Audio classification using extended baum-welch transformations

Audio classification has applications in a variety of contexts, such as automatic sound analysis, supervised audio segmentation and in audio information search and retrieval. Extended Baum-Welch (EBW) transformations are most commonly used as a discriminative technique for estimating parameters of Gaussian mixtures, though recently they have been applied in unsupervised audio segmentation. In this paper, we extend the use of these transformations to derive an audio classification algorithm. We find that our method outperforms both the Support Vector Machine (SVM) and Gaussian Mixture Model (GMM) likelihood classification methods.