子带特征参数在家禽应激发声识别中的应用

Application of Subband Feature of Poultry Vocalization for Identifying Their Status

基于子带梅尔频谱质心的优化特征参数(SMSC),研究了35日龄海兰褐蛋鸡在正常状态、光照与人为干扰等应激下发声信息的变化特征,并通过建立支持向量机模型(SVM),比较了采用优化特征参数与梅尔频标倒谱系数及其一阶、二阶导数组成的特征参数(MFCC-39)在识别不同应激中的应用效果。试验结果表明,采用加入梅尔频谱质心修正的特征参数,在分类正确识别率方面平均提高了24%。

Animal＇s welfare can be feed back from their vocalism.Some experiments were conducted to get 35-day-old chicken＇s vocalization in different environments,including normal environment,light off environment and human disturbance environment. Based on Mel frequency cepstral coefficient（MFCC）,a new algorithm for feature extraction——subband Mel spectrum centroid（SMSC） was adopted.Support vector machine（SVM） classification model was used to evaluate the performance of test using different sound parameters.The results show the accuracy of recognition of SMSC was 24% more than MFCC parameter.