MFCC_Coding_Demo2

基于倒谱短时部分反映了语音的声道特性，先用汉明窗取一帧语音，然后经变换得到语音倒谱，将倒谱短时部分取出，进行正交反变换后将得到声道的对数谱，即得到语音频谱的包络。将频谱包络和频谱画在一张图上，有很好的对比效果。获取的包络效果十分好。(Based Cepstral partly reflects the short channel characteristics of the speech, first take a Hamming window with a frame of speech, and speech cepstrum obtained by converting the cepstrum short segment out inverse orthogonal transform to obtain channel will of the spectrum, i.e. to obtain the envelope of the speech spectrum. The spectral envelope and spectral painted on a chart, there is a good contrast. Get the envelope effect is very good.)

语音增强电子书，国外经典教材，自适应滤波，卡尔曼滤波，最小均方算法等等(speech enhancement ebook)

一种加权K近邻语音情感识别程序，包括部分柏林语音情感库(A Weighting K-nearest neighbor speech emotion recognition program, including some libraries Berlin Speech Emotion)

回声消除算法比较，仿射投影APA与NLMS算法比较(Echo cancellation apa nlms)

程序实现的功能是：一种改进型的LMS算法！具有收敛速度快的特点(Procedures for the realization of the functions are: an improved algorithm of LMS! With the characteristics of fast convergence)

最权威和最全面的语音识别的特征提取的 IEEE的期刊论文1980(The most authoritative and comprehensive speech recognition feature extraction of the IEEE Journal Papers 1980)

语音通信，延迟小，质量高，游戏玩家专用哈。(Voice communications, delay, high quality, dedicated gamers Kazakhstan.)

（1）理解矢量量化的基本原理。 （2）掌握矢量量化器设计的LBG方法。 （3）运用LBG方法获取语音短时平均能量码书((1) to understand the basic principles of vector quantization. (2) master the vector quantizer design LBG method. (3) Using the LBG method to obtain the speech short time average energy code book)

本文介绍MVDR算法实现及其改进 用于多麦克风语音增强(The minimum variance distortionless response (MVDR), originally developed by Capon for frequency-wavenumber analysis, is a very well established method in array process-ing. It is also used in spectral estimation. The aim of this paper is to show how the MVDR method can be used to es-timate the magnitude squared coherence (MSC) function, which is very useful in so many applications but so few methods exist to estimate it)

特定人语音情感识别系统 关键词：语音信号，情感特征，情感识别 含论文、源代码、实验数据等( EMOTION RECOGNITION OF SPEECH BASED ON MATLAB Keywords：Speech Signal，Emotional Feature，Emotion Recognition)