Research on Acoustic Three-User Communication Based on Single Vector Sensor

Wang, Dayu; Zhao, Anbang; Jiang, Hui; Li, Xu; Hao, Xiuqiang; Chen, Yang

doi:10.1109/wicom.2009.5305566

Cited by 1 publication

(1 citation statement)

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“…Therefore, a method of reducing the impact of noise is the most critical part of underwater communication. Source separation technology is a good method of reducing noise [ 5 , 6 , 7 , 8 ] that has attracted a great deal of attention from researchers in both academia and industry. Among these source separation methods, non-negative matrix factorization (NMF) [ 9 ] is one method that can be used to separate source signals.…”

Section: Introductionmentioning

confidence: 99%

Time–Frequency Mask-Aware Bidirectional LSTM: A Deep Learning Approach for Underwater Acoustic Signal Separation

Chen

Liu

Xie

et al. 2022

Sensors

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Underwater acoustic signal separation is a key technique for underwater communications. The existing methods are mostly model-based, and cannot accurately characterize the practical underwater acoustic communication environment. They are only suitable for binary signal separation and cannot handle multivariate signal separation. However, recurrent neural networks (RNNs) show a powerful ability to extract the features of temporal sequences. Inspired by this, in this paper, we present a data-driven approach for underwater acoustic signal separation using deep learning technology. We use a bidirectional long short-term memory (Bi-LSTM) approach to explore the features of a time–frequency (T-F) mask, and propose a T-F-mask-aware Bi-LSTM for signal separation. Taking advantage of the sparseness of the T-F image, the designed Bi-LSTM network is able to extract the discriminative features for separation, which further improves the separation performance. In particular, this method breaks through the limitations of the existing methods and not only achieves good results in multivariate separation but also effectively separates signals when they are mixed with 40 dB Gaussian noise signals. The experimental results show that this method can achieve a 97% guarantee ratio (PSR), and the average similarity coefficient of the multivariate signal separation is stable above 0.8 under high noise conditions. It should be noted that our model can only handle known signals such as test signals for calibration.

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