Wavelet packets and de-noising based on higher-order-statistics for transient detection

• Published in: Signal processing Vol. 81; no. 9; pp. 1909 - 1926
• Main Authors: Ravier, Philippe, Amblard, Pierre-Olivier
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01-09-2001; Elsevier Science; Elsevier
• Subjects: Adapted segmentation; Applied sciences; Computer Science; De-noising; Detection, estimation, filtering, equalization, prediction; Engineering Sciences; Exact sciences and technology; Higher-order statistics; Information, signal and communications theory; Multiresolution analysis; ROC performance curves; Signal and communications theory; Signal and Image processing; Signal, noise; Telecommunications and information theory; Transient detection; Wavelet packets; Transient detection; De-noising; Adapted segmentation; Wavelet packets; Higher-order statistics; Multiresolution analysis; ROC performance curves; Performance evaluation; Transient response; Time frequency domain method; Segmentation; Acoustic signal; Noise reduction; Signal detector; Packet transmission; Algorithm; Statistical method; Wavelet transformation; Signal processing; Signal detection
• ISSN: 0165-1684; 1872-7557
• DOI: 10.1016/S0165-1684(01)00088-3

In this paper, we present a detector of transient acoustic signals that combines two powerful detection tools: a local wavelet analysis and higher-order statistical properties of the signals. The use of 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 wavelet packet transform. It leads to a partition of the signal which is ‘optimal’ according to a criterion that tests the Gaussian nature of the frequency bands. To get a time dependent detection curve, we perform a de-noising procedure on the wavelet coefficients: The Gaussian coefficients are set to zero. We then apply a classical method of detection on the time reconstructed de-noised signal. We study the performance of the detector in terms of experimental ROC curves. We show that the detector performs better than decompositions using other classical splitting criteria. In the last part, we present an application of the algorithm on real flow recordings of nuclear plant pipings. The detector indicates the presence of a missing body in the piping at some instants not seen with a classical energy detector.