计算机工程2011，Vol.37Issue(11)：192-194,3.DOI:10.3969/j.issn.1000.3842.2011.11.066
基于Bark子波变换的MFCC特征提取
MFCC Feature Extraction Based on Bark Wavelet Transform
摘要
Abstract
In order to improve the quality of speech in low Signal Noise Ratio(SNR), an improved Mel Frequency Cepstral Coefficient(MFCC)feature extraction method is proposed. On the basis of the traditional MFCC feature extraction, the improved method introduces Bark Wavelet Transform(BWT) for more suitable to human ear's auditory system, it is used to make preprocessing before Fast Fourier Transform(FFT), on the other hand, it is used to instead of Discrete Cosine Transform(DCT) in MFCC. In the pre-processing stage Lanczos window function is adopted to restrain the side lobe and to improve the robustness. Experimental results show that compared with the traditional MFCC, the improved method can improve the speaker identification accuracy in the noisy environment.关键词
说话人识别/Mel频率倒谱系数/Bark子波/窗函数Key words
speaker recognition/ Mel Frequency Cepstral Coefficient(MFCC)/ Bark wavelet/ window function分类
信息技术与安全科学引用本文复制引用
尹许梅,何选森..基于Bark子波变换的MFCC特征提取[J].计算机工程,2011,37(11):192-194,3.基金项目
湖南省湘潭市科技计划基金资助项目(ZJ20071008) （ZJ20071008）
