TY - GEN
T1 - Underwater Acoustic Sensing with Rational Orthogonal Wavelet Pulse and Auditory Frequency Cepstral Coefficient-Based Feature Extraction
AU - Tiantian, Guo
AU - Lim, Eng Gee
AU - Lopez-Benitez, Miguel
AU - Fei, Ma
AU - Limin, Yu
N1 - Funding Information:
This research was partially funded by Research Enhancement Fund of XJTLU (REF-19-01-04), National Natural Science Foundation of China (NSFC) (Grant No. 61501380), and by AI University Research Center (AI-URC) and XJTLU Laboratory for Intelligent Computation and Financial Technology through XJTLU Key Programme Special Fund (KSFP-02), Jiangsu Data Science and Cognitive Computational Engineering Research Centre, ARIES Research Centre, and Suzhou Municipal Key Laboratory Broadband Wireless Access Technology.
Publisher Copyright:
© 2022 IEEE.
PY - 2022/12/16
Y1 - 2022/12/16
N2 - 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.
AB - 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.
KW - CNN
KW - Gammatone frequency cepstral coefficient (GFCC)
KW - Mel frequency cepstral coefficient (MFCC)
KW - Rational orthogonal wavelet (ROW)
KW - Tracking
KW - Underwater communication
UR - http://www.scopus.com/inward/record.url?scp=85147695298&partnerID=8YFLogxK
U2 - 10.1109/ICCWAMTIP56608.2022.10016489
DO - 10.1109/ICCWAMTIP56608.2022.10016489
M3 - Conference Proceeding
AN - SCOPUS:85147695298
T3 - 2022 19th International Computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2022
BT - 2022 19th International Computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 19th International Computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2022
Y2 - 16 December 2022 through 18 December 2022
ER -