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              基于被動聲吶音頻信號的水中目標識別綜述

              徐齊勝 許可樂 竇勇 高彩麗 喬鵬 馮大為 朱博青

              徐齊勝, 許可樂, 竇勇, 高彩麗, 喬鵬, 馮大為, 朱博青. 基于被動聲吶音頻信號的水中目標識別綜述. 自動化學報, 2024, 50(4): 649?673 doi: 10.16383/j.aas.c230153
              引用本文: 徐齊勝, 許可樂, 竇勇, 高彩麗, 喬鵬, 馮大為, 朱博青. 基于被動聲吶音頻信號的水中目標識別綜述. 自動化學報, 2024, 50(4): 649?673 doi: 10.16383/j.aas.c230153
              Xu Qi-Sheng, Xu Ke-Le, Dou Yong, Gao Cai-Li, Qiao Peng, Feng Da-Wei, Zhu Bo-Qing. A review of underwater target recognition based on passive sonar acoustic signals. Acta Automatica Sinica, 2024, 50(4): 649?673 doi: 10.16383/j.aas.c230153
              Citation: Xu Qi-Sheng, Xu Ke-Le, Dou Yong, Gao Cai-Li, Qiao Peng, Feng Da-Wei, Zhu Bo-Qing. A review of underwater target recognition based on passive sonar acoustic signals. Acta Automatica Sinica, 2024, 50(4): 649?673 doi: 10.16383/j.aas.c230153

              基于被動聲吶音頻信號的水中目標識別綜述

              doi: 10.16383/j.aas.c230153
              詳細信息
                作者簡介:

                徐齊勝:國防科技大學計算機學院碩士研究生. 2021年獲得武漢大學學士學位. 主要研究方向為音頻信號處理, 并行計算. E-mail: qishengxu@nudt.edu.cn

                許可樂:國防科技大學計算機學院副教授. 2017年獲得法國巴黎六大博士學位. 主要研究方向為音頻信號處理, 機器學習和智能軟件系統. 本文通信作者. E-mail: xukelele@163.com

                竇勇:國防科技大學并行與分布處理國防科技重點實驗室教授. 1995年獲得國防科技大學博士學位. 主要研究方向為高性能計算, 智能計算, 機器學習和深度學習. E-mail: yongdou@nudt.edu.cn

                高彩麗:國防科技大學計算機學院碩士研究生. 2021年獲得南昌大學學士學位. 主要研究方向為人臉偽造檢測, 并行優化. E-mail: gaocl@nudt.edu.cn

                喬鵬:國防科技大學并行與分布處理國防科技重點實驗室助理研究員. 2018年獲得國防科技大學博士學位. 主要研究方向為高性能計算, 圖像恢復和深度強化學習. E-mail: pengqiao@nudt.edu.cn

                馮大為:國防科技大學計算機學院副教授. 2014年獲得法國巴黎第十一大學博士學位. 主要研究方向為分布計算與智能軟件系統. E-mail: dafeng@nudt.edu.cn

                朱博青:國防科技大學博士研究生. 2019年獲得國防科技大學碩士學位. 主要研究方向為多模態機器學習, 持續學習和計算聲學. E-mail: zhuboq@gmail.com

              A Review of Underwater Target Recognition Based on Passive Sonar Acoustic Signals

              More Information
                Author Bio:

                XU Qi-Sheng Master student at the School of Computer Science, National University of Defense Technology. He received his bachelor degree from Wuhan University in 2021. His research interest covers audio signal processing and parallel computing

                XU Ke-Le Associate professor at the School of Computer Science, National University of Defense Technology. He received his Ph.D. degree from Paris VI University in 2017. His research interest covers audio signal processing, machine learning, and intelligent software systems. Corresponding author of this paper

                DOU Yong Professor at the National Key Laboratory of Parallel and Distributed Processing, National University of Defense Technology. He received his Ph.D. degree from National University of Defense Technology in 1995. His research interest covers high performance computing, intelligence computing, machine learning, and deep learning

                GAO Cai-Li Master student at the School of Computer Science, National University of Defense Technology. He received his bachelor degree from Nanchang University in 2021. His research interest covers face forgery detection and parallel optimization

                QIAO Peng Assistant researcher at the National Key Laboratory of Parallel and Distributed Processing, National University of Defense Technology. He received his Ph.D. degree from National University of Defense Technology in 2018. His research interest covers high performance computing, image restoration, and deep reinforcement learning

