论文标题
时频面膜意识到双向LSTM：水下声信号分离的深度学习方法
Time-Frequency Mask Aware Bi-directional LSTM: A Deep Learning Approach for Underwater Acoustic Signal Separation
论文作者
论文摘要
水下声信号分离是水下通信的关键技术。现有方法主要基于模型，无法准确表征实用的水下声学通信环境。它们仅适用于二元信号分离，但无法处理多元信号分离。另一方面，复发性神经网络（RNN）在提取时间序列的特征方面具有强大的能力。在本文的启发下，我们提出了一种使用深度学习技术进行水下声学信号分离的数据驱动方法。我们使用双向长期记忆（BI-LSTM）来探索时间频率（T-F）掩码的特征，并提出一个用于信号分离的t-f掩膜识别的BI-LSTM。利用T-F图像的稀疏性，设计的BI-LSTM网络能够提取分离的判别特征，从而进一步提高了分离性能。特别是，该方法介绍了现有方法的局限性，不仅在多元分离中取得了良好的结果，而且在与40dB高斯噪声信号混合时，也可以有效地分离信号。实验结果表明，此方法可以达到$ 97 \％$保证比率（PSR），并且在高噪声条件下，多元信号分离的平均相似性系数在0.8以上稳定。
The underwater acoustic signals separation is a key technique for the underwater communications. The existing methods are mostly model-based, and could not accurately characterise the practical underwater acoustic communication environment. They are only suitable for binary signal separation, but cannot handle multivariate signal separation. On the other hand, the recurrent neural network (RNN) shows powerful capability in extracting the features of the temporal sequences. Inspired by this, in this paper, we present a data-driven approach for underwater acoustic signals separation using deep learning technology. We use the Bi-directional Long Short-Term Memory (Bi-LSTM) to explore the features of 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, not only achieves good results in multivariate separation, but also effectively separates signals when mixed with 40dB 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.
