TY - GEN
T1 - Underwater acoustic target recognition method based on CT and residual CNN
AU - Yao, Qihai
AU - Wang, Yong
AU - Yang, Yixin
N1 - Publisher Copyright:
© 2024 Copyright held by the owner/author(s).
PY - 2025/1/18
Y1 - 2025/1/18
N2 - This paper presents an underwater acoustic target recognition method using Chirplet transform (CT), with the residual convolutional neural network (CNN) as the classifier. The method involves decomposing signal by the empirical mode decomposition (EMD), denoising based on principal component analysis (PCA) algorithm, extracting CT spectrum features of underwater acoustic targets, and using a ResNet18 model for recognition. The results of different models, including support vector machine, ordinary CNN, VGG19, and ResNet18 are compared. The results show that the denoise method based on PCA can effectively reduce noise and redundancy. Compared to other features, the recognition accuracy of CT spectrum is better. The CT-ResNet18 achieves the best recognition performance. While this method is used in ship recognition, it can be applied to other target voice recognition, such as marine mammals.
AB - This paper presents an underwater acoustic target recognition method using Chirplet transform (CT), with the residual convolutional neural network (CNN) as the classifier. The method involves decomposing signal by the empirical mode decomposition (EMD), denoising based on principal component analysis (PCA) algorithm, extracting CT spectrum features of underwater acoustic targets, and using a ResNet18 model for recognition. The results of different models, including support vector machine, ordinary CNN, VGG19, and ResNet18 are compared. The results show that the denoise method based on PCA can effectively reduce noise and redundancy. Compared to other features, the recognition accuracy of CT spectrum is better. The CT-ResNet18 achieves the best recognition performance. While this method is used in ship recognition, it can be applied to other target voice recognition, such as marine mammals.
KW - acoustic target recognition
KW - Chirplet transform
KW - CNN
KW - machine learning
UR - http://www.scopus.com/inward/record.url?scp=85218417178&partnerID=8YFLogxK
U2 - 10.1145/3704391.3704403
DO - 10.1145/3704391.3704403
M3 - 会议稿件
AN - SCOPUS:85218417178
T3 - ITCC 2024 - 2024 6th International Conference on Information Technology and Computer Communications, ITCC 2024
SP - 81
EP - 87
BT - ITCC 2024 - 2024 6th International Conference on Information Technology and Computer Communications, ITCC 2024
PB - Association for Computing Machinery, Inc
T2 - 6th International Conference on Information Technology and Computer Communications, ITCC 2024
Y2 - 25 October 2024 through 27 October 2024
ER -
