GA_Matlab

mfcc algorithm this useful for analysis signal it s important feature

matlab FIR 低通，高通和带通滤波器的完整程序,本人花了好多时间自己写的,有滤波前后图形对比(matlab FIR low-pass，high-pass and band-pass IIR filters full process, I spent a lot of time to write your own, there is before and after the graphic correlation filter!)

建立小波神经网络负荷预测模型，并对小波神经网络的节点（输入层、中间层、输出层）进行选择,并选用合适的小波神经网络的训练函数,提高收敛速度和负荷预测精度。(The establishment of wavelet neural network load forecasting model, and wavelet neural network nodes (input layer, middle layer, output layer) to select and choose the appropriate wavelet neural network training function to improve the convergence speed and the load forecast accuracy.)

说明：  system initialisation in matlab

MATLAB program for comparison of arrays

closed loop control of insuction machine

一个demo 讲述掌上厨房的demo 应用Xcode(A demo about Palm kitchen demo application Xcode)

基于matlab高压直流电源simulink连接图，仿真(maltab/simuling power system )

实现了fisher线性分类器的功能,源码使用matlab编程,通俗易懂(Realize the fisher linear classifier functions, source code using matlab programming, user-friendly)

传统的短时谱估计语音增强算法通常假设语音谱分量相互独立，没有考虑语音谱分量间的相关性。针对这 一问题，该文提出一种新的基于多元Laplace分布模型的短时谱估计算法。首先，假设语音的离散余弦变换(DCT) 系数服从多元Laplace分布，以此利用谱分量间的相关性；在此基础上，利用多元随机矢量的高斯尺度混合模型表 示，推导得到语音DCT系数矢量的最小均方误差(MMSE)估计的解析表达式；并进一步推导了基于该分布模型的 语音存在概率，对最小均方误差估计子进行修正。实验结果表明，该算法在抑制背景噪声和减少语音失真等方面优 于传统的语音增强方法。(The spectral components of speech are usually assumed to be independent in traditional short-time spectrum estimation, which is not the case in practice. Tosolve this problem, a new speech enhancement algorithm with multivariate Laplace speech model is proposed in this paper. Firstly, the speech Discrete Cosine Transform (DCT) coefficients are modeled by a multivariate Laplace distribution, so the correlations between speech spectral components can be exploited. And then a Minimum-Mean-Square-Error (MMSE) estimator based on the proposed model is derived using a Gaussian scale mixture representation of random vectors. Furthermore, the speech presence uncertainty with the new model is derived to modify the MMSE estimator. Experimental results show that the developed method has better noise suppression performance and lower speech distortion compared to the traditional speech enhancement method. )