Open for Enrollment (In Development)
This exclusive course is part of the program:Music Information Retrieval
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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.
Below you will find an overview of the Learning Outcomes you will achieve as you complete this course.
• Ability to use a variety of framework and tools for MIR, such as include Marsyas, librosa, mirex_eval, VAMP plugins, Sonic Visualizer, and Audacity.
Developing User Interfaces
• Ability to demonstrate a basic applied understanding of how to create interactive visual interfaces for music information retrieval applications.
• Understanding of hidden markov models and other probabilistic models through MIR lens.
Instructors & Guests
George Tzanetakis is a Professor in the Department of Computer Science with cross-listed appointments in ECE and Music at the University of Victoria, Canada. He is the Canada Research Chair (Tier II) in the Computer Analysis of Audio and Music and received the Craigdarroch research award in artistic expression at the University of Victoria in 2012. In 2011 he was Visiting Faculty at Google Research. He received his PhD in Computer Science at Princeton University in 2002 and was a Post-Doctoral fellow at Carnegie Mellon University in 2002-2003. His research spans all stages of audio content analysis such as feature extraction, segmentation, classification with specific emphasis on music information retrieval. He is also the primary designer and developer of Marsyas an open source framework for audio processing with specific emphasis on music information retrieval applications. His pioneering work on musical genre classification received a IEEE signal processing society young author award and is frequently cited. More recently he has been exploring new interfaces for musical expression, music robotics, computational ethnomusicology, and computer-assisted music instrument tutoring.
What You Need to Take This Course
- 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.
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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