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).
Overview
Syllabus
- Tags
- Emotion Recognition and 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.
- Discriminative Classifiers
- Decision trees, perceptron, artificial neural networks, support vector machines will be covered in this session.
- Genre Classification
- This session is about methods of tag acquisition (surveys, games with a purpose), auto-tagging architectures, evaluation of auto-tagging.
- Supervised Learning and 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.
- Music Visualization
Taught by
George Tzanetakis
