Abstract：
This study aims to detect the bioacoustics signal in the underwater soundscape, specifically those produced by snapping shrimp, using adaptive iterative transfer learning. The proposed network is initially trained with pre-classified snapping shrimp sounds and Gaussian noise, then applied to classify and remove snapping-free noise from field data. This separated ambient noise is subsequently used for transfer learning. This process was iterated to distinguish more effectively between ambient noise and snapping shrimp sounds characteristics, resulting in improved classification. Through iterative transfer learning, significant improvements in precision and recall were observed. The application to field data confirmed that the trained network could detect signals that were difficult to identify using existing threshold classification methods. Furthermore, it was found that the rate of false detection decreased, and detection probability improved with each stage. This research demonstrates that incorporating the noise characteristics of field data into the trained network via iterative transfer learning can generate more realistic training data. The proposed network can successfully detect signals that are challenging to identify using existing threshold classification methods.
摘要：
本研究旨在检测水下声景中的生物声学信号,特别是那些通过捕虾生产的，使用自适应迭代迁移学习。所提出的网络最初使用预分类的捕捉虾声音和高斯噪声进行训练，然后应用于分类和去除现场数据中的无快照噪声。该分离的环境噪声随后用于转移学习。此过程进行了迭代，以更有效地区分环境噪声和响虾声音特征，从而改进分类。通过迭代迁移学习，观察到准确率和召回率有显著提高。对现场数据的应用证实，经过训练的网络可以检测到使用现有阈值分类方法难以识别的信号。此外，发现误检率下降，检测概率随着每个阶段的提高而提高。这项研究表明，通过迭代迁移学习将现场数据的噪声特征纳入训练网络可以生成更真实的训练数据。所提出的网络可以成功地检测到使用现有阈值分类方法难以识别的信号。
