基于MFCC-IMFCC和GA-SVM的鸟声识别  

Bird Sound Recognition Based on MFCC-IMFCC and GA-SVM

作　　者：韩鹏飞 陈晓[1,2] HAN Peng-Fei;CHEN Xiao(School of Electronic and Information Engineering,Nanjing University of Information Science and Technology,Nanjing 210044,China;Jiangsu Provincial Collaborative Innovation Center of Atmosphere Environment and Equipment Technology,Nanjing University of Information Science and Technology,Nanjing 210044,China)

机构地区：[1]南京信息工程大学电子与信息工程学院,南京210044 [2]南京信息工程大学江苏省大气环境与装备技术协同创新中心,南京210044

出　　处：《计算机系统应用》2022年第11期393-399,共7页Computer Systems & Applications

摘　　要：鸟声识别研究中声音特征选取对识别分类的准确度有很大影响.为了提高鸟声识别正确率,针对传统的梅尔倒谱系数(MFCC)对鸟声高频信息表征不足.提出了基于Fisher准则MFCC和翻转梅尔倒谱系数(IMFCC)的特征融合,得到新的特征参数MFCC-IMFCC应用于鸟声识别,提高对鸟声高频信息表征.同时通过遗传算法(GA)对支持向量机(SVM)中的惩罚因子C和核参数g进行优化,训练出GA-SVM分类模型.实验表明,在同一条件下,MFCC-IMFCC与MFCC相比,识别率有一定的提高.In the research of bird sound recognition,the selection of sound features has a great impact on the accuracy of recognition and classification.To improve the accuracy of bird sound recognition,this study starts with the problem that the traditional Mel frequency cepstral coefficient(MFCC)characterizes the high-frequency information in bird sound insufficiently.Feature fusion of MFCC based on Fisher criterion and inverted MFCC(IMFCC)is proposed to obtain a new feature parameter MFCC-IMFCC that can be applied to bird sound recognition to improve the characterization of the high-frequency information in bird sound.Meanwhile,the penalty factor C and the kernel parameter g in the support vector machine(SVM)are optimized by a genetic algorithm(GA),and a GA-SVM classification model is trained.Experiments show that under the same conditions,the recognition rate of the MFCC-IMFCC approach is higher than that of the MFCC one.

关 键 词：梅尔倒谱系数 逆梅尔倒谱系数 FISHER准则 GA-SVM 声音识别 

分 类 号：TN912.3[电子电信—通信与信息系统] Q958[电子电信—信息与通信工程]