work

计算语音、音频信号的信噪比程序（包含音频样本文件）(Calculation of voice, audio program signal to noise ratio (including the audio sample file))

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))

mfcc.m 用于语音识别上的mfcc特征参数提取函数。(mfcc.m mfcc characteristic parameters for speech recognition on the extraction function.)

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)

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)

这 里主要对LMS算法及一些改进的LMS算法（NLMS算法、变步长LMS算法、变换域LMS算法）之间的不同点进行了比较，在传统的LMS算法的基础上发 展了LMS算法的应用。另一方面又从RLS算法的分析中对其与LMS算法的不同特性进行了比较。(Here mainly on the LMS algorithm and some improvements of the LMS algorithm (NLMS algorithm, variable step size LMS algorithm, transform domain LMS algorithm) between the different points of comparison, in the traditional LMS algorithm developed on the basis of the application of the LMS algorithm. On the other hand from the analysis of RLS algorithm and LMS algorithm for its different characteristics compared.)

基于FastICA相结合实现的语音信号的采集、随机混合，再通过盲分离将混合后的语音信号分离(Speech signal acquisition and random mixing based on FastICA, and then the mixed speech signal is separated by blind separation.)

用于语音信号，心音信号基于SVM的MATLAB识别(Recognition of Speech Signal and Heart Sound Signal Based on SVM)

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)

实现已知训练序列的矢量量化器（LBG）算法。初始码书从训练序列每隔五个样本选取一组(Achieve a known training sequence of the vector quantizer (LBG) algorithm. The initial codebook from the training sequence every five samples to select a group of)