Creating a simplified music mood classification ground-truth set

Xiao Hu, Mert Bay, J. Stephen Downie

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

Abstract

A standardized mood classification testbed is needed for formal cross-algorithm comparison and evaluation. In this poster, we present a simplification of the problems associated with developing a ground-truth set for the evaluation of mood-based Music Information Retrieval (MIR) systems. Using a dataset derived from Last.fm tags and the USPOP audio collection, we have applied a K-means clustering method to create a simple yet meaningful cluster-based set of high-level mood categories as well as a ground-truth dataset.

Original languageEnglish (US)
Title of host publicationProceedings of the 8th International Conference on Music Information Retrieval, ISMIR 2007
Pages309-310
Number of pages2
StatePublished - 2007
Event8th International Conference on Music Information Retrieval, ISMIR 2007 - Vienna, Austria
Duration: Sep 23 2007Sep 27 2007

Publication series

NameProceedings of the 8th International Conference on Music Information Retrieval, ISMIR 2007

Other

Other8th International Conference on Music Information Retrieval, ISMIR 2007
Country/TerritoryAustria
CityVienna
Period9/23/079/27/07

ASJC Scopus subject areas

  • Music
  • Information Systems

Fingerprint

Dive into the research topics of 'Creating a simplified music mood classification ground-truth set'. Together they form a unique fingerprint.

Cite this