equalizer

熵值越大则每个符号包含的平均信息量越大。有研究发现，在有噪声的语音信号中，语音信号的熵和噪声信号的熵存在着较大的差异，对噪声信号来说在整个频带内分布相对平坦，熵值小，语音信号集中在某些特定频段内，熵值大。因此利用这个差异可以区分噪音段和语音段。(The greater the entropy is, the greater the average information of each symbol is. It is found that, in noisy speech signals, the entropy of speech signals and the entropy of noise signals are quite different. For noisy signals, the distribution is relatively flat in the whole frequency band, and the entropy value is small. The speech signal is concentrated in some specific frequency bands, and the entropy value is large. So the difference can be used to distinguish the noise segment and the speech segment.)

基于hmm的识别程序 识别数字0-9，识别率全对(speech recognition based on hmm number 0-9 all right)

用NLMS实现对语音的回声的消除,共4个文件,语音原声,语音回声,NLMS的实现,回声路径(Using NLMS realize voice echo on the elimination of a total of four documents, voice, soundtrack, voice echo, NLMS realization echo path)

本实例程序是基于matlab的语音识别程序，通过提取传统的MFCC特征集，采用当前流行的SVM分类器，作为对比实验是一个不错的选择。(This example program is popular SVM classifier matlab speech recognition program to extract MFCC feature set, as a comparative experiment is a good choice. )

基于DTW算法实现孤立数字及连续数字语句的识别，识别率还可以（内含语音数据集）(Based on DTW algorithm, isolated number and continuous number statements can be recognized, and the recognition rate can also be (including voice data set))

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)

psola算法的设计与实现，经过调试已经可以运行(psola Algorithm Design and Implementation, after debugging has to run)

为均衡带限信号所引起失真的横向或格型自适应均衡器(其中横向FIR系统长M=11), 系统输入是取值为±1的随机序列 ，其均值为零；参考信号 ；信道具有脉冲响应： 式中w用来控制信道的幅度失真(w = 2～4，例如，取w = 2.9,3.1,3.3,3.5等),而且信道受到均值为零、方差为 (例如，取 ，相当于信噪比为30dB)的高斯白噪声 的干扰。试比较基于下列五种算法自适应均衡器在不同信道失真、不同噪声干扰下的收敛情况(对应于每一种情况，在同一坐标下画出其学习曲线)： 横向/格-梯型结构LMS算法[1-4] 横向/格-梯型结构RLS算法[1-4] ()

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)

LPC Speech Recoginze