DocumentCode :
2958378
Title :
Enhancing SOM digital music archives using Scatter-Gather
Author :
Azcarraga, Arnulfo P. ; Caw, Aldrirch C.
Author_Institution :
De La Salle Univ., Manila
fYear :
2008
fDate :
1-8 June 2008
Firstpage :
1833
Lastpage :
1839
Abstract :
The MarB system is a digital archive of music files that are clustered and laid out as a self-organized map, following the SOM methodology for large digital archives. The system has the usual music archive features as follows: 1) automatic clustering and organization of music files into ldquoislands of related musicrdquo; 2) classification of music clusters into various music genres; 3) playback of music files selected by the user; and 4) automatic generation of related music files for every music file that is chosen. In addition to these rather common features found in most self-organizing maps (SOM) based digital music archives, MarB also allows for an interactive selection and clustering of sets and subsets of music files until a specific music file is found. This is done using a Scatter/Gather interface that allows the user to select interesting clusters of music files (gather mode), which are then re-organized and re-clustered (scatter mode) for the user to visually inspect and possibly listen to. The user is then asked to select new interesting clusters (gather mode again). This alternating selection and re-clustering process continues until the user chooses a specific music file, and is provided with a set of most related music files. A novel album dispersal measure is used to objectively assess the quality of the clusters produced both by the SOM and the special k -means algorithm employed in the Scatter-Gather module.
Keywords :
file organisation; information retrieval systems; music; self-organising feature maps; SOM digital music archives; Scatter-Gather module; automatic clustering; automatic generation; music files organization; self-organized map; Neural networks; Scattering;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence). IEEE International Joint Conference on
Conference_Location :
Hong Kong
ISSN :
1098-7576
Print_ISBN :
978-1-4244-1820-6
Electronic_ISBN :
1098-7576
Type :
conf
DOI :
10.1109/IJCNN.2008.4634047
Filename :
4634047
Link To Document :
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