Robust underwater noise targets classification using auditory inspired time-frequency analysis
Robust underwater noise targets classification using auditory inspired time-frequency analysis
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DOI：
10.1016/j.apacoust.2013.11.003
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发表时间：
2014-04-01
影响因子：
3.4
通讯作者：
Zeng, Xiangyang
中科院分区：
文献类型：
--
作者：
Wang, Shuguang;Zeng, Xiangyang
水下噪声目标分类在许多领域有着广泛的应用。在远距离检测过程中，不可避免的环境噪声会降低识别精度。因此，需要开发强有力的分类方法。受人耳听觉感知的启发，提出了一种结合巴克小波分析和希尔伯特黄变换的时频分析方法。利用巴克小波分析，信号被分成不同的子带，对应于听觉感知。然后应用去噪来增强分析的信号。利用Hilbert-Huang变换，提取了信号的瞬时频率和瞬时幅值。基于这些瞬时参数，各种功能的构建和比较。支持向量机被用作分类器。实验中使用了记录的水下噪声目标信号。通过加入不同水平的白色高斯噪声来模拟不同的信噪比。在实验中使用交叉验证程序。实验结果表明，与其他方法相比，该方法在不同信噪比下都能取得较好的识别效果。(C)2013爱思唯尔有限公司版权所有。
Underwater noise targets classification has variable applications in many fields. During the long range detection, inevitable environmental noise will decrease the recognition accuracy. Thus, robust classification methods need to be developed. Inspired by human auditory perception, a time-frequency analysis method that combines the Bark-wavelet analysis and Hilbert-Huang transform is presented. By using Bark-wavelet analysis, signals are divided into different sub-bands that correspond to the auditory perception. Then denoising is applied to enhance the analyzed signals. With the help of Hilbert-Huang transform, instantaneous frequencies and amplitudes are extracted. Based on these instantaneous parameters, various features are constructed and compared. Support vector machines are used as the classifier. Recorded underwater noise targets signals are used for the experiments. Various signal-to-noise ratios are simulated through the adding of white Gaussian noise at various levels. Cross-validation procedure was used in the experiments. The results showed that proposed method could achieve better recognition performances under different SNRs comparing to other methods. (C) 2013 Elsevier Ltd. All rights reserved.