Boosting Classifiers for Music Genre Classification
Ulaş Bağcı, Engin Erzin · 2006
This paper investigates discriminative boosting of classifiers to improve the automatic music genre classification performance. Two classifier structures, boosting of the Gaussian mixture model based classifiers and classifiers that are using the intergenre similarity information, are proposed. The first classifier structure presents a novel extension to the maximum-likelihood based training of the Gaussian mixtures to integrate GMM classifier into boosting architecture. In the second classifier structure, the boosting idea is modified to better model the intergenre similarity information over the mis-classified feature population. Once the inter-genre similarities are modeled, elimination of the inter-genre similarities reduces the inter-genre confusion and improves the identification rates. A hierarchical auto-clustering classifier scheme is integrated into the intergenre similarity modeling. Experimental results with promising classification improvements are provided.