| Instructor | Hao-Wen (Herman) Dong (ude.hcimu@gnodwh) |
| Room | Moore 376 (Davis) or Zoom |
| Days & times | 1:30–3pm, Mondays & Wednesdays |
| Office hours | By appointment |
[Gradescope] [Last year’s course website]
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 assignments and an open-ended project on a relevant topic of their choice. Prior coding experience is recommended.
This course counts towards the PAT Minor (Upper Level Elective), CS-Eng/DS-Eng (Flex Tech Elective), and MIDAS GDSC (Electives) programs.
| Week | Date | Lecture | Recording | Assignment |
|---|---|---|---|---|
| 1 | Aug 31 | Introduction | ||
| Background | ||||
| Sep 2 | ├ AI & ML Fundamentals | |||
| 2 | ├ |
|||
| Sep 9 | ├ AI & Music | |||
| 3 | Sep 14 | ├ Music Processing Fundamentals & PA 1 | HW 1 due | |
| Sep 16 | ├ Audio Processing Fundamentals | |||
| 4 | Sep 21 | ├ Audio Processing Fundamentals II & PA 2 | ||
| Sep 23 | ├ Machine Learning Fundamentals | PA 1 due | ||
| 5 | Sep 28 | ├ Deep Learning Fundamentals I | ||
| Sep 30 | └ Deep Learning Fundamentals II | PA 2 due | ||
| Analysis | ||||
| 6 | Oct 5 | ├ Source Separation | ||
| Oct 7 | ├ Convolutional Neural Networks & PA 3 | HW 2 due | ||
| 7 | Oct 12 | ├ Music Analysis | ||
| Oct 14 | └ Music Classification & PA 4 | PA 3 due | ||
| 8 | ||||
| Creation | ||||
| Oct 21 | ├ Language-based Music Generation | HW 3 due | ||
| 9 | Oct 26 | ├ Piano Roll-based Music Generation | ||
| Oct 28 | ├ Audio-domain Music Generation | PA 4 due | ||
| 10 | Nov 2 | └ Latent-based Music Generation | ||
| Nov 4 | Project Pitch | HW 4 due | ||
| Retrieval & Processing | ||||
| 11 | Nov 9 | ├ Music Production & Editing | ||
| Nov 11 | └ Music Search & Recommendation | PA 5 due | ||
| 12 | Nov 16 | Buffer | ||
| Nov 18 | Discussions | |||
| 13 | Nov 23 | Challenges & Opportunities | ||
| 14 | Nov 30 | Project Consultation | ||
| Dec 2 | Project Presentation | |||
| 15 | ||||
All slides are licensed under CC BY 4.0.
| Homework | Due |
|---|---|
| HW 1: Real or Fake!? | Sep 14 |
| HW 2: Source Separation | TBD |
| HW 3: AI Song Contest 2026 | TBD |
| HW 4: AI Music Tools | TBD |
| Programming Assignment | Due |
|---|---|
| PA 1: Symbolic Music Processing | TBD |
| PA 2: Spectral Analysis | TBD |
| PA 3: Source Separation | TBD |
| PA 4: Musical Note Classification | TBD |
| PA 5: Automatic Music Instrumentation | TBD |
| Milestone | Due |
|---|---|
| Pitch | Nov 4 |
| Presentation | Dec 2 |
| Report | Dec 11 |
All grading and regrade requests will be handled on Gradescope.
| Homework | 20% |
| Programming assignments | 50% |
| Project | 30% |
The final grading scale is as follows.
| A+ | 97+ | 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 |
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