Wavelet Packet Approach to Transient Signal Classification.

摘要

Time-frequency transforms, including wavelet and wavelet packet transforms, are generally acknowledged to be useful for studying non-stationary phenomena and, in particular, have been shown or claimed to be of value in the detection and characterization of transient signals. In many applications time-frequency transforms are simply employed as a visual aid to be used for signal display. Although there have been several studies reported in the literature, there is still considerable work to be done investigating the utility of wavelet and wavelet packet time-frequency transforms for automatic transient signal classification. In this paper, we contribute to this ongoing investigation by exploring the feasibility of applying the wavelet packet transform to automatic detection and classification of a specific set of transient signals in background noise. In particular, a noncoherent wavelet- packet-based algorithm specific to the detection and classification of underwater acoustic signals generated by snapping shrimp and sperm whale clicks is proposed. We develop a systematic feature extraction process which exploits signal class differences in the wavelet packet transform coefficients. The wavelet-packet-based features obtained by our method for the biologically generated underwater acoustic signals yield excellent classification results when used as input for a neural network and a nearest neighbor rule.