Universität Bielefeld Electronic Collections animiertes Foto Universität Bielefeld

Access to the Document



Visual mining in music collections with Emergent SOM

Risi, Sebastian ; Mörchen, Fabian ; Ultsch, Alfred ; Lehwark, Pascal



Download file

Abstract:
Different methods of organizing large collections of music with databionic mining techniques are described. The Emergent Self-Organizing Map is used to cluster and visualize similar artists and songs. The first method is the MusicMiner system that utilizes semantic descriptions learned from low level audio features for each song. The second method uses tags that have been assigned to music artists by the users of the social music platform Last.fm. For both methods we demonstrate the visualization capabilities of the U-Map. An intuitive browsing of large music collections is offered based on the paradigm of topographic maps. The semantic concepts behind the features enhance the interpretability of the maps.


Keywords: music similarity, clustering, ESOM, visualization, tagged data
Institution: Faculty of Technology, Research Groups in Informatics
DDC classification: Data processing, computer science, computer systems

Suggested Citation:
Risi, Sebastian ; Mörchen, Fabian ; Ultsch, Alfred ; Lehwark, Pascal  (2007)  Visual mining in music collections with Emergent SOM.


URL: http://biecoll.ub.uni-bielefeld.de/volltexte/2007/155



 Questions or comments: publikationsdienste.ub@uni-bielefeld.de
 Latest update: 15 Feb 2011
 Legal Notice
OPUS-Logo     OAI compliant      BU Logo
OAI-Logo