Underwater Acoustic Sensing with Rational Orthogonal Wavelet Pulse and Auditory Frequency Cepstral Coefficient-Based Feature Extraction

Guo Tiantian, E. Lim, M. López-Benítez, Ma Fei, Yu Limin
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引用次数: 1

Abstract

Active pulse design, target detection and classification play an essential role in underwater acoustic sensing. This paper addresses the system design with three kinds of pulse signals, including continuous wave (CW), linear frequency modulation (LFM) signal and rational orthogonal wavelet (ROW) signal. The detector design has an architecture of feature extraction and convolutional neural network (CNN) based classification. A geometric underwater channel model is adopted to facilitate the generation of training datasets with designated geometric underwater environment parameters. The simulated received pulse signals are converted into feature maps as the input of the classifier. This paper applies the acoustic features, Short Time Fourier Transform (STFT), Mel Frequency Cepstral Coefficients (MFCC) and Gammatone Frequency Cepstral Coefficients (GFCC) to construct different feature maps. A lightweight CNN model is used as the classifier. Experiments demonstrate the superiority of the ROW wavelet pulse signals and the proposed algorithm in target localization and underwater signal classification.
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基于合理正交小波脉冲和听觉倒谱系数的水声传感特征提取
主动脉冲设计、目标检测与分类在水声传感中起着至关重要的作用。本文采用连续波(CW)、线性调频(LFM)和有理正交小波(ROW)三种脉冲信号进行系统设计。该检测器设计具有特征提取和基于卷积神经网络(CNN)分类的结构。采用几何水下通道模型,便于生成具有指定几何水下环境参数的训练数据集。将接收到的模拟脉冲信号转换成特征映射作为分类器的输入。本文利用声学特征、短时傅里叶变换(STFT)、Mel频率倒谱系数(MFCC)和γ酮频率倒谱系数(GFCC)来构建不同的特征映射。使用轻量级CNN模型作为分类器。实验证明了ROW小波脉冲信号及其算法在目标定位和水下信号分类方面的优越性。
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