Wavelet network-based detection and classification of transients

A methodology is presented for developing a digital signal-processing archi tecture capable of simultaneous and automated detection and classification of transient signals. The basic unit of the aforementioned architecture is the wavelet network, which combines the ability of the wavelet transform of analyzing nonstationary signals with the classification capability of arti ficial neural networks. By exploiting the modularity as well as original strategies concerning wave let network implementation and training, the method succeeds in enhancing t he classification performance with respect to other available solutions.