speech-signal-processing

利用所给的中心削波,计算自相关/AMDF代码,选取一段语音,使用Praat预估其基音周期范围,合理设置预估的极值点距离等参数,计算并画出语音的基音频率曲线(By using the given center clipping, the autocorrelation /AMDF code is calculated, a speech is selected, the range of the pitch period is estimated by Praat, the estimated distance of the extreme point is set up reasonably, and the pitch frequency curve of the speech is calculated and drawn.)

matlab 实现的基于GMM的说话人识别系统(matlab implementation of the GMM-based speaker recognition system)

在平台上找到的滑动/移动DCTII算法，都是用不了的，尤其是用于LMS，根本不适用，查了国内外论文，遍了程序，供大家参考(In PUDN,most DCT programm are not fit to LMS adaptive filter. with several literatures, this programm is finished for LMS filter.)

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)

基于矢量量化的孤立词语音识别系统看，性能良好，Matlab 实现！(Based on vector quantization of isolated word speech recognition system to see, good performance, Matlab to achieve!)

分段信噪比测试，主要是用于语音增强，语音编码后的测试使用(segment SNR test, primarily for speech enhancement, speech coding after testing)

基于matlab的语言情感识别仿真，可以实现用户注册，识别使用者说话时的喜怒哀乐等情感，人数少时可以实现很高的准确率。(Emotion Recognition based on Matlab language simulation, you can implement user registration, the number of emotions such as feelings, identify the user to speak came from a high accuracy can be achieved.)

能有效实现语音合并，将两个相同采样率和相同精度的语音文件合并成为一个相同采样率和相同精度的语音文件。(Can effectively realize the merger of voice, and the two the same sampling rate and the same precision audio files into a different sampling rate and the same accuracy of voice files.)

hmm文件时运用HMM算法实现噪声环境下语音识别的。其中vad.m是端点检测程序；mfcc.m是计算MFCC参数的程序；pdf.m函数是计算给定观察向量对该高斯概率密度函数的输出概率；mixture.m是计算观察向量对于某个HMM状态的输出概率，也就是观察向量对该状态的若干高斯混合元的输出概率的线性组合；getparam.m函数是计算前向概率、后向概率、标定系数等参数；viterbi.m是实现Viterbi算法；baum.m是实现Baum-Welch算法；inithmm.m是初始化参数；train.m是训练程序；main.m是训练程序的脚本文件；recog.m是识别程序。(hmm HMM algorithm file using speech recognition in noisy environments. Which is the endpoint detection process vad.m mfcc.m procedure is to calculate the MFCC parameters pdf.m function is calculated for a given observation vector of the Gaussian probability density function of output probability mixture.m is to calculate the observation vector for a HMM state output probability of observation vector is the number of Gaussian mixture per state output probability of the linear combination getparam.m before the calculation of the probability function, backward probability, calibration coefficients and other parameters viterbi.m is Viterbi algorithm implementation baum.m Baum-Welch algorithm to achieve inithmm.m is the initialization parameters train.m is the training program main.m training program is a script file recog.m is to identify procedures.)

利用matlab提取声音的基本特征，包络、MFCC、声谱图、能量。代码不复杂，但是省去大家找这些的时间。(The basic feature extraction using matlab sound envelope, MFCC, sonograms, energy. Code is not complicated, but we look for these omitted time.)