Keywords
streaming music, timbre, minimum distance classification
Abstract
We consider the problem of measuring the similarity of streaming music content and present a method for modeling, on the fly, the temporal progression of a song’s timbre. Using a minimum distance classification scheme, we give an approach to classifying streaming music sources and present performance results for auto-associative song identification and for content-based clustering of streaming music. We discuss possible extensions to the approach and possible uses for such a system.
Original Publication Citation
Jake Merrell, Dan Ventura and Bryan Morse, "Clustering Music via the Temporal Similarity of Timbre", IJCAI Workshop on Artificial Intelligence and Music, pp. 153-164, 27.
BYU ScholarsArchive Citation
Merrell, Jacob; Morse, Bryan S.; and Ventura, Dan A., "Clustering Streaming Music via the Temporal Similarity of Timbre" (2007). Faculty Publications. 945.
https://scholarsarchive.byu.edu/facpub/945
Document Type
Peer-Reviewed Article
Publication Date
2007-01-01
Permanent URL
http://hdl.lib.byu.edu/1877/2552
Publisher
IJCAI
Language
English
College
Physical and Mathematical Sciences
Department
Computer Science
Copyright Status
© 2007 Dan Ventura et al.
Copyright Use Information
http://lib.byu.edu/about/copyright/