Sept. 13, 2004 to Sept. 14, 2004
Rudi Cilibrasi , CWI, The Netherlands
Paul Vit?nyi , CWI, The Netherlands
Ronald de Wolf , CWI, The Netherlands
We present a method for hierarchical music clustering, based 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, literature, and genomics. Indeed, it can be used to simultaneously cluster objects from completely different domains, like with like. It is based on an ideal theory of the information content in individual objects (Kolmogorov complexity), information distance, and a universal similarity metric. The approximation to the universal similarity metric obtained using standard data compressors is called "normalized compression distance (NCD)." Experiments using our CompLearn software tool show that the method distinguishes between various musical genres and can even cluster pieces by composer.
Rudi Cilibrasi, Paul Vit?nyi, Ronald de Wolf, "Algorithmic Clustering of Music", WEDELMUSIC, 2004, Web Delivering of Music, International Conference on, Web Delivering of Music, International Conference on 2004, pp. 110-117, doi:10.1109/WDM.2004.1358107