Narrow Results

The use of adaptive filters to alleviate the degradation caused by wind driven ambient noise in shallow water is considered in this paper. Since, underwater acoustic signals are greatly affected by the ocean interference and ambient noise disturbances when propagating through underwater channels, an effective adaptive filtering system is necessary for denoising the signal which are degraded by noise. Least mean square (LMS), normalized LMS (NLMS), Modified New LMS (MNLMS) and Kalman LMS (KLMS) based adaptive algorithms are analyzed in terms of their performance with the aid of performance measure characteristics such as signal to noise ratio (SNR) and mean square error (MSE). The MNLMS is developed by calculating an optimum learning parameter that best suits for the acoustic signal used. The analysis is carried out for a range of 100 Hz to 10 KHz source signals and the algorithm proves that any ambient noise signals against the source signal in this range can be eliminated and the source signal can be reconstructed. Our simulation results show that KLMS and MNLMS algorithms achieve remarkable performance even in the very low SNR region as compared to LMS and NMLS algorithms. Moreover, it is observed that the output convergence is also very fast for MNLMS and KLMS.

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.