材料分类从非视线声学回声使用波形声学混合特征融合的材料分类
Dilan Onat Alakuş1, İbrahim Türkoğlu2
1Department of Software Engineering, Faculty of Engineering, Kırklareli University, Kırklareli 39100, Türkiye.
Sensors (Basel, Switzerland)
|March 14, 2026
概括
这项研究提高了在非视线 (NLOS) 条件下声学材料的分类,使用了新的波纹声学混合特征融合与深度学习. 该CNN-LSTM模型实现了0.99准确度,提供了强大的NLOS声学传感.
科学领域:
- 声学 声学 在声学上
- 信号处理 信号处理
- 机器学习 机器学习
背景情况:
- 由于信号降解,非视线 (NLOS) 声学材料的分类具有挑战性.
- 现有的方法与复杂的声学环境和受阻的声音路径作斗争.
研究的目的:
- 通过混合特征融合和深度循环神经网络改进NLOS材料识别.
- 为实时NLOS声学传感开发一个强大的和可解释的框架.
主要方法:
- 使用模拟NLOS环境的ANLOS-R数据集收集了九种材料的回声信号.
- 提取的时间域声学特征和多尺度波纹能量/统计.
- 训练有素的深度学习模型包括LSTM,BiLSTM,GRU和CNN-LSTM在一个70维的混合特征集上.
主要成果:
- 卷积神经网络-LSTM (CNN-LSTM) 架构实现了最高的平衡精度和0.99.9的宏F1得分.
- 沙普利添加式扩展 (SHAP) 分析揭示了Mel-Frequency Cepstral Coefficients (MFCC) 和波形特征的互补作用.
- 该模型表现出强大的通用化和趋同性能.
结论:
- 拟议的波纹声学混合特征融合与CNN-LSTM有效地解决了NLOS材料分类的挑战.
- 该方法提供了一种可靠,可解释和数据驱动的解决方案,用于在受阻环境中实时声学传感.
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