| Instructor | Hao-Wen Dong (ude.hcimu@gnodwh) |
| Room | Moore 376 (Davis) or Zoom |
| Days & times | 9–10:30am, Mondays & Wednesdays |
| Office hours | By appointment |
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.
| Week | Date | Lecture | Assignment (tentative) | Project |
|---|---|---|---|---|
| 1 | Jan 8 | Introduction | ||
| Background | ||||
| 2 | Jan 13 | ├ What is AI? (recording) | ||
| Jan 15 | ├ AI & music (recording) | Homework 1 | ||
| 3 | Jan 20 | ├ |
└ due | |
| Jan 22 | ├ Machine learning fundamentals | Homework 2 | ||
| 4 | Jan 27 | └ Music and audio processing fundamentals | └ due | |
| Analysis | ||||
| Jan 29 | ├ Classification & source separation | Homework 3 | ||
| 5 | Feb 3 | ├ Transcription & optical music recognition | └ due | |
| Feb 5 | └ Beat-tracking & structural analysis | Homework 4 | ||
| 6 | Feb 10 | How to read and present a research paper? | └ due | |
| Creation | ||||
| Feb 12 | ├ Music composition & arrangement | Homework 5 | ||
| 7 | Feb 17 | ├ Music synthesis | └ due | |
| Feb 19 | └ Live improvisation | Homework 6 | ||
| 8 | Feb 24 | Paper presentation | │ | |
| Feb 26 | Paper presentation | │ | ||
| 9 | Mar 3 | │ | ||
| Mar 5 | │ | |||
| Retrieval & Processing | │ | |||
| 10 | Mar 10 | ├ Music search & recommendation | └ due | |
| Mar 12 | ├ Music enhancement | Homework 7 | ||
| 11 | Mar 17 | ├ Music production | └ due | |
| Mar 19 | └ Music editing | |||
| 12 | Mar 24 | Project pitch | Pitch | |
| Advanced Topics | ||||
| Mar 26 | ├ Interactive tools | Homework 8 | ||
| 13 | Mar 31 | ├ Singing voice synthesis | └ due | |
| Apr 2 | └ Audiovisual tools | |||
| 14 | Apr 7 | |||
| Apr 9 | ||||
| 15 | Apr 14 | Review | ||
| Apr 16 | Project presentation | Presentation | ||
| 16 | Apr 21 | Project presentation | Final report |
| Content (tentative) | Out | Due on | |
|---|---|---|---|
| Homework 1 | Real or fake!? | Jan 15 | Jan 22 |
| Homework 2 | Music & audio processing | TBD | TBD |
| Homework 3 | Sound separation | TBD | TBD |
| Homework 4 | AI song contest 2024 | TBD | TBD |
| Homework 5 | AI music creation tool | TBD | TBD |
| Homework 6 | In-context learning | TBD | TBD |
| Homework 7 | Music recommendation | TBD | TBD |
| Homework 8 | Piano Genie | TBD | TBD |
| Due on (tentative) | |
|---|---|
| Pitch | Mar 24 |
| Proposal | Mar 31 |
| Presentation | Apr 16 & 21 |
| Final report | Apr 28 |
All grading and regrade requests will be handled on Gradescope.
| Homework | 40% | Paper presentation | 15% |
|---|---|---|---|
| ├ Homework 1 | 5% | ||
| ├ Homework 2 | 5% | Project | 45% |
| ├ Homework 3 | 5% | ├ Presentation | 15% |
| ├ Homework 4 | 5% | ├ Results | 15% |
| ├ Homework 5 | 5% | └ Final report | 15% |
| ├ Homework 6 | 5% | ||
| ├ Homework 7 | 5% | ||
| └ Homework 8 | 5% |
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 |
There is no required reading. Here is some good optional reading:
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