A B S T R A C T This paper describes a new neural architecture for u i i-supervised learning of a classificat,ion of mixed t.rair-sient, signals. This method is I)asetl oii neural tkcli-niques for blind separation of sources and sul)space i-net,liotls. The feed-forward neural iiet,work dynaiii-ically builds and refreshes a n acoustic event.s classification by detecting novelties, creat,ing antl deleting classes. A self-organization process achieves a class prototype rotation in order to niinirnise the st,at,isti-cal dependence of class activities. Siiriulatd nirilt,i-tliniensional signals and rnixed acoustic signals i i i real noisy environment have been used to test, our inotlel. T h c result