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

Abstract

Abstract: 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.

Key words: cockpit voice recorder, non-voice acoustic signal, feature extraction, imbalanced sample, fuzzy support vector machine

CLC Number:

YANG Lin, WANG Congqing, JIANG Longsheng. Signal Recognition of Imbalanced Samples for CVR Based on Fuzzy SVM[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2012, (3): 544.

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Signal Recognition of Imbalanced Samples for CVR Based on Fuzzy SVM

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