8th Annual Conference of the International Speech Communication Association

Antwerp, Belgium
August 27-31, 2007

Audio Classification Using Extended Baum-Welch Transformations

Tara N. Sainath (1), Victor Zue (1), Dimitri Kanevsky (2)

(1) MIT, USA
(2) IBM T.J. Watson Research Center, USA

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.

Full Paper

Bibliographic reference.  Sainath, Tara N. / Zue, Victor / Kanevsky, Dimitri (2007): "Audio classification using extended baum-welch transformations", In INTERSPEECH-2007, 2969-2972.