Graduate School and Research Center in Digital Sciences

Variational bayesian feature saliency for audio type classification

Valente, Fabio;Wellekens, Christian J

ICASSP 2005, 30th IEEE International Conference on Acoustics, Speech, and Signal Processing, March 18-23, 2005- Philadelphia, USA

In this paper, 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 Models based recognition systems. VBFS is applied to capture inter-type and intra-type feature saliency for different audio type (music, background noise, wide band speech, narrow band speech, etc.) in order to increase model generality that’s always poor in non-speechmodels. We show that inferring saliency for different audio type improves classifications. Experiments are run on Broadcast news 1996-Hub4.

Document Doi Bibtex

Title:Variational bayesian feature saliency for audio type classification
Department:Multimedia Communications
Eurecom ref:1568
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Bibtex: @inproceedings{EURECOM+1568, doi = {}, year = {2005}, title = {{V}ariational bayesian feature saliency for audio type classification}, author = {{V}alente, {F}abio and {W}ellekens, {C}hristian {J}}, booktitle = {{ICASSP} 2005, 30th {IEEE} {I}nternational {C}onference on {A}coustics, {S}peech, and {S}ignal {P}rocessing, {M}arch 18-23, 2005- {P}hiladelphia, {USA}}, address = {{P}hiladelphia, {UNITED} {STATES}}, month = {03}, url = {} }
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