speech-seperation

数值分析---部分算法 希望对需要的人有一点点用！(--- Part of numerical analysis algorithm wishes to people in need have a little bit of use!)

MATLAB从入门到精通 书上源代码，人民邮电出版社(MATLAB books from the entry to the master source code, Posts & Telecom Press)

MATLAB GUI教学视频0：GUI中的基本操作 (MATLAB GUI instruction video 0: GUI in the basic operation of)

fuel cell and battery

Interpolation using Lagrange

动态滑模控制的matlab数值仿真，希望对大家有用(Dynamic sliding mode control of matlab numerical simulation, in the hope that useful to everybody)

it is about dc motor speed control using matlab

matlab函数分类整理，放在手边很有用，适合初学入门者收藏使用。(matlab function sorting, handy very useful for beginners beginner collection use.)

本实验语音库为免费的柏林情感语音库，其采样频率为16KHZ，16bit量化。该语音库共有500 句情感语音信号，分别由十名专业演员(5 男，5 女)在不同情感状态下（高兴、愤怒、平静、悲伤、害怕、厌烦、憎恨）朗读十句不同文本的德语组成。本实验选取其中的部分情感（高兴、愤怒、悲伤）加以识别。仿真实验环境为MATLAB7.0。 实验选取的情感特征为短时平均能量、短时平均幅度、基频和短时过零率。为了降低不同人在表达不同情感时的个人差异造成的影响，本文实验过程中将提取的情感特征进行归一化处理。归一化采取将同一个人的三种情感语音信号的情感特征放在一起归一化处理，并将归一化后的情感特征作为KNN分类器的训练样本和测试样本。 该实验使用了传统的KNN算法和改进算法两种识别方式，通过实验结果对比了各自的特点。 (This experiment YuYinKu for free Berlin YuYinKu emotion, the sampling frequency for 16 KHZ, 16 bit quantification. The YuYinKu 500 words of emotional speech signal, respectively by ten professional actors (5 male, five female) in different emotional state (happy, anger, sadness, fear, calm, bored, hate) read ten sentences in different text of the German. This experiment selection of part of the emotions (joy, anger, sadness) is recognized. The simulation experiment environment for MATLAB7.0. The selection of emotional characteristics for short-term average energy, short-term average amplitude, frequency, and the short zero rate. In order to reduce the different people in different emotional expression of the impact of individual differences, the experiment process will extract emotional characteristics is normalized. The normalized will take the same three kind of emotional speech signal emotional features normalized put together, and after the normalized the emotional characteristics)

说明：  数据聚类，主要对于数据点进行聚类，输出节点分类情况和节点中心(Data clustering, mainly for data points clustering, output node classification and node Center)