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Benchmarking Methods for Audio-Visual Recognition Using Tiny Training Sets

Xavier Alameda-Pineda 1 Jordi Sanchez-Riera 1 Radu Horaud 1, * 
* Corresponding author
1 PERCEPTION - Interpretation and Modelling of Images and Videos
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology
Abstract : The problem of choosing a classifier for audio-visual command recognition is addressed. Because such commands are culture- and user-dependant, methods need to learn new commands from a few examples. We benchmark three state-of-the-art discriminative classifiers based on bag of words and SVM. The comparison is made on monocular and monaural recordings of a publicly available dataset. We seek for the best trade off between speed, robustness and size of the training set. In the light of over 150,000 experiments, we conclude that this is a promising direction of work towards a flexible methodology that must be easily adaptable to a large variety of users.
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Submitted on : Friday, September 13, 2013 - 11:27:03 AM
Last modification on : Thursday, May 5, 2022 - 3:11:28 AM
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Xavier Alameda-Pineda, Jordi Sanchez-Riera, Radu Horaud. Benchmarking Methods for Audio-Visual Recognition Using Tiny Training Sets. ICASSP 2013 - IEEE International Conference on Acoustics, Speech, and Signal Processing, IEEE Signal Processing Society, May 2013, Vancouver, Canada. pp.3662-3666, ⟨10.1109/ICASSP.2013.6638341⟩. ⟨hal-00861645⟩



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