lms

FastICA算法，用于信号的独立分量分析，在ICA的基础上加快了收敛速度，有更高的效率！并且增加了图象界面，使用方便！(FastICA algorithm, the signal for an independent component analysis, at the ICA on the basis of accelerating the convergence rate, a more efficient! And to increase the image user interface!)

c#写的语音识别程序，识别效率还算可以。且根据识别可以给出相应的识别百分比。适合研究语音识别以及tts的参考！(c# to write the speech recognition process, identify efficiency figure. And recognition can be given according to the percentage of the corresponding identification. Tts speech recognition, as well as for research reference!)

lpc提取共振峰和基频，适合初学语音的同学使用，增强认识(lpc formant extraction and baseband, suitable for beginners to use the voice of the students, and enhance understanding)

基于matlab的HMM语音识别仿真，通过matlab实现对0-9数字的语音识别(HMM speech recognition simulation based on MATLAB)

基于lms的改进型谱减法，降低噪声效果好(Improved LMS-based spectral subtraction, noise reduction effect)

MFCC：Mel频率倒谱系数的缩写。Mel频率是基于人耳听觉特性提出来的，它与Hz频率成非线性对应关系。Mel频率倒谱系数(MFCC)则是利用它们之间的这种关系，计算得到的Hz频谱特征。(Speech signal feature extraction)

在evmdm642平台下的一个语音采集-回放任务，用48K/秒的采样率采集立体声音频输入，用给定的FIR 滤波参数处理数据后再播放出来。注：内附个人整理的详细操作文档。(evmdm642 platform in a speech to the gathering-intervals task with 48 K/s sampling rate Acquisition stereo audio input, used to the FIR filter parameters for data processing later. Note : with a detailed personal finishing operations documentation.)

matlab语音识别与分辨，可以做到特定语句的识别和特定人的识别。(Matlab speech recognition and resolution)

本系统是基于矢量量化技术的说话人识别系统。可根据语音库以及实时录音识别且具有图形用户界面。压缩包内含有源码、语音信息库音频及演示视频等。(The system is based on vector quantization speaker recognition system. Identifiable and has a graphical user interface based on speech and real-time recording. Within the compressed package contains the source code, voice information and presentation of audio and video.)

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.)