Starts in 7 days
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This exclusive course is part of the program:Music Information Retrieval
Starts in 7 days
You can also start immediately after joining!
This exclusive course is part of the program:
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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 adaptive mode and is open for enrollment. Learn more about adaptive courses here.
Session 1: Supervised Learning and Naive Bayes Classification (October 1, 2021)
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.
2. Introduction and Terminology
3. Probabilities, Models, Maximum Likelihood Estimation, Bayes Theorem
4. Independence, Conditional Probability, Conditional Independence and Bayes Theorem
5. Generative Models and Naive Bayes Classification (new)
6. Evaluation, Accuracy, Cross-Validation and Bootstrapping
Session 2: Discriminative Classifiers (October 8, 2021)
Decision trees, perceptron, artificial neural networks, support vector machines will be covered in this session.
Session 3: Genre Classification (October 15, 2021)
This session is about methods of tag acquisition (surveys, games with a purpose), auto-tagging architectures, evaluation of auto-tagging.
Session 4: Emotion Recognition and Regression (October 22, 2021)
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.
Session 5: Tags (October 29, 2021)
Session 6: Music Visualization (November 5, 2021)
2. Music Visualizers Based on Spectra
3. Principal Component Analysis and Timbregrams
4. Music Collection Visualization and Browsing
6. Self-Organizing Maps
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 And 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.
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