| 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 | Project |
|---|---|---|---|---|
| 1 | Jan 8 | Introduction | ||
| Background | ||||
| 2 | Jan 13 | ├ What is AI? [recording] | ||
| Jan 15 | ├ AI & music [recording] | Homework 1 | ||
| 3 | Jan 20 | ├ |
│ | |
| Jan 22 | ├ Machine learning fundamentals [recording] | └ due on Jan 22 | ||
| 4 | Jan 27 | ├ Music & audio processing fundamentals [recording] | ||
| Jan 29 | ├ Spectral analysis [recording] | Homework 2 | ||
| 5 | Feb 3 | ├ Deep learning fundamentals [recording] | │ | |
| Feb 5 | ├ Deep learning fundamentals II [recording] | └ due on Feb 7 | ||
| 6 | Feb 10 | └ Deep learning fundamentals III [recording] | Homework 3 | |
| Analysis | │ | |||
| Feb 12 | ├ CNNs & homework 3 walk through [recording] | │ | ||
| 7 | Feb 17 | ├ Music classification [recording] | │ | |
| Feb 19 | ├ Source separation [recording] | └ due on Feb 19 | ||
| 8 | Feb 24 | ├ Catch-up & homework 4 walk through [recording] | Homework 4 | |
| Feb 26 | └ Music analysis [recording] | └ due on Feb 28 | ||
| 9 | Mar 3 | Homework 5 | ||
| Mar 5 | │ | |||
| Creation | │ | |||
| 10 | Mar 10 | ├ Language-based music generation [recording] | │ | |
| Mar 12 | ├ Pianoroll-based music generation [recording] | └ due Mar 14 | Group forming | |
| 11 | Mar 17 | ├ Catch-up [recording] | ||
| Mar 19 | ├ Project pitch | Pitch | ||
| 12 | Mar 24 | ├ Audio-domain music generation [recording] | ||
| Mar 26 | └ Latent-based music generation [recording] | Homework 6 | ||
| Retrieval & Processing | │ | |||
| 13 | Mar 31 | ├ Music search & recommendation [recording] | │ | |
| Apr 2 | └ Music production & editing [recording] | │ | ||
| 14 | Apr 7 | Project consultation | │ | |
| Apr 9 | Project consultation | └ due Apr 11 | ||
| 15 | Apr 14 | Review & discussion | ||
| Apr 16 | Project presentation | Presentation | ||
| 16 | Apr 21 | Project presentation | Final report |
| Content | Out | Due on | |
|---|---|---|---|
| Homework 1 | Real or fake!? | Jan 15 | Jan 22 |
| Homework 2 | Music & audio processing | Jan 31 | Feb 7 |
| Homework 3 | Musical note classification | Feb 5 | Feb 19 |
| Homework 4 | Source separation | Feb 21 | Feb 28 |
| Homework 5 | AI Song Contest | Feb 28 | Mar 14 |
| Homework 6 | Music Generation | Mar 28 | Apr 11 |
| Due on | |
|---|---|
| Pitch | Mar 19 |
| Proposal | Mar 31 |
| Presentation | Apr 16 & 21 |
| Final report | Apr 28 |
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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