多类CNN方法用于自动分类海豚声调
Francesco Di Nardo1, Rocco De Marco2, Daniel Li Veli2
1Dipartimento di Ingegneria dell'informazione, Università Politecnica delle Marche, 60131 Ancona, Italy.
Sensors (Basel, Switzerland)
|April 26, 2025
概括
本研究引入了一种使用卷积神经网络 (CNN) 来从被动声学监测 (PAM) 数据中分类海豚声音的新方法. 美国有线电视新闻网 (CNN) 实现了高精度,提高了我们监测海豚种群和减少人与野生动物冲突的能力.
科学领域:
- 海洋生物学 海洋生物学
- 生物声学是一种生物声学.
- 人工智能的人工智能
背景情况:
- 被动声学监测 (PAM) 对于跟踪海豚等海洋哺乳动物至关重要.
- 了解海豚发声是评估生态系统健康和人类影响的关键.
- 目前分析海豚声音的方法可能是劳动密集型的,需要专业知识.
研究的目的:
- 开发和评估一种新的方法来使用卷积神经网络 (CNN) 来分类常见的瓶鼻海豚发声.
- 提高分析海豚监测水下声学记录的准确性和效率.
- 通过更好的声学监测,加强物种保护工作,减轻人类与渔业之间的冲突.
主要方法:
- 利用了近10,000个谱图的数据集,这些谱图来自于普通鼻海豚 (Tursiops truncatus) 的记录.
- 将边缘检测过器应用于光谱图,以减少噪声和改善特征提取.
- 训练并测试了CNN模型,使用10倍的交叉验证程序进行稳健的性能评估.
主要成果:
- CNN模型的整体平均准确率为95.2%,F1得分为87.8%.
- 记录了高类特异性准确度:哨声 (97.9%),回声定位点击 (94.5%),食声 (94.0%),以及爆发脉冲声音 (92.3%).
- 笛子的F1得分超过95%,其他的发音类型保持得分高于80%.
结论:
- 开发的基于CNN的方法提供了一个非常准确和有前途的工具,用于从PAM数据中分类海豚声调.
- 这种方法显著提高了用于海洋哺乳动物研究的被动声学监测的能力.
- 这些发现支持改进海豚保护战略和减轻海豚与渔业之间的冲突.
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