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.