mfcc

使用高斯混合模型（GMM），对声音信号进行识别，包括对特征参数的采集、训练和识别过程(Using the Gaussian mixture model (GMM)to realize the recognition of the sound signal, including the acquisition of the characteristic parameters,the training and the recognition process)

Active Feedforward Narrowband Noise Cancellation using Matlab and Simulink

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.)

语音信号的时域、频域分析,包括短时能量分析、短时平均过零率、自相关函数、短时平均幅度差函数等。(Time-domain speech signal, frequency domain analysis, including short-term energy analysis, the average short-term zero-crossing rate, autocorrelation function, such as short-time average magnitude difference function.)

语音的识别的前端技术，语音的基音周期检测(Front-end voice recognition technology, Voice Pitch Detection)

实现语音识别，使用HTK工具。README描述了具体的训练过程。(Voice recognition, using HTK tools. README describes the specific training process.)

语音识别开源代码 sphinx 0.8 windows版本(sphinx 0.8，ASR)

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.)

语音信号的短时分析，主要包括：分帧、短时能量、短时平均幅度、短时过零率、短时自相关函数、短时幅度差、倒谱、复倒谱、lpc系数、lpc谱估计等 绝对保证质量，是保研后导师布置的一些基础程序(Short-time speech signal analysis, mainly including: sub-frame, short-time energy, short-term average, short-time zero-crossing rate, short-time auto-correlation function, short-term rate of poor cepstrum, complex cepstrum, lpc coefficients, lpc spectral estimation, such as an absolute guarantee that the quality of instructors is the security arrangement after the inquest some of the basis of procedures)

端点检测程序 多频带，熵 谱熵 teger能量 希望大家好好学习好好利用(endpoint detection procedures multiband, entropy spectral entropy teger energy hope that we learn to make good use)