Separation of Nonlinearly Mixed Sources Using End-to-End Deep Neural Networks

Underwater Acoustic Nonlinear Blind Ship Noise Separation Using Recurrent Attention Neural Networks

“…However, the attention mechanism performs weakly in acquiring the local information. Bi-LSTM has been proven to have excellent performance in nonlinear BSS [42] and it is successful in extracting the local information but weak in acquiring global information; thus, it could work well to complement the attention mechanism. As a result, a Bi-LSTM layer is then utilized in the network, which is followed by a drop-out layer to avoid overfitting.…”

“…The referred networks should be representative of different types. The candidate in [42], combining Bi-LSTM, LSTM and a drop-out layer, shows good performance in end-to-end nonlinear blind source separation, denoted as RNN in the description hereafter. The classical Transformer network [43] is another good candidate to test the performance and is denoted as Transformer.…”

“…Based on this noise and nonlinear model, a dataset is generated and used in the neural network training, validation and testing. The simulation results indicate that the proposed network performs better in terms of separation accuracy than networks purely based on RNNs [42], the classical Transformer [43] or a recently published end-to-end BSS U-net [47]. The advantages of the proposed scheme are its lower mean square error (MSE), higher correlation coefficient and higher signal-to-distortion ratio (SDR).…”

“…Furthermore, the useful signals from the complex nonlinear mixtures are separated by applying a three-layer deep recurrent neural network to achieve single-channel BSS in [41]. Additionally, nonlinearity can be fairly approximated using a Taylor series and an end-to-end RNN that learns a nonlinear BSS system [42]. Even so, the performance can still be further improved for nonlinear BSS.…”