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Support material for Music Information Retrieval by George Tzanetakis
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Bayes Classification.ipynb
Clustering using Gaussian Mixture Models and PCA.ipynb
Clustering.ipynb
ConditionalProbabilities.ipynb
Dimensionality_reduction.ipynb
LICENSE.txt
Markov Chains and Hidden Markov Models.ipynb
README.md
RandomVariables.ipynb
_config.yml
csc475_comp_ethno.pdf
csc475_monophonic_pitch.pdf
csc475_rhythm.pdf
data_mining_conditional_probs.ipynb
data_mining_naive_bayes.ipynb
data_mining_random_variables.ipynb
ismir_2017_tutorial.pdf

README.md

Music Information Retrieval Support Materials by George Tzanetakis

This repository contains jupyter notebooks in Python used for teaching and tutorials for various topics in Machine Learning and Digital Signal Processing with a focus on applications in Music Information Retrieval. A set of associated slides from the tutorial "Bayes and Markov listen to music" presented at the International Conference of the Society for Music Information Retrieval (ISMIR) is also included. Even though the notebooks are mostly self-contained it makes more sense to explore them linearly using the following suggested order:

  1. Probability and Random Variables
  2. Conditional Probability
  3. Bayesian Classification
  4. Clustering using Gaussian Mixture Model and PCA
  5. Markov Chains and Hidden Markov Models

To run the notebooks you will need to install the following Python libraries:

  1. Numpy
  2. Scipy
  3. scikit-learn
  4. music21
  5. hmmlearn

In order for music21 to display scores you will need to have the MuseScore2 application installed. According to music21 documentation any MusicXML rendering software can work but I have not tested this.

Don't hesitate to send me any feedback you have or comments at gtzan@cs.uvic.ca

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