新建文件夹 (5)

对录音信号集 中的某一语音，利用BATLAB设计一美尔例谱算法，并实现。 取信号集 中的一个语音信号：“xxxxxx”，将它作为输入的语音信号来为设计一个美尔倒谱算法，在该算法中，主要设计了以下环节： 1.读入一个语音信号；2.对这个信号归一化；3.对归一化的信号进行加窗处理（这里的矩形窗长度必须为257，重帧长64）；4.进行预加重处理，即通过一个高通滤波器： ；5进行512点的FFT；6.分别取模平方得到功率谱；7.在设计的mel滤波器组中，我采用了25个带通滤波器；8.将得到的功率谱信号通过mel滤波器组，得到相应的25个功率值；9.对这些功率值取自然对数；10.对这些值取离散作弦变换；11.将得到的值去掉直流分量，取其它值作为MFCC参数。 (Concentrated on recording a voice signal, using BATLAB designed Mayer cases spectral algorithm, and implementation. Get a voice signal signal focus: "xxxxxx", will it as input for the design of a voice signal to Mel Cepstrum algorithm in the algorithm, the main design of the following links: 1. Read a speech signal 2. On the signal normalized 3. Of the normalized windowed signal processing (here, rectangular window length must be 257, re-frame size 64) 4. For Pre-emphasis processing through a high-pass filter: 5 for 512 points of FFT 6. modulo square were obtained spectrum 7. in the design of mel filter banks, I use a 25 band-pass filter 8 . will be the power spectrum signal by mel filter bank, corresponding to the 25 power values 9. on the power to take the natural logarithm value 10. to take these values as strings of discrete transform 11. will be of value to out DC component, to take other values as MFCC parameters.)

一维的MATLAB的FDTD的仿真程序，可帮助初学者对FDTD有所帮助。(One-dimensional FDTD simulation of the MATLAB program to help beginners on the FDTD help.)

说明：  一个非常不错的PCR工具包 功能比较齐全包含有PCRCV 等 并有帮助说明(A very nice feature relatively complete PCR kit and help explain)

MATLAB AR模型三谱计算程序，我们实验室用的，完美运行

本源码用MATLAB语言实现了自适应RLS算法，并附有FFT分析(rls fft)

matlab在系统辨识中的应用此处为M序列产生的源代码及运行后结果（包括图像）(Matlab system identification in the application here for the M series of source code and run after the results (including images))

使用MVDR算法进行波束形成并处理宽带信号，该算法分频带进行处理，并最后给出了波束图。(MVDR algorithm is used for beamforming and broadband signal processing. The algorithm divides the frequency band for processing, and finally gives the beam pattern.)

Using the L system method of fractal to draw the tree and plants

heat transfer example 1a

Model Predictive Control exercise pt 2