Blind Source Separation and Denoising of Underwater Acoustic Signals

Due to the addition of new underwater vessels and other natural noise contributors, the underwater environment is becoming congested and noisy. Undersea monitoring sonobuoys receive multiple mixed acoustic signals from different vessels that need to be separated and identified in the presence of underwater noise (UWN). It is extremely challenging to separate highly correlated acoustic signals from a noisy mixture without prior knowledge of mixing process and propagation channel. Also, in many cases, the separated signals from the noisy mixture doesn't accurately describe the correct signal. This study proposes a novel multi-stage method to separate underwater acoustic source signals from noisy mixture with suppression in noise. The first stage applies multivariate blind source separation (BSS) technique known as non-negative matrix factorization (NMF) that extracts the source signals from the noisy signal mixture. In the second stage, Minimum mean square error (MMSE) estimator is used to reduce noise in separated/reconstructed source signals by minimizing the mean square error (MSE) between reconstructed acoustic signal and original clean signal, enhancing signal reconstruction quality. The results of this study indicate the effectiveness of the proposed method in terms of MSE, signal-to-distortion ratio (SDR) and cepstral distance (CD) and compare it with existing techniques. Based on simulation outcomes our proposed method demonstrates superior separation performance by reducing MSE upto 47% and improving SDR of reconstructed acoustic signals upto 28% compared to existing solutions.