传统谱减与MCRA语音增强算法

语音信号处理第三版 赵力等编著 配套程序(Codes of speech signal processing 3rd.)

实验示例是基于语音中的mfcc，语音倒谱特征来进行聚类，先利用训练样本来计算训练样本聚类中心（用到了lbg算法），之后再进行分类。 注意：使用代码时需要自己更改文件路径。(This example is based on the MFCC in speech and the feature of speech Cepstrum to cluster. First, the training sample is used to calculate the training sample clustering center (using the LBG algorithm), then the classification is then carried out.)

一个完整的语音端点检测MATLAB程序序，包含有完整的文件，通过调试，正确可用 (An integrated voice activity detection MATLAB program, includinng complete documentation, through testing, can be used correctly )

语音信号的盲源分离算法研究及应用，本程序利用matlab实现(Blind Source Separation Algorithm Research and Application of the voice signal, the program using matlab realize)

传统语音增强算法，谱减法和MCRC算法，matlab源代码。(Traditional speech enhancement algorithm, spectral subtraction and MCRC algorithm, matlab source code.)

关于lms经典算法汇总，包括lms,nlms,LMS_Equalizer,Basic_LMS,LMS toolbox等等。(classical algorithm for matrix lms, including lms, nlms. LMS_Equalizer, Basic_LMS, LMS toolbox, and so on.)

Speech PROcessing tools. First edit the makefile to match your needs

通常语音信号在增强时会出现混响现象，演讲者为了消除背景混响，不得不频繁地偏转头部的方向，这样会造成脉冲响应的不断改变。我们结合盲解卷法和频谱消去法来提高逆滤波器的滤波效果。我们利用输入语音信号间的相关系数矩阵计算出稳定、精确的室内脉冲响应的逆滤波器，而这些输入信号无需测量室内的脉冲响应就能被观测到。逆滤波能够消除早期的反射，这些反射包含混响中的绝大部分能量。之后，用频谱消去法来抑制逆滤波后的信号的尾部混响。本方法在实际适应性方面的表现通过具体的实验进行了验证，结果表明盲解卷法和频谱消去法的结合相较于单独使用一种方法，能够提供一个更优越的演说环境。(Usually voice signal will be enhanced when the reverberation phenomenon, the speaker in order to eliminate the background reverberation, and had to frequently deflect the direction of the head, this will cause the impulse response of changing. We combine the blind deconvolution and spectral elimination method to improve the filtering effect of the inverse filter. We use the correlation coefficient matrix between the input speech signal to calculate the stable, accurate inverse filter of the indoor impulse response, and these input signals do not need to measure the room impulse response can be observed. Inverse filtering to eliminate early reflections, these reflections contains most of the energy in the reverberation. After the spectrum elimination method to suppress the tail reverberation of the signal after the inverse filtering. Performance in the actual adaptation of the method by specific experimental validation results show that the blind deconvolution and spectrum to eliminate )

科大讯飞语音识别最新Demo,国内优秀的语音识别平台(new demo)

SVM分类器，针对语音信号的分类识别应用。内容非常多，大家可以筛选着利用。(SVMclassification)