connect retrieval outputs to simple generative experiments
I’m a software engineer with a background in systems, automation, and music.
This project reflects my ongoing transition toward applied machine learning and generative music systems, grounded in fundamentals and thoughtful experimentation.
This project is for educational and portfolio purposes.