Thresholding wavelet networks for signal classification

This paper reports a new signal classification tool, a modified wavelet network called Thresholding Wavelet Networks (TWN). The network is designed for the purposes of classifying signals. The philosophy of the technique is that often the difference between signals may not lie in the spectral or temporal region where the signal strength is high. Unlike other wavelet networks, this network does not concentrate necessarily on the high-energy region of the input signals. The network iteratively identifies the suitable wavelet coefficients (scale and translation) that best differentiate the different signals provided during training, irrespective of the ability of these coefficients to represent the signals. The network is not limited to the changes in temporal location of the signal identifiers. This paper also reports the testing of the network using simulated signals.