Not available for purchase in India
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Not available for purchase in India
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).
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.
• 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.
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.
Prior Knowledge
Equipment
Software
Prerequisite
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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