Seth's AI MSc Dissertation


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

Related Work

For more information on this topic, see my list of music audio analysis resources.