                FENG Da-Wei Associate professor at the School of Computer Science, National University of Defense Technology. He received his Ph.D. degree from Paris-Sud University in 2014. His research interest covers distributed computing and intelligent software systems

                ZHU Bo-Qing Ph.D. candidate at the School of Computer Science, National University of Defense Technology. He received his master degree from National University of Defense Technology in 2019. His research interest covers multi-modal machine learning, continual learning, and computational acoustics

              • 摘要: 基于被動聲吶音頻信號的水中目標識別是當前水下無人探測領域的重要技術難題, 在軍事和民用領域都應用廣泛. 本文從數據處理和識別方法兩個層面系統闡述基于被動聲吶信號進行水中目標識別的方法和流程. 在數據處理方面, 從基于被動聲吶信號的水中目標識別基本流程、被動聲吶音頻信號分析的數理基礎及其特征提取三個方面概述被動聲吶信號處理的基本原理. 在識別方法層面, 全面分析基于機器學習算法的水中目標識別方法, 并聚焦以深度學習算法為核心的水中目標識別研究. 本文從有監督學習、無監督學習、自監督學習等多種學習范式對當前研究進展進行系統性的總結分析, 并從算法的標簽數據需求、魯棒性、可擴展性與適應性等多個維度分析這些方法的優缺點. 同時, 還總結該領域中較為廣泛使用的公開數據集, 并分析公開數據集應具備的基本要素. 最后, 通過對水中目標識別過程的論述, 總結目前基于被動聲吶音頻信號的水中目標自動識別算法存在的困難與挑戰, 并對該領域未來的發展方向進行展望.
              • 圖  1  基于機器學習的水聲目標識別方法

                Fig.  1  Machine learning-based methods for UATR

                圖  2  基于聲吶信號的水聲目標識別基本原理

                Fig.  2  Fundamental principles of UATR based on sonar signals

                圖  3  水聲目標識別的基本流程

                Fig.  3  Basic procedure of UATR

                圖  4  被動聲吶信號的特征圖示例

                Fig.  4  The illustrative feature examples of passive sonar signals

                圖  5  基于深度學習的水中音頻信號特征提取范式

                Fig.  5  Deep learning-based paradigm for underwater acoustic signals feature extraction

                圖  6  基于深度學習的水聲目標識別主流算法模型發展時間軸線

                Fig.  6  Timeline: Evolution of mainstream deep learning algorithms for UATR

                圖  7  基于CNN的水聲目標識別方法基本架構

                Fig.  7  Basic framework of CNN-based methods for UATR

                圖  8  基于CNN的水聲目標識別主流優化方法

                Fig.  8  Mainstream optimization methods for CNN-based UATR

                圖  9  基于CNN與Bi-LSTM融合的水聲目標識別方法網絡架構

                Fig.  9  Network framework of UATR methods based on the fusion of CNN and Bi-LSTM

                圖  10  基于Transformer的水聲目標識別方法基本架構

                Fig.  10  Basic framework of Transformer-based methods for UATR

                圖  11  基于RBM自編碼器重構的水聲目標識別方法架構

                Fig.  11  The framework of RBM autoencoder-based reconstruction methods for UATR

                圖  12  SSAST的網絡結構

                Fig.  12  The network architecture of SSAST

                圖  13  不同深度學習方法在水聲目標識別領域的性能對比

                Fig.  13  Performance comparison of various deep learning methods for UATR

                表  1  典型傳統機器學習的水聲目標識別算法

                Table  1  Typical traditional machine learning algorithms for UATR

                年份機器學習算法音頻特征數據集
                1992Naive Bayes[54]目標固有物理機理特征自建數據集
                2016DT[55]時域、頻域特征仿真數據集
                2014基于小波變換的時頻特征自建數據集
                2016MFCC真實數據集
                2017GFCC歷史數據集
                2017SVM[56?62]改進的GFCC艦船數據集
                2018過零率魚類數據集
                2019融合表征自建數據集
                2022LOFAR譜ShipsEar
                2018KNN[60, 62]MFCC魚類數據集
                2022LOFAR譜ShipsEar
                2011SVDD[63]艦船數據集
                2014HMM[56, 64]MFCC
                2018機器音頻數據集
                下載: 導出CSV

