HHT

语音压缩感知，观测矩阵采用混沌序列，稀疏字典采用KSVD训练得到。(compressed sensing with chaotic sequence and K-SVD )

经典麦克风语音增强算法，当阵元数目越多，语音增强效果越好(Classical microphone speech enhancement algorithm, the more the number of elements, the better the speech enhancement effect)

mfcc特征提取法 以及svm训练 可以使用(MFCC feature extraction method and SVM training can be used)

比较经典的用lms算法实现自适应均衡器的matlab的程序，适合初学者学习(Comparison of the classical LMS algorithm with adaptive equalizer matlab procedures, suitable for beginners to learn)

线性预测倒谱系数（Linear Prediction Cepstrum Coefficient,LPCC）是线性预测系数（Linear Prediction Coefficient,LPC）在倒谱域中的表示。该特征是基于语音信号为自回归信号的值设，利用线性预测分析获得倒谱系数。(Linear Prediction Cepstrum Coefficient)

不错的盲源分离工具箱，实现对声源的分离,包含多个子程序(a nice toolbox of blind source separation used to separate multiple sources)

用三种经典的方法对语音信号进行增强，效果还不错并且三种方法的比较(With three classical methods of speech signal enhancement, the results were good and Comparison of Three Methods)

语音信号的时域频域分析，从短时能量到语谱图，以及线性预测参数和梅尔倒谱系数等等(Speech signal in time domain frequency domain analysis, from the short-term energy to the spectrogram, and the linear prediction parameters and the Mel cepstral coefficients, etc.)

简单语音识别源码是一个调用讯飞语音识别SDK的例子源码是一个最纯净的Demo比较容易看懂。实现的是点击按钮开始语音监听，手机需要联网，2/3G的均可，识别后会以Toast的方式提示出来，识别率还是比较高的。之前自己是用别人的Demo总是不成功，是因为，讯飞的SDK想使用是需要APPID的，可能过太长时间久会注销，还有，未通过审核的APPID每天只能试用500次，如果太多人试用，就是达到上限导致无法使用。(A simple speech recognition source code is a call Ifly speech recognition SDK example source code is a demo of the purest easier to understand. The realization of the click button to start the voice listening, mobile phone need to networking, 2/3G can, after the recognition will be prompted by Toast way out, the recognition rate is still relatively high. Before he is with someone else s demo is not always successful, is because the Xunfei SDK to use is appid, may be too long for a long time will be canceled, and not through the audit appid every day can only be tried out 500 times, if too many people tried is reaching the limit to use.)

design of an equalizer for speech processing with using Parks-McClellan algorithm