基于声音特征优化和改进支持向量机的鸟声识别OACSTPCD

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.