An efficient noise reduction technique in underwater acoustic signals using enhanced optimization-based residual recurrent neural network with novel loss function

Abstract

An important process for underwater acoustic signal is noise reduction. In ocean exploration and the military, the minimization of noise in the underwater environment is essential to provide a significant impact in our society. While considering the different acoustic channels and complexity of marine environment, the noise reduction process in acoustic signals is always difficult. Owing to these complexities, an advanced noise reduction mechanism for underwater acoustic signal denoising is performed using adaptive deep learning method is developed. The proposed model involves finding and analyzing the noises present in the underwater acoustic signal, which is helpful for underwater target detection, recognition and acoustic communication quality. Here, a Advanced Recurrent Neural Network with Novel Loss Function (ARRNN-NLF) is implemented for reducing noises in underwater acoustic signals. Hence, the input signal is given to the reduction process where the ARRNN-NLF network is utilized for densification. The high-order nonlinear features from the original signal are extracted and converted into subvectors with fixed lengths based on temporal dimension. Finally, the denoised signal is obtained from the developed ARRNN with a minimum loss between the predicted and target output. Here, the parameters from ARRNN-NLF are optimized by the Enhanced Osprey Optimization Algorithm (EOOA) for enhancing the denoising model. The resultant results are evaluated by diverse conventional denoising models to prove efficiency.