Algorithmic Clustering of Music
Sound
2016-08-31 v1 Machine Learning
Data Analysis, Statistics and Probability
Abstract
We present a fully automatic method for music classification, based only on compression of strings that represent the music pieces. The method uses no background knowledge about music whatsoever: it is completely general and can, without change, be used in different areas like linguistic classification and genomics. It is based on an ideal theory of the information content in individual objects (Kolmogorov complexity), information distance, and a universal similarity metric. Experiments show that the method distinguishes reasonably well between various musical genres and can even cluster pieces by composer.
Cite
@article{arxiv.cs/0303025,
title = {Algorithmic Clustering of Music},
author = {Rudi Cilibrasi and Paul Vitanyi and Ronald de Wolf},
journal= {arXiv preprint arXiv:cs/0303025},
year = {2016}
}
Comments
17 pages, 11 figures