基于MFCC-IMFCC和GA-SVM的鸟声识别

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

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.