                表  2  基于卷積神經網絡的水聲目標識別方法

                Table  2  Convolutional neural network-based methods for UATR

                年份技術特點模型優劣分析數據集來源樣本大小
                2017卷積神經網絡[66]自動提取音頻表征, 提高了模型的精度Historic Naval Sound and Video database16類
                2018卷積神經網絡[71]使用極限學習機代替全連接層, 提高了模型的識別精度私有數據集3類
                2019卷積神經網絡[70]使用二階池化策略, 更好地保留了信號分量的差異性中國南海5類
                一種基于聲音生成感知機制的卷積神經網絡[41]模擬聽覺系統實現多尺度音頻表征學習, 使得表征更具判別性Ocean Networks Canada4類
                2020基于ResNet的聲音生成感知模型[42]使用全局平均池化代替全連接層, 極大地減少了參數, 提高了模型的訓練效率Ocean Networks Canada4類
                一種稠密卷積神經網絡DCNN[43]使用DCNN自動提取音頻特征, 降低了人工干預對性能的影響私有數據集12類
                2021一種具有稀疏結構的GoogleNet[72]稀疏結構的網絡設計減少了參數量, 提升模型的訓練效率仿真數據集3類
                一種基于可分離卷積自編碼器的SCAE模型[73]使用音頻的融合表征進行分析, 證明了方法的魯棒性DeepShip5類
                殘差神經網絡[76]融合表征使得學習到的音頻表征更具判別性, 提升了模型的性能ShipsEar5類
                基于注意力機制的深度神經網絡[46]使用注意力機制抑制了海洋環境噪聲和其他艦船信號的干擾, 提升模型的識別能力中國南海4類
                基于雙注意力機制和多分辨率卷積神經網絡架構[81]多分辨率卷積網絡使得音頻表征更具判別性, 雙注意力機制有利于同時關注局部信息與全局信息ShipsEar5類
                基于多分辨率的時頻特征提取與數據增強的水中目標識別方法[85]多分辨率卷積網絡使得音頻表征更具判別性, 數據增強增大了模型的訓練樣本規模, 從而提升了模型的識別性能ShipsEar5類
                2022基于通道注意力機制的殘差網絡[82]通道注意力機制的使用使得學習到的音頻表征更具判別性和魯棒性, 提升了模型的性能私有數據集4類
                一種基于融合表征與通道注意力機制的殘差網絡[83]融合表征與通道注意力機制的使用使得學習到的音頻表征更具判別性和魯棒性, 提升了模型的性能DeepShip ShipsEar5類
                2023基于注意力機制的多分支CNN[74]注意力機制用以捕捉特征圖中的重要信息, 多分支策略的使用提升了模型的訓練效率ShipsEar5類
                下載: 導出CSV

                表  3  基于時延神經網絡、循環神經網絡和Transformer的水聲目標識別方法

                Table  3  Time delay neural networks-based, recurrent neural network-based and Transformer-based methods for UATR

                年份技術特點模型優劣分析數據集來源樣本大小
                2019基于時延神經網絡的UATR[87]時延神經網絡能夠學習音頻時序信息, 從而提高模型的識別能力私有數據集2類
                2022一種可學習前端[88]可學習的一維卷積濾波器可以實現更具判別性的音頻特征提取, 表現出比傳統手工提取的特征更好的性能QLED, ShipsEar,
                DeepShip
                QLED 2類ShipsEar 5類DeepShip 4類
                2020采用Bi-LSTM同時考慮過去與未來信息的UATR[89]使用雙向注意力機制能夠同時學習到歷史和后續的時序信息, 從而使得音頻表征蘊含信息更豐富以及判別性更高, 然而該方法復雜度較高Sea Trial2類
                基于Bi-GRU的混合時序網絡[90]混合時序網絡從多個維度關注時序信息, 從而學習到更具判別性的音頻特征, 提高模型的識別能力私有數據集3類
                2021采用LSTM融合音頻表征[91]該方法能夠同時學習音頻的相位和頻譜特征, 并加以融合, 從而提升模型的識別性能私有數據集2類
                CNN與Bi-LSTM組合的UATR[92]CNN與Bi-LSTM組合可以提取出同時關注局部特性和時序上下文依賴的音頻特征, 提高了模型的識別能力私有數據集3類
                2022一維卷積與LSTM組合的UATR[93]首次采用一維卷積和LSTM的組合網絡提取音頻表征, 能夠在提高音頻識別率的同時降低模型的參數量, 然而該方法穩定性有待提高ShipsEar5類
                2022Transformer[94?95]增強了模型的泛化性和學習能力, 提高了模型的識別準確率ShipsEar5類
                加入逐層聚合的Token機制, 同時兼顧全局信息和局部特性, 提高了模型的識別準確率ShipsEar,
                DeepShip
                5類
                下載: 導出CSV

