PAT 498/598: Music and AI (Winter 2025)
| Instructor | Hao-Wen Dong (ude.hcimu@gnodwh) |
| Room | Moore 376 (Davis) or Zoom |
| Days & times | 9–10:30am, Mondays & Wednesdays |
| Office hours | By appointment |
[Gradescope] [YouTube Playlist]
Description
An introduction to the emerging field of AI music. This course introduces students to AI’s applications in music from analysis, creation, retrieval to processing. Example topics include music transcription, optical music recognition, music source separation, automatic music composition, music synthesis, music recommendation and auto-mixing. Students will gain hands-on experience on using AI tools through open-ended assignments and a final project on a relevant topic of their choice. Prior coding experience is recommended.
Objectives
- Gain an overview of the field of AI music
- Learn fundamental AI concepts and principles
- Learn representative AI tools for music analysis, creation, retrieval and processing
- Gain hands-on experience on using AI music tools
Schedule
| Week | Date | Lecture | Recording | Assignment |
|---|---|---|---|---|
| 1 | Jan 8 | Introduction | ||
| Background | ||||
| 2 | Jan 13 | ├ What is AI? | ||
| Jan 15 | ├ AI & Music | HW 1 | ||
| 3 | Jan 20 | ├ |
│ | |
| Jan 22 | ├ Machine Learning Fundamentals | └ due on Jan 22 | ||
| 4 | Jan 27 | ├ Music & Audio Processing Fundamentals | ||
| Jan 29 | ├ Spectral Analysis | HW 2 | ||
| 5 | Feb 3 | ├ Deep Learning Fundamentals | │ | |
| Feb 5 | ├ Deep Learning Fundamentals II | └ due on Feb 7 | ||
| 6 | Feb 10 | └ Deep Learning Fundamentals III | HW 3 | |
| Analysis | │ | |||
| Feb 12 | ├ CNNs & HW 3 Walkthrough | │ | ||
| 7 | Feb 17 | ├ Music Classification | │ | |
| Feb 19 | ├ Source Separation | └ due on Feb 19 | ||
| 8 | Feb 24 | ├ Catch-up & HW 4 Walkthrough | HW 4 | |
| Feb 26 | └ Music Analysis | └ due on Feb 28 | ||
| 9 | Mar 3 | HW 5 | ||
| Mar 5 | │ | |||
| Creation | │ | |||
| 10 | Mar 10 | ├ Language-based Music Generation | │ | |
| Mar 12 | ├ Pianoroll-based Music Generation | └ due Mar 14 | ||
| 11 | Mar 17 | ├ Catch-up | ||
| Mar 19 | ├ Project pitch | |||
| 12 | Mar 24 | ├ Audio-domain Music Generation | ||
| Mar 26 | └ Latent-based Music Generation | HW 6 | ||
| Retrieval & Processing | │ | |||
| 13 | Mar 31 | ├ Music Search & Recommendation | │ | |
| Apr 2 | └ Music Production & Editing | │ | ||
| 14 | Apr 7 | Project Consultation | │ | |
| Apr 9 | Project Consultation | │ | ||
| 15 | Apr 14 | Discussion | └ due Apr 14 | |
| Apr 16 | Discussion | |||
| 16 | Apr 21 | Project presentation |
All slides are licensed under CC BY 4.0 
.
Assignments
- All assignments must be completed on your own. You are welcome to exchange ideas with your peers, but this should be in the form of concepts and discussion, not in the form of writing and code.
- 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.
- Late submissions will be deducted by 1 point per day. No late submission is allowed a week after the due date.
| Assignments | Content | Out | Due |
|---|---|---|---|
| HW 1 | Real or fake!? | Jan 15 | Jan 22 |
| HW 2 | Music & audio processing | Jan 31 | Feb 7 |
| HW 3 | Musical note classification | Feb 5 | Feb 19 |
| HW 4 | Source separation | Feb 21 | Feb 28 |
| HW 5 | AI Song Contest | Feb 28 | Mar 14 |
| HW 6 | Music Generation | Mar 28 | Apr 14 |
Project
- Instructions can be found here.
