Creating a Simplified Music Mood Classification Ground-Truth Set
نویسندگان
چکیده
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.
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تاریخ انتشار 2007