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Creating a Simplified Music Mood Classification Ground-Truth Set (2007)
| Content Provider | CiteSeerX |
|---|---|
| Author | Hu, Xiao Bay, Mert Downie, J. Stephen |
| Description | 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 In Proceedings of the 8th International Conference on Music Information Retrieval (ISMIR 2007 |
| File Format | |
| Language | English |
| Publisher Date | 2007-01-01 |
| Access Restriction | Open |
| Subject Keyword | Ground-truth Dataset Ground-truth Set Fm Tag Uspop Audio Collection Standardized Mood Classification Testbed High-level Mood Category Formal Cross-algorithm Comparison Meaningful Cluster-based Set Mood-based Music Information Retrieval K-means Clustering Method |
| Content Type | Text |
| Resource Type | Article |