Mixed acoustic events classification using ICA and subspace classifier

This paper describes a new neural architecture for unsupervised learning of a classi cation of mixed transient signals. This method is based on neural techniques for blind separation of sources and subspace methods. The feed-forward neural network dynamically builds and refreshes an acoustic events classication by detecting novelties, creating and deleting classes. A self-organization process achieves a class prototype rotation in order to minimise the statistical dependence of class activities. Simulated multidimensional signals and mixed acoustic signals in real noisy environment have been used to test our model. The results on classi cation and detection model properties are encouraging, in spite of structured sound bad modeling.