面向不均匀飞机舱音信号样本的模糊支持向量机识别方法

 针对驾驶舱话音记录器(CVR)中记录的舱音背景信息多而复杂、频率范围宽、非平稳等特点,通过对15种舱音信息进行傅里叶变换和小波包变换,依次提取其Mel倒谱系数(MFCC)和小波包分解系数(WPC),利用距离可分性判据对MFCC和WPC信息进行压缩融合,得到舱音信息特征向量。设计了面向不均衡样本的模糊支持向量机(FSVM),分别计算每种类别样本及其内每种舱音信息的2个隶属度,然后利用FSVM对舱音信号进行分类识别,解决了CVR信号含噪奇异样本和数目不均衡样本时识别性能较差的缺点,实验表明该方法明显优于常规支持向量机(SVM)和FSVM,分类识别率达到98.33%。

Signal Recognition of Imbalanced Samples for CVR Based on Fuzzy SVM

The voice signals in a cockpit voice recorder(CVR)are complex,non-stationary,and exist in a wide frequency range.By using Fourier transform and wavelet packet transform for the fifteen kinds of signals in a CVR,Mel frequency cepstrum coefficient(MFCC) and wavelet packet coefficient(WPC) are extracted as the initial characteristic samples.The characteristic vectors are determined by compression of the MFCC and WPC samples using a geometric distance classification criterion.A fuzzy support vector machine(FSVM) is designed to handle the imbalanced sample classification in the CVR,in which two different fuzzy-membership values in relation to the imbalanced samples are calculated by the extracted samples in each voice signal.The above method can improve the recognition performance of the voice signals with imbalanced samples in the presence of outliers and noise.The experimental results show that it is obviously superior to the conventional support vector machine(SVM) and FSVM with a 98.33% recognition rate.