期刊
ECOLOGICAL INFORMATICS
卷 62, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.ecoinf.2021.101277
关键词
Marine mammal sounds; AlexNet; Transfer learning; Feature visualization; Strongest activations
类别
资金
- China Scholarship Council [201806330047, 201808370191]
- Acoustical Society of America International Student Grant (2018)
In this study, AlexNet with transfer learning was used to automatically detect and classify killer whale, long-finned pilot whale, and harp seal sounds with high accuracy. The challenging dataset evaluation confirmed the excellent performance of the proposed method in real-world scenarios.
In this study, AlexNet with transfer learning was employed to automatically detect and classify the sounds of killer whales, long-finned pilot whales, and harp seals with widely overlapping living areas. Transfer learning was used to overcome the overfitting problem of deep network as the training samples was insufficient. A challenging dataset containing both target (the three marine mammal sounds) and non-target (ambient noise including ship noise, pulse interference, and man-made sounds, etc) sounds collected from different recording times, locations and devices was used to examine the performance of the proposed method, and the sounds used in the test dataset were completely independent of the training dataset. The overall accuracy of the trained detection and classification models reached 99.96% and 97.42% respectively. Importantly, each trained model took only 1.3 ms to detect or classify a single image. Furthermore, feature visualizations and strongest activations demonstrated that the proposed method learns the true differences between different marine mammal sounds rather than differences between different recording environments and devices. Therefore, all results show that the proposed method has excellent performance and great potential for practical application.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据