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深度光谱学习用于枪弹分类:CNN架构和时间频率表示的比较研究
Pafan Doungpaisan1, Peerapol Khunarsa2
1Faculty of Industrial Technology and Management, King Mongkut's University of Technology North Bangkok, Bangkok 10800, Thailand.
Journal of imaging
|August 27, 2025
概括
深度学习模型使用光谱图像准确地分类枪支声音. 在公共安全应用中,Mel,CQT和Cochleagram光谱与CNN的准确度超过94%.
科学领域:
- 听力学
- 机器学习
- 信号处理
背景情况:
- 枪声分类对于公共安全,法医分析和监视至关重要.
- 深度学习模型有助于提高枪支声音识别的准确性和效率.
研究的目的:
- 评估深度学习模型对枪支声音的分类性能.
- 调查各种时间频谱表示的效果,并将其转换为CNN分析的图像.
主要方法:
- 分析了12个时间频谱图 (Mel,Bark,MFCC,CQT,Cochleagram,STFT,FFT,Reassigned,Chroma,光谱对比,波纹).
- 将光谱图转换为RGB图像,用于计算机视觉技术.
- 在光谱图像上训练了六个卷积神经网络 (CNN) 架构 (ResNet18,ResNet50,ResNet101,GoogLeNet,Inception-v3,InceptionResNetV2).
主要成果:
- 当与ResNet101和InceptionResNetV2等深度CNN一起使用时,CQT,Cochleagram和Mel光谱图实现了高分类精度 (> 94%).
- 将光谱图转换为图像使得基于图像的处理和深度学习模型的有效使用成为可能.
- 这种方法在捕获光谱-时间模式以准确分类枪支声音方面表现出强大.
结论:
- 深度学习模型,特别是CNN,结合基于图像的光谱分析,为枪支声音分类提供了强大的框架.
- 当转换为深度学习的图像格式时,特定的光谱图表示 (CQT,Cochleagram,Mel) 是非常有效的.
- 这种方法提高了公共安全和法医调查等应用的准确性和稳定性.
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