基于MFCC和小波包變換及模糊SVM的飛機(jī)艙音識(shí)別.pdf

1、南京航空航天大學(xué)碩士學(xué)位論文基于MFCC和小波包變換及模糊SVM的飛機(jī)艙音識(shí)別姓名：姜龍生申請(qǐng)學(xué)位級(jí)別：碩士專(zhuān)業(yè)：模式識(shí)別與智能系統(tǒng)指導(dǎo)教師：王從慶2011-01基于 MFCC 和小波包變換及模糊 SVM 的飛機(jī)艙音識(shí)別 ii ABSTRUCT There are many air disasters in the world every year. A necessary evidence in the analysis of air

2、 disaster is Black Box which includes Flight Data Recorder(FDR) and Cockpit Voice Recorder(CVR). CVR records some objective voices which reflect the condition of aircrafe and equipment,and some subjective information whi

3、ch reflects the perception and emotions of pilot,such as voices,aviation noise and background sound.CVR is an important evidence in the analysis of air disaster.It provides important evidence for the air disaster recons

4、truction. The voice signals in CVR are complex and non-stationary,and they have wide frequency range.This paper studies the classify of cockpit voice according to fourier transform,wavelet packet transform and fuzzy SVM.

5、 The major works are summarized as follows: First of all,on the basis of the “aircrafe cabin sound sample library”of the center of aviation safety technology CACC,this paper reduces the noise and intercept the cockpit vo

6、ice with Adobe Auditio.The alarm sounds,switch,knob and other independent samples are successfully separated from the mixed signals. Secondly,fourier transform and wavelet packet transform are used for the independent co

7、ckpit voice, Mel Frequency Cepstrum Coefficient(MFCC) and Wavelet Packet Coefficient (WPC) are extracted as the initial characteristics.The finally characteristics are determined by geometric distance classifiability cri

8、terion. Then,the support vector machine (SVM)algorithm is sensitive to outliers and noise present in the datasets and when it comes to imbalanced samples,SVM produces suboptimal classification models. Fuzzy SVM(FSVM) is

9、 a variant of the SVM algorithm,which has been proposed to handle the problem of outliers and noise.However,like the normal SVM algorithm,FSVM can also suffer from the problem of imbalanced samples.In this paper,we prese

10、nt a method to improve FSVM for imbalanced samples learning,which can be used to handle the imbalanced samples problem in the presence of outliers and noise.Training samples are assigned two different fuzzy-membership va

11、lues,and these membership values are incorporated into the SVM learning algorithm. Based on the experiment results,it can be concluded that the proposed method is a very effective method. Lastly, a software to classify t