wvd_test

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GMM训练模型，并用于说话人识别等相关领域，有不错的效果(GMM apply for text to speech)

the document explains speech recognition using mel frequency cpstrum coeefficient and noise reduction method

实现多个人的说话人识别，基于gmm模型，用mfcc参数训练(voice conversion based on gmm)

利用自适应滤波器对心音信号进行预测，包括了LMS算法，归一化LMS，变步长LMS和RLS算法，其中RLS的预测效果最好.(Heart sound signals using adaptive filter to predict, including the LMS algorithm, the normalized LMS, variable step size LMS and RLS algorithm, in which the best forecast of RLS.)

频域的模态参数识别方法之一，可以对振动信号进行分析处理，得出结构的固有频率，阻尼比，振型系数，方法很好，程序完整(Frequency-domain modal parameter identification methods that can analyze the vibration signal processing, draw the structure of natural frequencies, damping ratio, mode shape coefficient, very good method, program integrity)

适用于对各种语音信号的分帧，将语音信号分段处理(Applicable in a variety of speech signal sub-frame, the speech signal Break Processing)

实现声源定位功能，利用两个麦克风的语音数据获得原始声源的位置!(Achieve sound localization function, using two microphones for voice access to the original source data location!)

计算语音的短时平均能量。可以作为清音和浊音的特征参数，可以作为有声无声的依据(Calculate the short-term average energy of speech. It can be used as a characteristic parameter of voiceless and unvoiced sounds, and as a basis for sound and silence.)

采用广义旁瓣抵消（GSC）自适应波束形成方法实现时域和频域滤波，采用LMS自适应算法，最终实现语音增强。（文件中包含纯净语音及不同信噪比的带噪语音）(Generalized sidelobe canceller (GSC) adaptive beamforming method to achieve time-domain filtering using the LMS adaptive algorithm, and ultimately the speech enhancement)