Seth's AI MSc Dissertation

Published September 2000.


People are able to make stylistic distinctions between samples of music quickly and easily. Reliably duplicating this ability with computers has proven to be difficult, but a simple system with modest accuracy can still be useful for some music organization applications.

I have created software to extract certain features from recorded music, and trained and tested three classifiers (Generalized Linear Model, Multilayer Perceptron, and k-Nearest Neighbor) each on three tasks of genre classification using a large collection of labelled examples.

There was little variance in performance among the three classifiers. On average the classifiers correctly classified 77% of the test data in a task involving two highly similar genres, 82% in a task with three highly dissimilar genres, and 64% in a task with seven genres of mixed similarity.

postscript (301kB) PDF (655kB)
My MSc dissertation
extract-1.0.tar.gz (8kB)
Feature extraction source code
BibTeX entry