电子测量与仪器学报

曾豫宁,行鸿彦,侯天浩,王心怡,郑锦程.基于MFCC和PSO-SVM的雨量识别方法[J].电子测量与仪器学报,2025,39(2):83-91

基于MFCC和PSO-SVM的雨量识别方法

Rainfall recognition method based on MFCC and PSO-SVM

DOI：

英文关键词:Mel frequency ceptral coefficient particle swarm optimization algorithm support vector machine rainfall prediction

基金项目:国家自然科学基金(62171228)、国家重点研发计划(2021YFE0105500)项目资助

作者	单位
曾豫宁	南京信息工程大学电子与信息工程学院南京210044
行鸿彦	1.南京信息工程大学电子与信息工程学院南京210044；2.南通理工学院电气与能源工程学院南通226001
侯天浩	南京信息工程大学电子与信息工程学院南京210044
王心怡	南京信息工程大学电子与信息工程学院南京210044
郑锦程	南京信息工程大学电子与信息工程学院南京210044

Author	Institution
Zeng Yuning	School of Electronics and Information Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China
Xing Hongyan	1.School of Electronics and Information Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China;2.School of Electrical and Energy Engineering, Nantong Institute of Technology, Nantong 226001, China
Hou Tianhao	School of Electronics and Information Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China
Wang Xinyi	School of Electronics and Information Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China
Zheng Jincheng	School of Electronics and Information Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China

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中文摘要:

针对现有基于雨声信号及机器学习方法的雨量识别准确率较低等问题，通过分析雨声信号的频率特性，研究雨声信号的梅尔倒谱系数静态与动态特征，提出了一种梅尔倒谱系数（MFCC）与粒子群算法优化支持向量机（PSO-SVM）相结合的雨量识别方法。通过提取雨声信号的MFCC静态与动态特征，利用随机森林算法内置的重要性评估机制进行特征选择，引入PSO算法对SVM的惩罚参数c以及核函数参数g进行微调，寻找最优参数组合，实现精准的雨量识别。实验结果表明，MFCC特征与其他特征相比能更有效的表征雨滴声纹信号特征，经过随机森林特征选择后的总体雨量识别准确率提高了5%，结合优化后的PSO-SVM进行雨量识别，其总体雨量识别准确率达到了91.1%，其中大雨、小雨的降雨识别准确率也均超过了90%，中雨的降雨识别准确率稍低，但也达到了86.5%。

英文摘要:

Aiming at the problem of low accuracy in rainfall recognition based on rain sound signals and machine learning methods, this paper analyzes the frequency characteristics of rain sound signals, studies the static and dynamic features of Mel frequency cepstral coefficients of rain sound signals, and proposes a rainfall recognition method that combines Mel frequency cepstral coefficients (MFCC) with particle swarm optimization support vector machine (PSO-SVM). By extracting the static and dynamic features of MFCC from rain sound signals, using the importance evaluation mechanism built into the random forest algorithm for feature selection, and introducing PSO algorithm to fine tune the penalty parameter c and kernel function parameter g of SVM, the optimal parameter combination is found to achieve accurate rainfall identification. The experimental results show that MFCC features can more effectively characterize the characteristics of raindrop voiceprint signals compared to other features. After random forest feature selection, the overall accuracy of rainfall recognition increased by 5%. Combined with optimized PSO-SVM for rainfall recognition, the overall accuracy of rainfall recognition reached 91.1%. The accuracy of rainfall recognition for heavy and light rain also exceeded 90%, while the accuracy of rainfall recognition for moderate rain was slightly lower, but still reached 86.5%.

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