MATLAB-SRS3D-hrtf

说明：  这里介绍了一种基于自适应滤波的噪声抵消法，采用归一化最小均方误差算法，采集实际噪声环境下各种不同信噪比的带噪语音样本进行降噪处理，实验结果表明，处理后信号的信噪比得到了较大程度的提高，大大改善了听音效果，具有很高的可懂度，且语音自然度好，没有失真；并与谱减法进行了比较，自适应噪声抵消法的降噪幅度比谱减法有一定提高，在听音效果上，用自适应噪声抵消法处理后的语音在清晰度，自然度方面优于谱减法。(Here a novel adaptive noise cancellation method using normalized least mean square algorithm, collecting a variety of actual noise environment, the SNR of noisy speech samples for noise reduction, experimental results show that treatment After the signal to noise ratio was greater improvement in the level, greatly improved the listening results, with high intelligibility and naturalness of speech is good, no distortion and were compared with the spectral subtraction, adaptive noise cancellation law than the spectral subtraction noise reduction rate of a certain improvement in the listening results, and with the adaptive noise cancellation method in the treatment of voice clarity, naturalness is better than spectral subtraction.)

语音维纳滤波，对语音段添加白噪声，然后通过维纳滤波器对其进行滤波(Wiener filtering for speech)

Matlab在脆弱水印中的应用。对matlab在脆弱水印中的应用指明了方向(Matlab in fragile watermarking application. Of fragile watermarking in matlab application specified in the direction)

本人编写的用直方图提取纹理特征的源码，希望对大家有用(I am prepared to use the histogram of the source texture feature extraction, in the hope that useful)

遗传算法程序，很实用，高人编程。有详细的程序注释，可以供参考(It compares the effects of early warning using BP Neural Network before and after the rank sum test, sampling the listed companies in Shenzhen and Shanghai SecurityMarkets. The results showthat BP neural net- work model has good application value to the warning for financial distress of China’s listed companies. Especially, )

二并行FIR算法具有速度快的优点，其算法对信号的处理有它独特的优势，其中的源程序对算法进行了仿真，与结论一致(FIR Parallel algorithm )

fuzzy cmeans program in matlab for segmentation

2D FDTD of a region with Mur s absorbing boundary condition (ABC)

Script for computing the frequency offset from an OFDM short preamble

SOM神经网络，可以进行特征提取和模式分类，特别是特征维数较多的情况(SOM neural network, feature extraction and pattern can be classified, more especially characteristic dimension of the case )