SeqFusionNet:用于序列感知和全球集成声学表示的混合模型
Tianhao Wu1, Wei Ma1, Ouyuping Gu1
1College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya'an, Sichuan Province, China.
PloS one
|September 8, 2025
概括
使用变压器和MLP,SeqFusionNet准确地对动物的发声进行了分类,从而加强了生物多样性监测. 这种生物声学框架支持野生动物保护和生态研究.
科学领域:
- 生物声学是一种生物声学.
- 机器学习 机器学习
- 野生动物保护 野生动物保护
背景情况:
- 动物的发声对于沟通,物种保护和生物多样性跟踪至关重要.
- 传统的视觉监控方法在距离和环境条件方面存在局限性.
- 基于呼叫的监测为研究动物提供了更有效和更直接的方法.
研究的目的:
- 引入SeqFusionNet,一个先进的动物声音分类模型.
- 为了评估模型在各种声学数据集中的性能.
- 为了证明模型的稳定性和对生物声学监测的概括能力.
主要方法:
- 开发了SeqFusionNet,将变压器集成为序列编码和MLP用于全球感知.
- 编制并使用了四个声学数据集:猪,鸟,城市声音和海洋哺乳动物.
- 进行了广泛的实验,以评估跨物种适用性和识别准确性.
主要成果:
- 在分类各种动物呼叫时,SeqFusionNet实现了高精度:猪的95.00%,鸟类的95.24%,海洋哺乳动物的97.50%,城市声音的94.39%.
- 与现有方法相比,该模型表现出优越的性能.
- SeqFusionNet在不同物种和数据集中显示出强大的稳定性和概括性.
结论:
- SeqFusionNet是自动化生物声学监测的有效框架.
- 该模型支持野生动物保护,生态研究和环境声音分析.
- 这项研究提供了一个可扩展和高效的解决方案来分析动物的发声.
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