Open for Enrollment (In Development)
This exclusive course is part of the program:Music Information Retrieval
Would you like to enroll?
Enrollment for this course has closed. But you can enroll in a future offering (please select)
Enrollment has closed
Enrollment for this course is currently closed, but the next offering will be available shortly. Check back soon!
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.
Below you will find an overview of the Learning Outcomes you will achieve as you complete this course.
• Have a basic applied understanding of various supervised learning algorithms such as naive bayes, support vector machines, neural networks and decision trees.
• Understanding of how they can be applied to MIR tasks such as genre classification and instrument classification.
• Understanding of the different ways tags for describing music tracks can be obtained (surveys, games with a purpose, auto-tagging).
• Ability to formulate and understand auto-tagging as a machine learning problem and understand how it is evaluated.
Regression and Clustering
• Understanding of the basic concepts behind regression and clustering.
• Understanding of how these techniques can be applied for mood and emotion analysis of music signals.
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" 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.
Peer Assessment Code of Conduct: Part of what makes Kadenze a great place to learn is our community of students. While you are completing your Peer Assessments, we ask that you help us maintain the quality of our community. Please:
- Be Polite. Show your fellow students courtesy. No one wants to feel attacked - ever. For this reason, insults, condescension, or abuse will not be tolerated.
- Show Respect. Kadenze is a global community. Our students are from many different cultures and backgrounds. Please be patient, kind, and open-minded when discussing topics such as race, religion, gender, sexual orientation, or other potentially controversial subjects.
- Post Appropriate Content. We believe that expression is a human right and we would never censor our students. With that in mind, please be sensitive of what you post in a Peer Assessment. Only post content where and when it is appropriate to do so.
Please understand that posts which violate this Code of Conduct harm our community and may be deleted or made invisible to other students by course moderators. Students who repeatedly break these rules may be removed from the course and/or may lose access to Kadenze.
Students with Disabilities: Students who have documented disabilities and who want to request accommodations should refer to the student help article via the Kadenze support center. Kadenze is committed to making sure that our site is accessible to everyone. Configure your accessibility settings in your Kadenze Account Settings.