LPCC

这是一个在线签名的程序，使用了AR实现算法和DTW算法(This is an online signature process, using the AR algorithm and DTW algorithm)

利用自相关或平均幅度差提取基音原理，帧长160，帧移80。求能量E、过零率Z和基音周期pitch；采用自相关法或平均幅度差法求取基音周期，基音周期范围（80~400hz）；画出能量、过零率和基音曲线。(Pitch principle is extracted by means of autocorrelation or mean amplitude difference)

通常语音信号在增强时会出现混响现象，演讲者为了消除背景混响，不得不频繁地偏转头部的方向，这样会造成脉冲响应的不断改变。我们结合盲解卷法和频谱消去法来提高逆滤波器的滤波效果。我们利用输入语音信号间的相关系数矩阵计算出稳定、精确的室内脉冲响应的逆滤波器，而这些输入信号无需测量室内的脉冲响应就能被观测到。逆滤波能够消除早期的反射，这些反射包含混响中的绝大部分能量。之后，用频谱消去法来抑制逆滤波后的信号的尾部混响。本方法在实际适应性方面的表现通过具体的实验进行了验证，结果表明盲解卷法和频谱消去法的结合相较于单独使用一种方法，能够提供一个更优越的演说环境。(Usually voice signal will be enhanced when the reverberation phenomenon, the speaker in order to eliminate the background reverberation, and had to frequently deflect the direction of the head, this will cause the impulse response of changing. We combine the blind deconvolution and spectral elimination method to improve the filtering effect of the inverse filter. We use the correlation coefficient matrix between the input speech signal to calculate the stable, accurate inverse filter of the indoor impulse response, and these input signals do not need to measure the room impulse response can be observed. Inverse filtering to eliminate early reflections, these reflections contains most of the energy in the reverberation. After the spectrum elimination method to suppress the tail reverberation of the signal after the inverse filtering. Performance in the actual adaptation of the method by specific experimental validation results show that the blind deconvolution and spectrum to eliminate )

有关语音的matlab源程序,其中包含语音的频谱分析等.(Matlab source code on the voice, including voice spectrum analysis.)

PESQ-主观语音质量评估。 ITU-T P.862建议书提供的客观MOS值评价方法。PESQ可以广泛的用于现场和模拟网络中端到端测量。背景(环境)噪声和噪声处理,可以通过用PESQ比较干净的、未处理的信号和编码的、经噪声干扰失真的信号评估得到。(PESQ- subjective speech quality assessment. ITU-T P.862 recommendations for objective MOS value uation method book provides. PESQ can be widely used in the field and in the end measurement analog networks. Background (ambient) noise and noise processing, you can use PESQ relatively clean, unprocessed signal uation and signal encoded by the noise distortion obtained.)

gmm模型下的语音情感识别系统，GMM只是一个数学模型，只是对数据形态的拟和，但是和你所看到的数据分布存在出入也是正常的，因为用EM估计GMM的那些参数时，一般假设我们所得到的数据是不完备的（也就是说假设我们看到的数据分布不是真正的分布，它在运算时把那部分丢失或者叫隐藏的数据“补”上了）(gmm model speech emotion recognition system, GMM is a mathematical model, but fitting the data form, but you can see there is access to the data distribution is normal, because those with the EM estimated GMM parameters, the general assumption we get the data is incomplete (that is, assuming we see the distribution of the data distribution is not real, it is part of the operation at the time that is lost or hidden data is called " fill" on the))

回音消除算法matlab,采用的VIRE的双方通话检测(Matlab algorithms eliminate echoes, the two sides call the VIRE Detection)

在MATLAB中完成对语音的梅尔倒谱系数的提取(Extraction of Mel cepstrum coefficients)

基于MFCC的利用LPCC做的说话人识别论文(Based on the MFCC the use of LPCC do the speaker identification papers)

FastICA算法，用于信号的独立分量分析，在ICA的基础上加快了收敛速度，有更高的效率！并且增加了图象界面，使用方便！(FastICA algorithm, the signal for an independent component analysis, at the ICA on the basis of accelerating the convergence rate, a more efficient! And to increase the image user interface!)