Ocean noise has a negative impact on the acoustic recordings of odontocetes' echolocation clicks. In this study, deep convolutional autoencoders (DCAEs) are presented to denoise the echolocation clicks of the finless porpoise (Neophocaena phocaenoides sunameri). A DCAE consists of an encoder network and a decoder network. The encoder network is composed of convolutional layers and fully connected layers, whereas the decoder network consists of fully connected layers and transposed convolutional layers. The training scheme of the denoising autoencoder was applied to learn the DCAE parameters. In addition, transfer learning was employed to address the difficulty in collecting a large number of echolocation clicks that are free of ambient sea noise. Gabor functions were used to generate simulated clicks to pretrain the DCAEs; subsequently, the parameters of the DCAEs were fine-tuned using the echolocation clicks of the finless porpoise. The experimental results showed that a DCAE pretrained with simulated clicks achieved better denoising results than a DCAE trained only with echolocation clicks. Moreover, deep fully convolutional autoencoders, which are special DCAEs that do not contain fully connected layers, generally achieved better performance than the DCAEs that contain fully connected layers.

中文翻译：

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使用深度卷积自动编码器对江豚（Neophocaena phocaenoides sunameri）的回声定位点击进行降噪的迁移学习

海洋噪音对齿科动物回声定位点击的声学记录有负面影响。在这项研究中，提出了深度卷积自编码器 (DCAE) 来对江豚 ( Neophocaena phocaenoides sunameri ) 的回声定位咔嗒声进行降噪。）。DCAE 由编码器网络和解码器网络组成。编码器网络由卷积层和全连接层组成，而解码器网络由全连接层和转置卷积层组成。应用去噪自编码器的训练方案来学习 DCAE 参数。此外，迁移学习被用来解决收集大量没有环境海噪声的回声定位点击的困难。Gabor 函数用于生成模拟点击以预训练 DCAE；随后，使用江豚的回声定位咔嗒声微调 DCAE 的参数。实验结果表明，与仅使用回声定位点击训练的 DCAE 相比，使用模拟点击预训练的 DCAE 获得了更好的去噪效果。而且，