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Kadenze

Music Data Mining

University of Victoria via Kadenze

This course may be unavailable.

Overview

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).

Syllabus

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.

Taught by

George Tzanetakis

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