For classification of underwater acoustic signals, we propose a novel sparse anisotropic chirplet transform (ACT) to reveal fine time–frequency structures. The signal features in the form of a time–frequency map are fed into a deep convolutional neural network, referred to as a time–frequency feature network (TFFNet), which brings flexibility to signal classification. The TFFNet is based on a novel efficient feature pyramid enhancing feature (EFP) maps by aggregating the context information at different scales. To remove the gridding artifacts on enhanced feature maps, a form of aggregating transformation, a forward feature fusion, is utilized to merge the forward feature maps. Main contributions of this work are a novel sparse ACT, a TFFNet classifier, and an EFP with forward feature fusion. Experimental results demonstrate that the sparse ACT provides a high-resolution time–frequency representation of underwater signals and the TFFNet improves the classification performance compared to known networks and two machine learning methods (random forest and support vector machine with radial basis function kernel) on two real data sets, an underwater acoustic communication signal data set and whale sounds data set.

中文翻译：

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基于稀疏时频表示和深度学习的水下声信号分类

对于水声信号的分类，我们提出了一种新颖的稀疏各向异性 chirplet 变换 (ACT) 来揭示精细的时频结构。将时频图形式的信号特征输入深度卷积神经网络，称为时频特征网络（TFFNet），为信号分类带来了灵活性。TFFNet 基于一种新颖的高效特征金字塔增强特征 (EFP) 映射，通过聚合不同尺度的上下文信息。为了去除增强特征图上的网格伪影，一种聚合变换形式，前向特征融合，被用来合并前向特征图。这项工作的主要贡献是新颖的稀疏 ACT、TFFNet 分类器和具有前向特征融合的 EFP。