测控技术2024，Vol.43Issue(6)：21-25,32,6.DOI:10.19708/j.ckjs.2024.06.004
基于声音特征优化和改进支持向量机的鸟声识别
Bird Sound Recognition Based on Optimized Sound Features and Improved SVM
摘要
Abstract
To improve the accuracy of bird sound recognition with low number of parameters,a new bird sound recognition method is proposed,including optimization of bird sound signal features and crow search support vector machine(SVM)classification recognition.Firstly,principal component analysis is used to optimize the Mel frequency cepstral coefficients(MFCC)and flipped Mel frequency cepstral coefficients extracted from bird sound,and the optimized sound features parameters is taken as input for the bird sound recognition algorithm.Then,the crow search algorithm is used to optimize the kernel parameters and loss values of the SVM,and an improved SVM network is obtained for bird sound classification and recognition.The experimental test results show that the correct recognition rate of the method for five bird sounds is 92.2％,and the best recognition effect can be achieved when the sound feature dimension is 16.The method provides a feasible approach for automatic bird sound recognition in the wild.关键词
声音识别/鸟声识别/主成分分析/支持向量机/乌鸦搜索算法Key words
sound recognition/bird sound recognition/principal component analysis/SVM/crow search algo-rithm分类
计算机与自动化引用本文复制引用
陈晓,曾昭优..基于声音特征优化和改进支持向量机的鸟声识别[J].测控技术,2024,43(6):21-25,32,6.基金项目
南京信息工程大学大学生创新创业训练计划项目(XJDC202310300067) （XJDC202310300067）
