streaming music, timbre, minimum distance classification
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). All Faculty Publications. 945.
Physical and Mathematical Sciences
© 2007 Dan Ventura et al.
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