Combining an adapted wavelet analysis with fourth-order statistics for transient detection
Combining an adapted wavelet analysis with fourth-order statistics for transient detection
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DOI：
10.1016/s0165-1684(98)00117-0
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发表时间：
1998-10-01
影响因子：
4.4
通讯作者：
Amblard, PO
中科院分区：
文献类型：
--
作者：
Ravier, P;Amblard, PO
本文提出了一种新的瞬态声信号检测器。它结合了两个强大的检测工具：局部小波分析和信号的高阶统计特性。使用这两种技术使得在低信噪比条件下，当其他检测手段不再足够时，检测成为可能。该算法使用自适应Malvar小波变换。它导致信号的分区，根据测试段的高斯性质的标准，该分区是“最佳的”。基于四阶累积量的统计量从该分割计算。本文的第二部分是对探测器性能的研究。这项研究是根据实验ROC曲线。我们表明，在一般情况下，检测器的性能优于能量检测器。实际上，性能取决于瞬态的性质;我们的检测器特别适合振荡瞬态。(C)1998 Elsevier Science B. V.保留所有权利。
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.