AIKED'06 Proceedings of the 5th WSEAS International Conference on Artificial Intelligence, Knowledge Engineering and Data Bases
Evolving computer-generated music by means of the normalized compression distance
SMO'05 Proceedings of the 5th WSEAS international conference on Simulation, modelling and optimization
"Tell me more": finding related items from user provided feedback
DS'11 Proceedings of the 14th international conference on Discovery science
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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.