An approach based on variational Bayesian feature saliency (VBFS) for robust audio type classification is proposed. VBFS aims at finding the most discriminative features in Gaussian mixture model based recognition systems. VBFS is applied to capture inter-type and intra-type feature saliency for different audio types (music, background noise, wide band speech, narrow band speech, etc.) in order to increase a model generality that is always poor in non-speech models. We show that inferring saliency for different audio types improves classifications. Experiments are run on broadcast news 1996-Hub4.
['Fabio Valente', 'Christian Wellekens']
Variational Bayesian feature saliency for audio type classification