                表  4  基于遷移學習的水聲目標識別方法

                Table  4  Transfer learning-based methods for UATR

                年份技術特點模型優劣分析數據集來源樣本大小
                2019基于ResNet的遷移學習[98]在保證較高性能的同時減少對標簽樣本的需求, 但不同領域任務的數據特征分布存在固有偏差Whale FM website16類
                2020基于ResNet的遷移學習[99]在預訓練模型的基礎上設計模型集成機制, 提升識別性能的同時減少了對標簽樣本的需求, 但不同領域任務的數據特征分布存在固有偏差私有數據集2類
                基于CNN的遷移學習[102]使用AudioSet音頻數據集進行預訓練, 減輕了不同領域任務的數據特征分布所存在的固有偏差
                2022基于VGGish的遷移學習[103]除了使用AudioSet數據集進行預訓練, 還設計基于時頻分析與注意力機制結合的特征提取模塊, 提高了模型的泛化能力ShipsEar5類
                下載: 導出CSV

                表  5  基于無監督學習和自監督學習的水聲目標識別方法

                Table  5  Unsupervised and self-supervised learning-based methods for UATR

                年份技術特點模型優劣分析數據集來源樣本大小
                2013深度置信網絡[104?108]對標注數據集的需求小, 但由于訓練數據少, 容易出現過擬合的風險私有數據集40類
                2017加入混合正則化策略, 增強了所學到音頻表征的判別性, 提高了模型的識別準確率3類
                2018加入競爭機制, 增強了所學到音頻表征的判別性, 提高了模型的識別準確率2類
                2018加入壓縮機制, 減少了模型的冗余參數, 提升了模型的識別準確率中國南海2類
                2021基于RBM自編碼器與重構的SSL[111]降低了模型對標簽數據的需求, 增強了模型的泛化性和可擴展性ShipsEar5類
                2022基于掩碼建模與重構的SSL[113]使用掩碼建模與多表征重構策略, 提升了模型對特征的學習能力, 從而提升了識別性能DeepShip5類
                2023基于自監督對比學習的SSL[112]降低了模型對標簽數據的需求, 增強了學習到的音頻特征的泛化性和對數據的適應能力ShipsEar, DeepShip5類
                下載: 導出CSV

                表  6  常用的公開水聲數據集總結

                Table  6  Summary of commonly used public underwater acoustic signal datasets

                數據集名稱數據結構數據類型采樣頻率 (kHz)獲取地址
                類別樣本數 (個)持續時間 (樣本)總時間
                DeepShipCargo Ship110180 ~ 610 s10 h 40 min音頻32DeepShip
                Tug70180 ~ 1140 s11 h 17 min
                Passenger Ship706 ~ 1530 s12 h 22 min
                Tanker706 ~ 700 s12 h 45 min
                ShipsEarA161729 s音頻32ShipsEar
                B171435 s
                C274054 s
                D92041 s
                E12923 s
                Ocean Network Canada (ONC)Background noise170003 s8 h 30 min音頻ONC
                Cargo170008 h 30 min
                Passenger Ship170008 h 30 min
                Pleasure craft170008 h 30 min
                Tug170008 h 30 min
                Five-element acoustic dataset9個類別3600.5 s180 s音頻Five-element···
                Historic Naval Sound and Video16個類別2.5 s音視頻10.24Historic Naval···
                DIDSON8個類別524DIDSON
                Whale FMPilot whale108581 ~ 8 s5 h 35 min音頻Whale FM
                Killer whale4673
                注: 1. 官方給出的DeepShip數據集只包含Cargo Ship、Tug、Passenger Ship和Tanker這4個類別. 在實際研究中, 學者通常會自定義一個新的類別
                “Background noise”作為第5類.
                2. 獲取地址的訪問時間為2023-07-20.
                下載: 導出CSV
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                        出版歷程
                        • 收稿日期:  2023-03-22
                        • 錄用日期:  2023-07-10
                        • 網絡出版日期:  2024-03-14
                        • 刊出日期:  2024-04-26

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