| 07 Jan 2009 | The course will be taught at BRAMS on the campus of Université de Montréal. See www.brams.org/contact.html for directions to BRAMS. |
| 01 Jan 2009 | Look here ("News") for urgent announcements and read Daily notes for course notes and all other information. The syllabus is available in pdf format. |
There is significant programming work in the homeworks and final project. My goal is that the projects deal with open problems in the field. In the Winter 2005 version of this course two projects were transformed into winning contest submissions in Genre Recognition and Note Onset Detection at an international programming contest (``MIREX'' at the ISMIR 2005 conference).
| Task | Amount |
| Homeworks | 40% |
| Weekly Readings | 10% |
| Reading Presentations | 10% |
| Final Project | 40% |
This is a lab and readings course. There will be no exams.
Homework: (40%) Regular homework assignments dealing with signal processing, music feature extraction and machine learning will be given.
Weekly Readings: (10%) Weekly journal papers, book chapters and tutorial material will be assigned. Careful reading of all material is required.
Reading Presentations: (10%) Each student will present a short summary of one weekly reading and will then lead discussion of the paper.
Final Project: (40%) At mid-term students will submit a 3 to 5 page project proposal for approval. The project must involve an application of machine learning in the domain of music. Students will be furnished with project suggestions and will be shown where to find relevant datasets. Students will present their projects to the class in a 20-30 minute presentation at the end of the term. Example topics: