最小均方误差的信噪比和信干噪比 lmssnr

最经典的基于ICA实现的语音信号的采集、随机混合，再通过盲分离将混合后的语音信号分离(The most classical ICA-based speech signal acquisition, random mixing, and then the mixed speech signal is separated by blind separation.)

用三种经典的方法对语音信号进行增强，效果还不错并且三种方法的比较(With three classical methods of speech signal enhancement, the results were good and Comparison of Three Methods)

ARMA模型建立程序，给出自相关序列，指定ARMA模型的P阶，Q阶参数，得出相应的参数值 function [bq ,ap]=armahat(rx,p,q)(ARMA model for the establishment of procedures, to come from related sequences, designated the P-order ARMA model, Q-order parameters, the corresponding parameter values derived function [bq, ap] = armahat (rx, p, q))

hMM的前向算法 ,详细的的介绍了算法的过程，并又程序的编程说明(hmm the method of hmm )

熵值越大则每个符号包含的平均信息量越大。有研究发现，在有噪声的语音信号中，语音信号的熵和噪声信号的熵存在着较大的差异，对噪声信号来说在整个频带内分布相对平坦，熵值小，语音信号集中在某些特定频段内，熵值大。因此利用这个差异可以区分噪音段和语音段。(The greater the entropy is, the greater the average information of each symbol is. It is found that, in noisy speech signals, the entropy of speech signals and the entropy of noise signals are quite different. For noisy signals, the distribution is relatively flat in the whole frequency band, and the entropy value is small. The speech signal is concentrated in some specific frequency bands, and the entropy value is large. So the difference can be used to distinguish the noise segment and the speech segment.)

4元麦克风阵列去除语音混响，适合正在学习阵列去混响的同学们参考一下(Microphone Array Dereverberation)

语音信号去噪，在MATLAB开发环境下，对含噪声的语音信号进行卡尔曼滤波，实现语音增强的功能。(The voice signal denoising, the Kalman filter, the noisy speech signal in the MATLAB development environment Wave, speech enhancement.)

基于熵的端点检测算法，比传统的端点检测能更精确的检测出端点(endpoint detection algorithm than the traditional endpoint detection more accurate detection of Endpoint)

HHT变换，经验模态分解，时变频率与阻尼的模式提取(HHT transform, empirical mode decomposition, extraction of time-varying model)

对基于残留噪声去除的谱减法的编程，实现了高去噪水平(The program of spectral subtraction based on residual noise removal has achieved high denoising level.)