Combining an adapted wavelet analysis with fourth-order statistics for transient detection

In this paper, we present a new detector of transient acoustic signals. It combines two powerful detection tools: a local wavelet analysis and higher-order statistical properties of the signals. Using both techniques makes detection possible in low signal-to-noise ratio conditions, when other means of detection are no longer sufficient. The proposed algorithm uses the adapted Malvar wavelet transform. It leads to a partition of the signal which is 'optimal' according to a criterion that tests the Gaussian nature of the segments. A statistic based on the fourth-order cumulant is computed from this segmentation. The second part of this paper is devoted to the study of performance of the detector. This study is made in terms of experimental ROC curves. We show that in general the detector performs better than the energy detector. Actually, the performance depends on the nature of the transients; our detector is especially well adapted for oscillatory transients. (C) 1998 Elsevier Science B.V. All rights reserved.