segSNR+IS

语音信号分帧后，计算每帧信号的短时平均能量(After the speech signal is framed, the short-term average energy of each frame signal is calculated)

MFCC特征提取实例MFCC特征提取实例MFCC特征提取实例MFCC特征提取实例(Examples MFCC feature extraction)

1、文件夹中包含了经典功率谱估计和自适应均衡算法两个实验的所有程序。 2、R.m、LMS.m、LMSmain.m为自适应均衡算法的程序： R.m用来计算输入信号的自相关矩阵及其特征值； LMS.m为时域LMS算法，用统计的方法仿真得出不同信道参数和不同步长下的学习曲线； LMSmain.m为实验主程序，按照实验要求中的具体数据得到实验结果和曲线。 3、functionx.m、fzhouqitu.m、spectrum.m、bt.m、bart_lett.m、welch.m、SPECTRUMmain.m为经典谱估计的程序： functionx.m产生需要进行谱估计的函数； fzhouqitu.m用来计算信号周期图的函数； spectrum.m是用周期图法进行谱估计的函数 bt.m是用BT图法进行谱估计的函数 bart_lett.m是用BARTLETT法进行谱估计的函数 welch.m是用WELCH法进行谱估计的函数 SPECTRUMmain.m是主程序，按照实验要求中的具体数据得到实验结果和曲线。 (a document folder contains the classic power spectrum estimation and adaptive equalization algorithm for the two experiments all the procedures. 2, R.m, LMS.m, LMSmain.m adaptive equalization algorithm for the procedure : R.m used to calculate the input signal and the correlation matrix eigenvalue; LMS.m too LMS algorithm, use statistical simulation method come to a different channel parameters and the synchronous learning curve; LMSmain.m main program for the experiment, according to the experimental requirements of the specific data and the curve of the experimental results. 3, functionx.m, fzhouqitu.m, spectrum.m. bt.m, bart_lett.m, welch.m. SPECTRUMmain.m classical spectrum estimation procedures : functionx.m have a need for spectrum estimation function; fzhouqitu.m used to calculate t)

谱减法自适应滤波以及听觉掩蔽效应00865833(Spectral subtraction )

科大讯飞关于语音识别开发SDK库，支持语音转汉字功能及根据读汉字功能。有兴趣的可以好好研究研究(IFLYTEK speech recognition development SDK library are interested in a good studies)

声音特征提取程序，分帧后，对于每一帧提取MFCC等特征(voice feature extraction)

LPC Speech Recoginze

基于动态时间规整与隐马尔科夫模型的自动语音识别系统(Automatic speech recognition system based on dynamic time warping and hidden Markov model)

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.)

语音库开发工具源码，例程程序创建机读SAPI.SpVoice对象开发语音库。(Speech development tools source , routine program to create machine-readable SAPI.SpVoice speech object development .)