动态信噪比下舱音信号的降噪方法对比  

Comparison of Denoising Methods for the Cockpit Voice Signal Under Dynamic SNR

作　　者：周兆明[1] 王从庆[1] 李蕾 胡朝军[3] 

机构地区：[1]南京航空航天大学自动化学院 [2]北京市大兴区安全生产监督管理局 [3]中国人民解放军94916部队

出　　处：《电光与控制》2014年第10期94-98,105,共6页Electronics Optics & Control

摘　　要：针对舱音信息中响度大、种类多和频率范围宽的噪声对舱音识别性能造成严重影响的问题,利用基于最小均方差算法的自适应滤波器对舱音进行降噪。通过调整滤波器的阶数和步长使降噪效果达到最佳,然后对降噪后的舱音进行预加重、分帧、加窗及傅里叶变换;依次提取每个舱音信号的梅尔倒谱系数和一阶差分倒谱参数作为特征向量;设计支持向量机,利用舱音进行训练和识别,解决了舱音样本在低信噪比下识别性能低的缺点。仿真结果表明,该方法明显优于小波包降噪,识别精确率达到96.9231%。The noise of cockpit voice information has high loudness ,numerous types and wide frequency range,which has serious influence on performance of cockpit voice recognition .To solve the problem,an adaptive filter based on Least Mean Square algorithm was used for noise reduction,which could achieve the best noise reduction effect by adjusting the order and the step length of the filter .After that,the cockpit voice was pre-emphasized,framed,windowed and conducted Fourier transform ,followed by extracting Mel-Frequency Cepstrum Coefficients （MFCC） and first-order differential cepstrum parameters as feature vectors .Finally,a Support Vector Machine （SVM） was designed for training and identification.The problem that the performance of cockpit voice recognition is poor under a low SNR was solved .The simulation results show that this method is obviously superior to the wavelet packet de -noising,and the recognition accuracy rate reaches 96.9231%.

关 键 词：舱音记录器 声音识别 自适应滤波 梅尔倒谱系数 支持向量机 

分 类 号：V248.2[航空宇航科学与技术—飞行器设计] TP391.4[自动化与计算机技术—计算机应用技术]