TY - GEN
T1 - Underwater Target Detection and Localization with Feature Map and CNN-Based Classification
AU - Guo, Tiantian
AU - Song, Yunze
AU - Kong, Zejian
AU - Lim, Enggee
AU - Lopez-Benitez, Miguel
AU - Ma, Fei
AU - Yu, Limin
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 (KSF-P-02), Jiangsu Data Science and Cognitive Computational Engineering Research Centre, and ARIES Research Centre.
Publisher Copyright:
© 2022 IEEE.
PY - 2022/4/22
Y1 - 2022/4/22
N2 - The purpose of this paper is to apply the acoustic features, Mel Frequency Cepstral Coefficient (MFCC) and Gammatone Frequency Cepstral Coefficient (GFCC), to underwater signal classification. Underwater acoustic signals are vibration signals, and their characteristics are similar to speech signals. The auditory feature extraction method in speech recognition can also be applied to the underwater environment. For underwater communication, we simulate two models designed for underwater target detection and localization. One is the deterministic model, which is considered as basic model; the other is to combine the deterministic model and statistic model, which is called combined model. The geometric channel model facilitates the generation of the database for different geometric settings. The database is generated by adjusting the parameters of the underwater environment. The classifier adopts a convolutional neural network (CNN). The input to the CNN is the feature maps after feature extraction. We choose continuous wavelet transform (CWT) and short-time Fourier transform (STFT) for comparison. Experiments show the effectiveness of the system architecture and superiority of the proposed algorithm in underwater signal classification and target localization.
AB - The purpose of this paper is to apply the acoustic features, Mel Frequency Cepstral Coefficient (MFCC) and Gammatone Frequency Cepstral Coefficient (GFCC), to underwater signal classification. Underwater acoustic signals are vibration signals, and their characteristics are similar to speech signals. The auditory feature extraction method in speech recognition can also be applied to the underwater environment. For underwater communication, we simulate two models designed for underwater target detection and localization. One is the deterministic model, which is considered as basic model; the other is to combine the deterministic model and statistic model, which is called combined model. The geometric channel model facilitates the generation of the database for different geometric settings. The database is generated by adjusting the parameters of the underwater environment. The classifier adopts a convolutional neural network (CNN). The input to the CNN is the feature maps after feature extraction. We choose continuous wavelet transform (CWT) and short-time Fourier transform (STFT) for comparison. Experiments show the effectiveness of the system architecture and superiority of the proposed algorithm in underwater signal classification and target localization.
KW - CNN
KW - Gammatone Frequency Cepstral Coefficient (GFCC)
KW - Mel Frequency Cepstrum Coefficient (MFCC)
KW - Underwater communication
UR - http://www.scopus.com/inward/record.url?scp=85136970541&partnerID=8YFLogxK
U2 - 10.1109/CTISC54888.2022.9849785
DO - 10.1109/CTISC54888.2022.9849785
M3 - Conference Proceeding
AN - SCOPUS:85136970541
T3 - CTISC 2022 - 2022 4th International Conference on Advances in Computer Technology, Information Science and Communications
BT - CTISC 2022 - 2022 4th International Conference on Advances in Computer Technology, Information Science and Communications
A2 - Gerogianni, Vassilis C.
A2 - Yue, Yong
A2 - Kamareddine, Fairouz
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th International Conference on Advances in Computer Technology, Information Science and Communications, CTISC 2022
Y2 - 22 April 2022 through 24 April 2022
ER -