On mixture model complexity estimation for music recommender systems

Wietse Balkema, Ferdi van der Heijden, Bas Meijerink

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademic

Abstract

Content-based music navigation systems are in need of robust music similarity measures. Current similarity measures model each song with the same model parameters. We propose methods to efficiently estimate the required number of model parameters of each individual song. First results of a study on relationships between a small set of basic audio features are presented. We conclude that there are only very small correlations between models on low- and on high-dimensional features. When we compare a very simple clustering algorithm with an algorithm that estimates model parameters using the MDL criterium, we find a surprisingly strong correlation between the estimated number of mixture components.
Original languageEnglish
Title of host publicationProceedings 17th Annual Workshop on Circuits, Systems and Signal Processing 2006
PublisherSTW
Pages121-124
Number of pages4
EditionCD-ROM
ISBN (Print)978-90-73461-44-4
Publication statusPublished - 23 Nov 2006
Event17th Annual Workshop on Circuits, Systems and Signal Processing, ProRISC 2006 - Veldhoven, Netherlands
Duration: 23 Nov 200624 Nov 2006
Conference number: 17

Workshop

Workshop17th Annual Workshop on Circuits, Systems and Signal Processing, ProRISC 2006
Abbreviated titleProRISC 2006
Country/TerritoryNetherlands
CityVeldhoven
Period23/11/0624/11/06

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