Project
PAT 498/598: Music and AI (Winter 2025)
Project Pitch in Class on Mar 19
Presentation in Class on Apr 21
Report Due at 11:59pm ET on Apr 28
Instructions
- This is a group project. A group size of 2-3 is expected.
- Please provide proper citations/references for any external resources you use in your writing and code.
- Please submit your work to Gradescope.
- All assignments are due at 11:59pm ET on the date specified.
- No late submissions! Submit your work early and update it later.
Topic
This is an open-ended group project. You may work on any of the following topics:
- Building a new AI music tool
- Exploring creative & artistic use of AI music tools
- Analyzing systematically existing AI music tools
Project Pitch
Please give a 10-min presentation:
- Team member introduction
- Topic: What do you want to work on?
- Topic: Who is the target audience/user/customer/reader?
- Methodology: How are you going to approach it?
- Methodology: What are the tools (programming languages, platforms, plugins, hardware, questionnaires, etc.) that you’ll be using?
- Expected results: What are the expected deliverables (e.g., an instrument, a plugin, a web/mobile app, a standalone software, an installation, a performance, a composition, an analysis)?
- Planning: What are the milestones? What do you expect to achieve by the end of February and March?
Presentation
Please give a 10-min presentation that summarizes your motivations, methods, results, analysis and discussions. You may follow any structure that best suits your narrative.
Report
Please turn in a 2 to 3-page (excluding references) report that summarizes your motivations, methods, results, analysis and discussions. You may use any template for your report.
Rubrics
- Presentation (15pt)
- Attendance (5pt)
- Clarity (5pt)
- Organization and presentation (5pt)
- Results (15pt)
- System/experiment design (5pt)
- Implementation (5pt)
- Experimental/analytic results (5pt)
- Report (15pt)
- Writing clarity (5pt)
- Organization and presentation (5pt)
- Discussion (5pt)
Suggestions & Tips
- An active GitHub repository with many open/closed issues is usually a good sign.
- Always look for backup codebase so that you have a plan B. Any online repository is not guaranteed to work.
- If you plan to train or finetune something, think about the “data & model” at the same time. You will need the right dataset and the right model to succeed.