| Instructor | Hao-Wen Dong (ude.hcimu@gnodwh) |
| Room | Moore 376 (Davis) |
| Days & times | 12:30–2pm, Mondays & Wednesdays |
| Office hours | By appointment |
[Gradescope] [Canvas]
An introduction to generative AI and its applications to music and audio creation. Topics include music generation, audio synthesis and assistive music creation tools. Students work on a semester-long group project to gain hands-on experience on creating music using AI tools. Prior coding experience is recommended.
| Week | Date | Lecture | Assignments | Project |
|---|---|---|---|---|
| 1 | Aug 26 | Introduction | ||
| Background | ||||
| Aug 28 | ├ Intro to AI Music | |||
| 2 | ├ |
|||
| Sep 4 | ├ Intro to AI Music II | |||
| 3 | Sep 9 | ├ Deep Learning Fundamentals I | ||
| Sep 11 | ├ Deep Learning Fundamentals II | Assignment 1 | ||
| 4 | Sep 16 | ├ Guest lecture by Prof. Bryan Pardo | └ due on Sep 20 | |
| Sep 18 | ├ Optimization | |||
| 5 | Sep 23 | ├ CNNs | Assignment 2 | |
| Sep 25 | └ Assignment 2 Walk Through | │ | ||
| Deep Generative Models | │ | |||
| 6 | Sep 30 | ├ RNNs, LSTMs & Transformers | │ | |
| Oct 2 | ├ VAEs & GANs | │ | ||
| 7 | Oct 7 | ├ Diffusion Models | │ | |
| Oct 9 | └ Midterm Review | └ due on Oct 11 | ||
| 8 | ||||
| Symbolic Music Generation | ||||
| Oct 16 | ├ Language-based Music Generation | |||
| 9 | Oct 21 | ├ Piano Roll-based Music Generation | ||
| Oct 23 | └ Controllable Music Generation | |||
| Audio Synthesis | ||||
| 10 | Oct 28 | ├ Time-domain Audio Synthesis | ||
| Oct 30 | ├ Assignment 3 Walk Through & Catch-up | Assignment 3 | ||
| 11 | Nov 4 | ├ Frequency-domain Audio Synthesis | │ | |
| Nov 6 | └ Latent-based Audio Synthesis | │ | Pitch | |
| 12 | │ | |||
| │ | ||||
| 13 | Nov 18 | Catch-up & Guest talk by Jiwoo Ryu | │ | Proposal |
| Assistive Music Creation Tools | │ | |||
| Nov 20 | ├ Neural Audio Effects & Automatic Mixing | └ due on Nov 22 | ||
| 14 | Nov 25 | ├ Live Performance & Interactive Systems | ||
| 15 | Dec 2 | └ Multimodal Music Generation | ||
| Dec 4 | Review & Discussions | |||
| 16 | Dec 9 | Project Presentation | Presentation & final report |
All grading will be handled via Gradescope, including assignment submission and regrade requests.
| Assignments | 40% | Project | 60% |
|---|---|---|---|
| ├ Assignment 1 | 10% | ├ Proposal | 10% |
| ├ Assignment 2 | 10% | ├ Final report | 25% |
| └ Assignment 3 | 20% | └ Presentation | 25% |
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 |
| Content | Out | Due on | |
|---|---|---|---|
| Assignment 1 | AI song contest | Sep 11 | Sep 20 |
| Assignment 2 | Musical note classification using CNNs | Sep 23 | Oct 11 |
| Assignment 3 | Unconditional music/audio generation | Oct 31 | Nov 22 |
| Due on | |
|---|---|
| Pitch | Nov 6 |
| Proposal | Nov 18 |
| Presentation | Dec 9 |
| Final report | Dec 15 |
There is no required reading. Here is some good optional reading:
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