- 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.
| Due | |
|---|---|
| Group forming | Mar 12 |
| Pitch | Mar 19 |
| Presentation | Apr 21 |
| Report | Apr 28 |
Grading
All grading and regrade requests will be handled on Gradescope.
| Assignments | 50% | Project | 50% |
|---|---|---|---|
| ├ HW 1 | 5% | ├ Presentation | 20% |
| ├ HW 2 | 10% | ├ Results | 15% |
| ├ HW 3 | 15% | └ Report | 15% |
| ├ HW 4 | 5% | ||
| ├ HW 5 | 5% | ||
| └ HW 6 | 10% |
The final grading scale is as follows.
| A+ | >96 | B+ | 87–89 | C+ | 77–79 | D+ | 67–69 | F | <60 |
| A | 93–96 | B | 83–86 | C | 73–76 | D | 63–66 | ||
| A− | 90–92 | B− | 80–82 | C− | 70–72 | D− | 60–62 |
Computing Resources
- You will want to access a GPU machine via Google Colab or Great Lakes for faster training of deep learning models.
- Check out this 10-min YouTube tutorial to learn how to work with Google Colab. The free version is good enough for the assignments.
- Follow the instructions to learn how to access Great Lakes. You will be provided with ~560 GPU hours for assignments and project.
Optional Reading
- “Fundamentals of Music Processing“ by Meinard Müller (notebooks)
- “Intelligent Music Production“ by Brecht De Man, Ryan Stables, and Joshua D. Reiss
- Course material for “Deep Learning for Music Analysis and Generation“ by Yi-Hsuan Yang
- Course material for “Musical Applications of Machine Learning“ by Juhan Nam
- Course material for “Machine Learning for Music“ by Julian McAuley
Policies
Attendance
- On-time attendance to class is expected.
- Please contact me if you have special circumstances, including illness, family emergency and conference travel.
Course Recordings
- Course lectures may be recorded and made available to other students in this course.
- If you do not wish to be recorded, please contact me to discuss alternative arrangements.
Generative AI Usage
- Feel free to use GenAI tools (U-M GPT, ChatGPT, Gemini, Claude, GitHub Copilot, DALLE, Midjourney, Stable Diffusion, Firefly, etc.) in your workflow. However, you must disclose your usage of GenAI services in your write-ups.
- You take full responsibility for AI-generated materials as if you had produced them yourself: ideas should be attributed and facts should be true.
Plagiarism & Academic Misconduct
- Plagiarism and cheating violate SMTD’s Academic Code of Conduct. All plagiarism, cheating and other academic misconduct cases will be reported to SMTD’s Office of Academic and Student Affairs.
- All assignments must be completed on your own. You are welcome to exchange ideas with your peers, but this should be in the form of concepts and discussion, not in the form of writing and code.
- You must provide proper citations/references for any external resources you use in your writing and code.
Accommodations for Students with Disabilities/Disability Statement
The University of Michigan recognizes disability as an integral part of diversity and is committed to creating an inclusive and equitable educational environment for students with disabilities. Students who are experiencing a disability-related barrier should contact Services for Students with Disabilities ((734) 763-3000 or ssdoffice@umich.edu). For students who are connected with SSD, accommodation requests can be made in Accommodate. If you have any questions or concerns please contact your SSD Coordinator or visit SSD’s Current Student webpage. SSD considers aspects of the course design, course learning objects and the individual academic and course barriers experienced by the student. Further conversation with SSD, instructors, and the student may be warranted to ensure an accessible course experience.
Sexual Misconduct Policy
Title IX prohibits discrimination on the basis of sex, which includes sexual misconduct — including harassment, domestic and dating violence, sexual assault, and stalking. We understand that sexual violence can undermine students’ academic success and we encourage anyone dealing with sexual misconduct to talk to someone about their experience, so they can get the support they need. Confidential support and academic advocacy can be found with the Sexual Assault Prevention and Awareness Center (SAPAC) on their 24-hour crisis line at (734) 936-3333. Alleged violations can be non-confidentially reported to the Office for Institutional Equity (OIE).
Mental Health and Well-Being
Students may experience stressors that can impact both their academic experience and their personal well-being. These may include academic pressure and challenges associated with relationships, mental health, alcohol or other drugs, identities, finances, etc. If you are experiencing concerns, seeking help is a courageous thing to do for yourself and those who care about you. If the source of your stressors is academic, please contact me so that we can find solutions together. For personal concerns, U-M offers many resources, some of which are listed at Resources for Students on the Well-being Collective website. You can also search for additional resources on that website.