CREATING A SIMPLIFIED MUSIC MOOD CLASSIFICATION GROUND-TRUTH SET
Xiao Hu, Mert Bay, John Stephen Downie · 2007
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. 1