Noise Reduction Network: Implementing an Efficient Adaptive Residual Autoencoder with Spatial-Temporal Attention Mechanism for Reducing Noise in Underwater Acoustic Signals

In this research work, a novel deep learning framework is suggested to recognize highly connected acoustic signals from a noisy mixture for enhancing the reliability and clarity of underwater communication. At first, essential Underwater Acoustic Signals (UAS) are collected from the standard resources. Then, the gathered signals are given to the noise reduction phase for enhancing the signal clarity. Here, noises presented in the UAS are reduced using the developed Adaptive Residual Autoencoder with Spatial-Temporal Attention (AResASTA) model. Further, the parameters in the proposed AResAe-STA are tuned using the Renovated Random Attribute-based Golf Optimization Algorithm (RRA-GOA). It can effectively handle large volumes of data promptly to generate optimal solutions. This allows the clear detection of UAS, leading to improved signal recognition and classification. At last, the noise reduction outcomes are attained from the developed AResAe-STA mechanism. Further, various performance measures are used to optimally validate the system performance, and it is compared with several existing methods to observe its effectiveness. The proposed method’s overall performance is maximized by 7.49% of DPTN, 5.47% of DPRN, 3.21% of BSS, and 1.05% of AResASTA in terms of the PCC measure.