Underwater acoustic source separation with deep Bi-LSTM networks

Zhang, Wen; Li, Xiaoyong; Zhou, Aolong; Ren, Kaijun; Song, Junqiang

doi:10.1109/icicsp54369.2021.9611911

Cited by 4 publications

(2 citation statements)

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“…Deep learning methods built on recurrent neural networks are essential to modern signal processing. They are often used as a vital module for mask separation in the field of underwater acoustic signal noise reduction [23] [24] [25]. However, the inherent sequential processing sequence mode of the Recurrent Neural Network (RNN) prevents the model from parallelizing the computation during training.…”

Section: Introductionmentioning

confidence: 99%

Method of Underwater Acoustic Signal Denoising Based on Dual-Path Transformer Network

2024

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The presence of natural ambient noise interferes with the system for locating and identifying underwater targets. This paper suggests that a Dual-Path Transformation Network(DPTN) reduces ambient noise in underwater acoustic signals. First, the input acoustic signals' higher-order non-linear features are extracted using a multi-scale convolutional encoder neural network. Second, sub-vectors with the same length are created according to the time dimension from the higher-order non-linear features. The subvectors are stitched together to form a three-dimensional tensor. Third, a neural network transformer based on the feed-forward network is constructed. Further, to capture long-term series features and separate the target signal from the noisy signals, the three-dimensional tensor is used as the input of the transformerbased masking network. Finally, overlap-add and transpose are used to obtain discernible target signals. The experimental results verify the effectiveness of the proposed underwater acoustic signal denoising algorithm and demonstrate that the proposed DPRN method can obtain higher output signal-to-noise ratio (SNR) and the scale-invariant signal-to-noise ratio (SI-SNR) compared with the other classical algorithms.

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Section: Introductionmentioning

confidence: 99%

Method of Underwater Acoustic Signal Denoising Based on Dual-Path Transformer Network

2024

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“…Artificial neural networks are also frequently used in similar situations. The method of time-frequency domain source separation is utilized in [34], by using deep bidirectional long short-term memory (Bi-LSTM) recurrent neural networks (RNN) to estimate the ideal amplitude mask target. In addition, [35] uses a Bi-LSTM approach to explore the features of a time-frequency (T-F) mask and applies it for signal separation.…”

Section: Introductionmentioning

confidence: 99%

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

Song,

Feng,

Wang

et al. 2024

Remote Sensing

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Ship-radiated noise is the main basis for ship detection in underwater acoustic environments. Due to the increasing human activity in the ocean, the captured ship noise is usually mixed with or covered by other signals or noise. On the other hand, due to the softening effect of bubbles in the water generated by ships, ship noise undergoes non-negligible nonlinear distortion. To mitigate the nonlinear distortion and separate the target ship noise, blind source separation (BSS) becomes a promising solution. However, underwater acoustic nonlinear models are seldom used in research for nonlinear BSS. This paper is based on the hypothesis that the recovery and separation accuracy can be improved by considering this nonlinear effect in the underwater environment. The purpose of this research is to explore and discover a method with the above advantages. In this paper, a model is used in underwater BSS to describe the nonlinear impact of the softening effect of bubbles on ship noise. To separate the target ship-radiated noise from the nonlinear mixtures, an end-to-end network combining an attention mechanism and bidirectional long short-term memory (Bi-LSTM) recurrent neural network is proposed. Ship noise from the database ShipsEar and line spectrum signals are used in the simulation. The simulation results show that, compared with several recent neural networks used for linear and nonlinear BSS, the proposed scheme has an advantage in terms of the mean square error, correlation coefficient and signal-to-distortion ratio.

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