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Music Information Retrieval

University of Victoria via Kadenze Program


Ability to critically read, understand, and implement algorithms and systems described in research publications at the International Conference of the Society for Music Information Retrieval (ISMIR) and other peer-reviewed journals and conferences.

Understanding of the wide diversity of evaluation metrics and methodologies required to develop effective music information retrieval software systems.

Ability to integrate interdisciplinary knowledge in the process of developing a non-trivial potentially collaborative project.


Courses under this program:
Course 1: Extracting Information From Music Signals

The course introduces audio signal processing concepts motivated by examples from MIR research. More specifically students will learn about spectral analysis and time-frequency representations in general, monophonic pitch estimation, audio feature extraction, beat tracking, and tempo estimation.

Course 2: Machine Learning for Music Information Retrieval

An introduction to data mining through the lens of music information retrieval. Topics explored include classification (genre, mood, instrument), multi-label classification (tagging), and regression (emotion/mood).

Course 3: Music Retrieval Systems

Course 3 builds upon the digital signal processing concepts we have learned in Course 1 and the machine learning concepts we have learned in Course 2 to investigate a variety of interesting music information retrieval tasks. As these tasks become more advanced and complicated, the examples and assignments in this course shift from programming examples from scratch to utilizing existing libraries and frameworks. Topics explored include: music recommendation and query-by-humming, automatic chord…

Taught by

Keon Ju Lee, Jordie Shier, Jackie Aldern, George Tzanetakis, David Howe and Richard Sheehan


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