基于可学习细粒度小波变换的自适应船舶辐射噪声识别
Adaptive ship-radiated noise recognition with learnable fine-grained wavelet transform
AI总结：
针对复杂水下环境中船舶辐射噪声识别困难的问题，提出自适应广义识别系统AGNet，通过引入可学习细粒度小波变换与并行卷积注意力模块，有效提取水声特征并在多个数据集上取得优于基线方法的鲁棒性能。
摘要：
分析海洋声学环境是一项棘手的任务。背景噪声和多变的信道传输环境使得实现准确的船舶辐射噪声识别变得复杂。现有识别系统在应对多变的水下环境时能力薄弱，导致在实际应用中表现不佳。为了使识别系统在各种水下环境中保持鲁棒性，本研究提出了一种自适应广义识别系统——AGNet（Adaptive Generalized Network，自适应广义网络）。通过将固定的小波参数转化为细粒度的可学习参数，AGNet能够学习不同频率下水声的特征。其灵活且细粒度的设计有助于捕获更多的背景声学信息（例如背景噪声、水下传输信道）。为了利用小波频谱图中的隐含信息，AGNet采用带有并行卷积注意力模块的卷积神经网络作为分类器。实验表明，我们的AGNet在多个水声数据集上优于所有基线方法，并且AGNet能从迁移学习中获益更多。此外，AGNet在面对各种干扰因素时表现出鲁棒的性能。
英文摘要：
Analyzing the ocean acoustic environment is a tricky task. Background noise and variable channel transmission environment make it complicated to implement accurate ship-radiated noise recognition. Existing recognition systems are weak in addressing the variable underwater environment, thus leading to disappointing performance in practical application. In order to keep the recognition system robust in various underwater environments, this work proposes an adaptive generalized recognition system - AGNet (Adaptive Generalized Network). By converting fixed wavelet parameters into fine-grained learnable parameters, AGNet learns the characteristics of underwater sound at different frequencies. Its flexible and fine-grained design is conducive to capturing more background acoustic information (e.g., background noise, underwater transmission channel). To utilize the implicit information in wavelet spectrograms, AGNet adopts the convolutional neural network with parallel convolution attention modules as the classifier. Experiments reveal that our AGNet outperforms all baseline methods on several underwater acoustic datasets, and AGNet could benefit more from transfer learning. Moreover, AGNet shows robust performance against various interference factors.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
