Wavelet network-based detection and classification of transients

A methodology is presented for developing a digital signal-processing architecture 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 artificial neural networks. By exploiting the modularity as well as original strategies concerning wavelet network implementation and training, the method succeeds in enhancing the classification performance with respect to other available solutions.