Algorithmic clustering of music Export

@inproceedings{citeulike:951025, abstract = {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.}, author = {Cilibrasi, Rudi L. and Vit\'{a}nyi, Paul M. B. and de Wolf, Ronald}, citeulike-article-id = {951025}, citeulike-linkout-0 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1358107}, journal = {Web Delivering of Music, 2004. WEDELMUSIC 2004. Proceedings of the Fourth International Conference on}, keywords = {clustering, compression-distance, cultural-artifacts, methodology, quartet-tree}, pages = {110--117}, posted-at = {2006-11-18 19:30:18}, priority = {2}, title = {Algorithmic clustering of music}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1358107}, year = {2004} }

TY - CONF ID - citeulike:951025 L3 - citeulike-article-id:951025 N2 - 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. JF - Web Delivering of Music, 2004. WEDELMUSIC 2004. Proceedings of the Fourth International Conference on EP - 117 TI - Algorithmic clustering of music SP - 110 KW - clustering KW - compression-distance KW - cultural-artifacts KW - methodology KW - quartet-tree AU - Cilibrasi, Rudi AU - Vitányi, Paul AU - de Wolf, Ronald PY - 2004/// UR - http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1358107 ER -