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Echo

Echo

The human ear cannot distinguish between two sources of sound if they happen to reach within a specific time interval, typically 0.1 seconds apart. More than this, and they are perceived as separate sources.
Imagine the sound is reflected back to the ears. Assuming that the source is very close to the human, the difference between hearing the two sounds—the emitted sound and the reflected sound—may be more than the minimum time for perceiving distinct sounds. If this is the case,...

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Bat2Web:使用音频传感器数据对蝙蝠物种回声定位信号进行实时分类的框架.

Taslim Mahbub¹, Azadan Bhagwagar¹, Priyanka Chand¹

¹Department of Computer Science and Engineering, American University of Sharjah, Sharjah 26666, United Arab Emirates.

Sensors (Basel, Switzerland)

|May 11, 2024

概括

现在可以使用微小的神经网络和物联网传感器来自动识别蝙蝠物种. 该系统从回声定位呼叫中准确识别蝙蝠,有助于保护和生态监测.

关键词:

在谷歌的珊瑚.这就是为什么物联网是物联网物联网.洛拉旺人洛拉旺人这是一个NVIDIA Jetson.蝙蝠回声定位分析蝙蝠物种分类蝙蝠物种分类生物声学是一种生物声学.机器学习是机器学习.

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相关实验视频

Last Updated: Jun 26, 2025

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Published on: October 15, 2014

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科学领域:

生态与保护生物学生态与保护生物学
生物声学是一种生物声学.
人工智能的人工智能

背景情况:

蝙蝠对生态系统健康至关重要,它们的识别对生态研究和保护至关重要.
传统的蝙蝠物种识别依赖于分析回声定位呼叫,这是一个复杂和耗时的过程,需要专家分析.
需要自动识别方法来克服手动蝙蝠呼叫分析的挑战.

研究的目的:

设计和实施用于蝙蝠物种识别的自动声学监测系统.
利用物联网 (IoT) 技术和神经网络进行高效的蝙蝠监控.
开发一个紧的神经网络模型,能够从回声定位数据准确识别蝙蝠物种.

主要方法:

开发一个紧的卷积神经网络 (CNN) 模型来分析蝙蝠回声定位信号.
将CNN模型与物联网设备集成,实现实时,低功耗的声学监控.
在NVIDIA Jetson Nano和Google Coral等边缘设备上部署和评估系统的性能.

主要成果:

紧的CNN模型在蝙蝠物种识别方面取得了高性能,F1得分为0.9578,准确率为97.5%.
开发的系统证明了使用边缘计算对蝙蝠进行自动声学监测的可行性.
在各种边缘设备上成功部署和评估证实了该系统的实际应用性.

结论:

使用紧的神经网络和物联网的自动蝙蝠物种识别是有效和准确的.
这项技术为生态监测和保护工作提供了可扩展的解决方案.
该系统为研究人员提供了一种有价值的工具,可以有效地研究蝙蝠种群及其息地.