Huang Zhaoqiong, Xu Ji, Gong Zaixiao, Wang Haibin, Yan Yonghong
Key Laboratory of Speech Acoustics and Content Understanding, Institute of Acoustics, Chinese Academy of Sciences, No. 21 North 4th Ring Road, Haidian District, Beijing 100190, People's Republic of China.
State Key Laboratory of Acoustics, Institute of Acoustics, Chinese Academy of Sciences, No. 21 North 4th Ring Road, Haidian District, Beijing 100190, People's Republic of China.
J Acoust Soc Am. 2018 May;143(5):2922. doi: 10.1121/1.5036725.
Deep neural networks (DNNs) are advantageous for representing complex nonlinear relationships. This paper applies DNNs to source localization in a shallow water environment. Two methods are proposed to estimate the range and depth of a broadband source through different neural network architectures. The first adopts the classical two-stage scheme, in which feature extraction and DNN analysis are independent steps. The eigenvectors associated with the modal signal space are extracted as the input feature. Then, the time delay neural network is exploited to model the long term feature representation and constructs the regression model. The second concerns a convolutional neural network-feed-forward neural network (CNN-FNN) architecture, which trains the network directly by taking the raw multi-channel waveforms as input. The CNNs are expected to perform spatial filtering for multi-channel signals, in an operation analogous to time domain filters. The outputs of CNNs are summed as the input to FNN. Several experiments are conducted on the simulated and experimental data to evaluate the performance of the proposed methods. The results demonstrate that DNNs are effective for source localization in complex and varied water environments, especially when there is little precise environmental information.
深度神经网络（DNN）在表示复杂非线性关系方面具有优势。本文将DNN应用于浅水环境中的源定位。提出了两种方法，通过不同的神经网络架构来估计宽带源的距离和深度。第一种方法采用经典的两阶段方案，其中特征提取和DNN分析是独立的步骤。与模态信号空间相关的特征向量被提取作为输入特征。然后，利用时延神经网络对长期特征表示进行建模并构建回归模型。第二种方法涉及卷积神经网络-前馈神经网络（CNN-FNN）架构，它直接以原始多通道波形作为输入来训练网络。预计卷积神经网络对多通道信号执行空间滤波，其操作类似于时域滤波器。卷积神经网络的输出被求和作为前馈神经网络的输入。对模拟数据和实验数据进行了多项实验，以评估所提方法的性能。结果表明，DNN对于复杂多变水环境中的源定位是有效的，尤其是在精确环境信息很少的情况下。
