使用Multi-HCNet在音频中检测电网频率
Yujin Li1, Tianliang Lu1, Shufan Peng1
1College of Information and Cyber Security, People's Public Security University of China, Beijing 100038, China.
Sensors (Basel, Switzerland)
|June 27, 2025
概括
本研究介绍了Multi-HCNet,这是一种深度学习模型,用于检测电网频率 (ENF) 信号,即使由于高通波而丢失基本频率数据. 该模型在法医音频分析中实现了高精度,可以区分50 Hz和60 Hz信号.
科学领域:
- 法医科学 法医科学 法医科学
- 信号处理 信号处理
- 机器学习 机器学习
背景情况:
- 电网频率 (ENF) 检测对于法医音频和视频分析至关重要.
- 通信系统中的高通选通过删除基本频率信息来降低ENF检测.
- 现有的ENF检测方法在过环境中会损失性能.
研究的目的:
- 开发一个创新的深度学习模型,Multi-HCNet,用于在高通过环境中强大的ENF信号检测.
- 为了应对现代通信场景中基本频率信息丢失的挑战.
- 为了提高法医应用中ENF检测的准确性和可靠性.
主要方法:
- 多重HCNet模型包含一组高阶波过器 (AFB) 来捕获波组件.
- 分组多通道适应性注意力机制 (GMCAA) 用于区分多个频率信号 (例如50 Hz与60 Hz).
- 鼻腔激活功能 (SAF),以更好地捕捉ENF信号的周期性.
主要成果:
- 多重HCNet实现了98.84%的峰值检测准确度,并在高通过下保持了超过80%的平均准确度.
- 该模型有效地检测到ENF信号,即使没有基本的频率信息.
- 证明了成功区分50 Hz和60 Hz的ENF信号.
结论:
- 多重HCNet提供了一个新的解决方案,用于在具有挑战性的环境中检测ENF信号,而基本的频率数据丢失.
- 该模型能够区分50 Hz和60 Hz信号,从而支持实际的法医应用.
- 这项研究显著提高了ENF检测准确性和法医分析的稳定性.
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