| Instructor | Hao-Wen 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 open-ended assignments and a final project on a relevant topic of their choice. Prior coding experience is recommended.
| Week | Date | Lecture | Assignment | Project |
|---|---|---|---|---|
| 1 | Aug 25 | Introduction | ||
| Background | ||||
| Aug 27 | ├ What is AI? | Homework 1 | ||
| 2 | ├ |
└ due | ||
| Sep 3 | ├ AI & music | |||
| 3 | Sep 8 | ├ Machine learning fundamentals | ||
| Sep 10 | ├ Music & audio processing fundamentals | |||
| 4 | Sep 15 | ├ Spectral analysis | Homework 2 | |
| Sep 17 | ├ Deep learning fundamentals | │ | ||
| 5 | ├ |
│ | ||
| ├ |
└ due | |||
| 6 | Sep 29 | ├ Deep learning fundamentals II | Homework 3 | |
| Oct 1 | └ Deep learning fundamentals III | │ | ||
| Analysis | │ | |||
| 7 | Oct 6 | ├ CNNs & homework 3 walk through | │ | |
| Oct 8 | ├ Music classification | │ | ||
| 8 | ├ |
└ due | ||
| Oct 15 | ├ Source separation | Homework 4 | ||
| 9 | Oct 20 | ├ Catch-up & homework 4 walk through | │ | |
| Oct 22 | └ Music analysis | └ due | ||
| Creation | ||||
| 10 | Oct 27 | ├ Language-based music generation | Homework 5 | |
| Oct 29 | ├ Pianoroll-based music generation | └ due | Group forming | |
| 11 | Nov 3 | ├ Audio-domain music generation | ||
| Nov 5 | ├ Project pitch | Homework 6 | Pitch | |
| 12 | Nov 10 | ├ Catch-up & homework 6 walk through | │ | |
| Nov 12 | └ Latent-based music generation | │ | ||
| Retrieval & Processing | │ | |||
| 13 | Nov 17 | ├ Music search & recommendation | │ | |
| Nov 19 | └ Music production & editing | └ due | ||
| 14 | Nov 24 | Discussion | ||
| 15 | Dec 1 | Review | ||
| Dec 3 | Project presentation | Presentation | ||
| 16 | Report |
| Content | Out | Due on | |
|---|---|---|---|
| Homework 1 | Real or fake!? | TBD | TBD |
| Homework 2 | Music & audio processing | TBD | TBD |
| Homework 3 | Musical note classification | TBD | TBD |
| Homework 4 | Source separation | TBD | TBD |
| Homework 5 | AI Song Contest | TBD | TBD |
| Homework 6 | Music Generation | TBD | TBD |
| Due on (tentative) | |
|---|---|
| Group forming | Oct 29 |
| Pitch | Nov 5 |
| Presentation | Dec 3 |
| Final report | Dec 15 |
All grading and regrade requests will be handled on Gradescope.
| Homework | 55% | Project | 45% |
|---|---|---|---|
| ├ Homework 1 | 5% | ├ Presentation | 15% |
| ├ Homework 2 | 10% | ├ Results | 15% |
| ├ Homework 3 | 15% | └ Final report | 15% |
| ├ Homework 4 | 5% | ||
| ├ Homework 5 | 5% | ||
| └ Homework 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 |
There is no required reading. Here is some good optional reading:
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