Abnormal Sound Feature Extraction Based on EEMD
CHEN Zhiquan
YANG Jun
QIAO Shushan
Abstract：Aiming at solving the low recognition rate of abnormal sound recognition caused by using M FCC ,LPCC as feature ,the project proposes a feature extraction method for abnormal sound based on Ensemble Empirical Mode Decomposi‐tion (EEMD) combining the high nonlinearity and non‐stationary .First the abnormal sounds are segmented into frames and every frame of the sound is decomposed into IMFS ,then features including energy ,cross rate ,energy ratio ,and M FCC are extracted for every IMF .Finally the feature vectors are segmented and the means of every segment are computed as the final features .Using these features as input ,then the project adopts SVM as classifier to recognize seven kinds of abnormal sounds ,and the recognition rate is tested in railway background .Experiment results show that these features can improve the recognition rate comparing with M FCC .
Keywords：abnormal sound recognitionempirical mode decompositionfeature extractionsupport vector machine
Publication Date：2016-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 1875-1879,1894 )