Enrollment Closed
Open for Enrollment (In Development)
This exclusive course is part of the program:
Music Information Retrieval
Coming Soon
4 Sessions / 13 hours of work per session
Included w/ premium membership ($20/month)
Skill Level
Music, Music Information Retrieval, Audio Signal Processing, Data Mining
Open For Enrollment

Music Data Mining

Music Data Mining
Coming Soon

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4 Sessions / 13 hours of work per session
Included w/ premium membership ($20/month)
Skill Level
Music, Music Information Retrieval, Audio Signal Processing, Data Mining
Course Description

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).


This course is in scheduled mode. Learn more about scheduled courses here.

Session 1: Naive Bayes classification
In this session, we will learn about the main idea of generative classifiers using probabilistic modeling, Bayes theorem, the naive bayes assumption, evaluation of classification, cross-validation.
Session 2: Discriminating classifiers
Decision trees, perceptron, artificial neural networks, support vector machines will be covered in this session.
Session 3: Tagging
This session is about methods of tag acquisition (surveys, games with a purpose), auto-tagging architectures, evaluation of auto-tagging.
Session 4: Regression
We will learn about Regression and how it is applied in emotion/mood recognition, and other regression applications such as surrogate sensing for music instruments.
Learning Outcomes

Below you will find an overview of the Learning Outcomes you will achieve as you complete this course.

Instructors & Guests
What You Need to Take This Course

Prior Knowledge

  • Good knowledge of programming, basic linear algebra, probability, and statistics.


  • Computer with installation privileges.


  • The course is mostly software agnostic but existing frameworks for MIR and audio will be used. All software will be freely available and typically also open source. Examples include: Audacity, Marsyas, Sonic Visualizer, and VAMP plugins.


  • "Extracting Information from Music Signals" must be completed prior to taking this course.
Additional Information

PLEASE NOTE: Taking part in a Kadenze course as a Premium Member does not affirm that you have been enrolled or accepted for enrollment by University of Victoria.

In order to receive college credit for these program courses, you must successfully complete and pass all 3 courses in this program. If a student signs up for the Music Information Retrieval program, it is recommended that these courses are taken sequentially.

*Partial credit will not be awarded for completion of only one course.

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