This paper presents a neural architecture for 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.
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