Model breaking detection using independent component classifier
Résumé
This paper presents a neural architecture ibr model breaking detection in real world conditions. This technique use an Independent Component Classifier [1] for detection of unexpected or unknown events in noisy and varying environment. This method is based on subspace classifier [2] and Independant Component Analysis [3]. A feed-forward neural network adapts itself to input evolutions, by detecting novelties, creating and deleting classes. A second process achieves a prototype rotation in order to minimise mutual information of different classes. This synaptic weight evolution rule is based on an anti-hebbian learning rule inspired from neural methods for blind separation of sources [4]. Consequently , under the assumption of statistical independence of different classes, the system is able to detect novelties hidden by simultaneous acoustic events. Novelty detection performances in various situations have been tested : isolated novelty, novelty which occurs mixed with an event of a known class, and several simultaneous novelties. We have also studied the evolution of detection performances obtained by varying the noise level. These experiments have shown good detection performanc~ and low false detection rate.