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[Speech/Voice recognition/combine] CHMMparameters DL : 0: 　提出了一种新的连续型隐马尔可夫模型(HMM ) 的概率密度函数, 并导出了一系列的参数寻优迭代公式,-A new Continuous Hidden Markov Model (HMM) of the probability density function, and derived a series of parameters optimization iteration formula,
Date : Size : 174kb User : 杨絮
[assembly language] chap08 DL : 0: ex6_1 ～ ex6_3二项分布的随机数据的产生 ex6_4 ～ ex6_6通用函数计算概率密度函数值 ex6_7 ～ ex6_20常见分布的密度函数 ex6_21 ～ ex6_33随机变量的数字特征 ex6_34 采用periodogram函数来计算功率谱 ex6_35 利用FFT直接法计算上面噪声信号的功率谱 ex6_36 利用间接法重新计算上例中噪声信号的功率谱 ex6_37 采用tfe函数来进行系统的辨识，并与理想结果进行比较 ex6_38 在置信度为0.95的区间上估计有色噪声x的PSD ex6_39 在置信度为0.95的区间上估计两个有色噪声x，y之间的CSD ex6_40 用程序代码来实现Welch方法的功率谱估计 ex6_41 用Welch方法进行PSD估计，并比较当采用不同窗函数时的结果 ex6_42 用Yule-Walker AR法进行PSD估计 ex6_43 用Burg算法计算AR模型的参数 ex6_44 用Burg法PSD估计 ex6_45 比较协方差方法与改进的协方差方法在功率谱估计中的效果 ex6_46 用Multitaper法进行PSD估计 ex6_47 用MUSIC法进行PSD估计 ex6_48 用特征向量法进行PSD估计-ex6_1 ~ ex6_3 binomial distribution of the generated random data ex6_4 ~ ex6_6 generic function value of the probability density function ex6_7 ~ ex6_20 common distribution density function ex6_21 ~ ex6_33 figure characteristics of random variables periodogram function ex6_34 used to calculate the power spectrum ex6_35 direct method using FFT signal above the noise of the power spectrum ex6_36 recalculated using the indirect method on the example of the power spectrum of noise signal tfe function ex6_37 used for system identification, and results were compared with the ideal ex6_38 at 0.95 confidence interval for the estimated colored noise x on the PSD ex6_39 at 0.95 confidence interval of the two colored noise on the estimated x, y between the CSD ex6_40 code used to achieve the Welch method of power spectrum estimation Welch method ex6_41 with PSD estimates, and compare different window function when the results when ex6_42 using Yule-Walker AR method is esti
Date : Size : 7kb User : 张满超
[Speech/Voice recognition/combine] speech2 DL : 0: 为了提高语音分离算法的收敛速度以及分离性能，提出把拉普拉斯正态混合分布概率密度函数作为语音信号概率密度函数的估计，得到一个更加适合语音信号分离的激活函数，基于此函数提出一种快速语音分离算法.-In order to improve speech separation algorithm convergence speed and separation performance, raise the normal mixture distribution Laplace probability density function as the voice signal probability density function estimation, be a more suitable for speech signal separation of activation function, this function is proposed based on kind of fast speech separation algorithm.
Date : Size : 1kb User : lihao
[Speech/Voice recognition/combine] hmm DL : 0: 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.
Date : Size : 526kb User : 于军
[assembly language] wfg DL : 0: MATLAB实现最大似然估计，本程序可以实现概率密度函数已知的情况下5个参数的估计~-MATLAB to achieve maximum likelihood estimation, the program can be achieved under the circumstances known to the probability density function estimation parameters 5 ~
Date : Size : 2kb User : 王小帅
[Editor] Histogram-matlab.m DL : 0: 统计直方图 matlab 概率密度函数和统计直方图的拟合-Histogram matlab probability density function and a histogram fitting
Date : Size : 1kb User : 七七
[Speech/Voice recognition/combine] Speech Encoding - Frequency Analysis MATLAB DL : 0: The speech signal for the particular isolated word can be viewed as the one generated using the sequential generating probabilistic model known as hidden Markov model (HMM). Consider there are n states in the HMM. The particular isolated speech signal is divided into finite number of frames. Every frame of the speech signal is assumed to be generated from any one of the n states. Each state is modeled as the multivariate Gaussian density function with the specified mean vector and the covariance matrix. Let the speech segment for the particular isolated word is represented as vector S. The vector S is divided into finite number of frames (say M). The i th frame is represented as Si . Every frame is generated by any of the n states with the specified probability computed using the corresponding multivariate Gaussian density model.
Date : Size : 769kb User : Khan17