Enrollment Closed
Open for Enrollment (In Development)
This exclusive course is part of the program:
Music Information Retrieval
Coming Soon
5 Sessions / 10 hours of work per session
Included w/ premium membership ($20/month)
Skill Level
Music, Music Information Retrieval, Audio Signal Processing, MIR, Algorithms
Open For Enrollment

Music Retrieval Systems

Music Retrieval Systems
Coming Soon

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5 Sessions / 10 hours of work per session
Included w/ premium membership ($20/month)
Skill Level
Music, Music Information Retrieval, Audio Signal Processing, MIR, Algorithms
Course Description

Based on the concepts and algorithms explored in the previous 2 courses, "Extracting Information from Music Signal" and "Music Data Mining", we show how more complete and complex music retrieval systems, tasks, and algorithms can be developed. More specifically we will look at how four complete music retrieval systems are put together: audio fingerprinting, query-by-humming, chord-detection, polyphonic music-score alignment, and a visual music browser based on self-organizing maps.


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

Session 1: Audio fingerprinting
In this session, we will cover the basics of audio fingerprinting and watermarking: audio landmark extraction, quantization, jaccard similarity, minhash, locality sensitive hashing.
Session 2: Chord Detection
This session will describe the problem of chord detection, a quick introduction to music theory and notation, hidden markov models and other types of probabilistic modeling for chord detection and structure segmentation.
Session 3: Query-by-Humming
We will learn about the basic architecture of a query-by-humming system, theme extraction, note segmentation and quantization.
Session 4: Polyphonic audio-score alignment
In this session, we will cover MIDI, symbolic music representations, dynamic programming, self-similarity matrices, polyphonic audio-score alignment.
Session 5: Music Visualization
Principal component analysis, self-organizing maps, visualization in MIR will be covered in this session.
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" and "Music Data Mining" 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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