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Anqi Jin and Xiangyang Zeng
Long-range underwater targets must be accurately and quickly identified for both defense and civil purposes. However, the performance of an underwater acoustic target recognition (UATR) system can be significantly affected by factors such as lack of data...
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Sirui Shen, Daobin Zhang, Shuchao Li, Pengcheng Dong, Qing Liu, Xiaoyu Li and Zequn Zhang
Heterogeneous graph neural networks (HGNNs) deliver the powerful capability to model many complex systems in real-world scenarios by embedding rich structural and semantic information of a heterogeneous graph into low-dimensional representations. However...
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Shiqi Deng, Zhiyu Sun, Ruiyan Zhuang and Jun Gong
Anomaly detection has a wide range of applications and is especially important in industrial quality inspection. Currently, many top-performing anomaly detection models rely on feature embedding-based methods. However, these methods do not perform well o...
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Fang Ji, Junshuai Ni, Guonan Li, Liming Liu and Yuyang Wang
Underwater acoustic target recognition methods based on time-frequency analysis have shortcomings, such as missing information on target characteristics and having a large computation volume, which leads to difficulties in improving the accuracy and imme...
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Eran Shachar, Israel Cohen and Baruch Berdugo
This paper presents an echo suppression system that combines a linear acoustic echo canceller (AEC) with a deep complex convolutional recurrent network (DCCRN) for residual echo suppression. The filter taps of the AEC are adjusted in subbands by using th...
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