基于音频信号的蜜蜂识别非侵入性系统和自动编码器的最大概率分类
Urszula Libal1, Pawel Biernacki1
1Department of Acoustics, Multimedia and Signal Processing, Wroclaw University of Science and Technology, 50-370 Wroclaw, Poland.
Sensors (Basel, Switzerland)
|August 29, 2024
概括
本研究介绍了一种人工智能驱动的方法,通过分析飞行声音来识别蜜蜂类型. 该系统准确地区分工蜂和无人机蜂,使用用于养蜂应用的音频分析.
科学领域:
- 生物声学是一种生物声学.
- 人工智能在农业中的应用
- 昆虫学 昆虫学是一门学科.
背景情况:
- 人工智能 (AI) 和物联网 (IoT) 的整合正在彻底改变蜂巢监控.
- 区分工蜂和无人机蜂对于蜂巢管理和了解蜂群健康至关重要.
- 目前用于蜜蜂类型识别的方法可能是劳动密集型或缺乏自动化.
研究的目的:
- 开发一种自动识别蜜蜂类型 (工人与无人机) 基于其飞行声音的自动方法.
- 评估各种音频信号预处理和表示技术,以准确分类蜜蜂.
- 实施和验证一个自编码神经网络,使用声学特征来区分蜜蜂类型.
主要方法:
- 从蜂巢入口附近的工人和无人机蜜蜂收集和分析了音频信号.
- 信号预处理技术和频域表示的比较:Mel-Frequency Cepstral Coefficients (MFCCs),Gammatone Cepstral Coefficients (GTCCs),MUSIC,以及伯格的PSD估计.
- 利用自编码神经网络,根据信号表示重建错误对蜜蜂进行分类,采用新的值策略.
主要成果:
- 已经证明,仅靠音频信号分析就足以区分无人机和工蜂.
- 为此声学分类任务确定了有效的信号预处理和表示方法.
- 实现了高水平的检测准确性,验证了拟议的基于自动编码器的方法.
结论:
- 拟议的方法允许使用飞行声学自动和准确地区分蜜蜂类型.
- 这项技术可以成为养蜂人高效自动化系统的基础.
- 声学监测为蜂群管理提供了一个非侵入性和可扩展的解决方案.
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