He Lei, Shen Xiao-Hong, Zhang Mu-Hang, Wang Hai-Yan
School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an 710072, China.
School of Electronic Information and Artificial Intelligence, Shaanxi University of Science and Technology, Xi'an 710021, China.
Entropy (Basel). 2020 Mar 25;22(4):374. doi: 10.3390/e22040374.
Due to the diversity of ship-radiated noise (SRN), audio segmentation is an essential procedure in the ship statuses/categories identification. However, the existing segmentation methods are not suitable for the SRN because of the lack of prior knowledge. In this paper, by a generalized likelihood ratio (GLR) test on the ordinal pattern distribution (OPD), we proposed a segmentation criterion and introduce it into single change-point detection (SCPD) and multiple change-points detection (MCPD) for SRN. The proposed method is free from the acoustic feature extraction and the corresponding probability distribution estimation. In addition, according to the sequential structure of ordinal patterns, the OPD is efficiently estimated on a series of analysis windows. By comparison with the Bayesian Information Criterion (BIC) based segmentation method, we evaluate the performance of the proposed method on both synthetic signals and real-world SRN. The segmentation results on synthetic signals show that the proposed method estimates the number and location of the change-points more accurately. The classification results on real-world SRN show that our method obtains more distinguishable segments, which verifies its effectiveness in SRN segmentation.
由于舰船辐射噪声（SRN）的多样性，音频分割是舰船状态/类别识别中的一个重要步骤。然而，由于缺乏先验知识，现有的分割方法不适用于SRN。在本文中，通过对顺序模式分布（OPD）进行广义似然比（GLR）检验，我们提出了一种分割准则，并将其引入到SRN的单变点检测（SCPD）和多变点检测（MCPD）中。所提出的方法无需进行声学特征提取和相应的概率分布估计。此外，根据顺序模式的顺序结构，在一系列分析窗口上有效地估计了OPD。通过与基于贝叶斯信息准则（BIC）的分割方法进行比较，我们评估了所提出方法在合成信号和实际SRN上的性能。合成信号上的分割结果表明，所提出的方法能更准确地估计变点的数量和位置。实际SRN上的分类结果表明，我们的方法获得了更具区分性的片段，这验证了其在SRN分割中的有效性。
