GSC

采样小波包分解语音信号，分解为3层，并求出分解系数(Speech signal using wavelet packet decomposition，decomposing 6 level，and attain decomposition coefficient)

文中研究了6种常用数字调制信号识别的特征参数集，并采用决策树判别方法进行分类识别。仿真结果表明，在SNR≥5dB时，识别正确率在99 以上，且当SNR≥20dB时，识别正确率达到100 。其特点是，算法简单，识别正确率高，达到了自动分类识别的目的，并有利于实现识别的实时化。(In this paper, we study the set of characteristic parameters of the six kinds of commonly used digital modulation signal recognition, and decision tree method for classification. The simulation results show that SNR 鈮� 5dB, the correct rate more than 99 , and when SNR 鈮� 20dB, the correct rate of 100 . Which is characterized by simple algorithm to identify the correct rate, to achieve the purpose of automatic classification and recognition, and help to identify real-time.)

3、语音特征提取与分类 首先, 待识别语音转化为电信号后输入识别系统, 经过预处理后用数学方法提取语音特征信号, 提取出的语音特征信号可以看成该段语音的模式。(3, the voice feature extraction and classification First, to be recognized voice into electrical signals input recognition system, after pretreatment with a mathematical method to extract speech characteristic signal extracted speech characteristic signal can be seen as the voice segment model.)

matlab代码，语音的压缩和编码，主要利用了心理声学模型，得到低比特编码 和高质量语音(matlab code, voice compression and coding, psychoacoustic model used by low-bit encoding and high-quality voice)

多抽样率信号处理，包括邓氏关联度、绝对关联度、斜率关联度、改进绝对关联度,完整的基于HMM的语音识别系统。( Multirate signal processing, Including Deng s correlation, absolute correlation, correlation of slope, improved absolute correlation, Complete HMM-based speech recognition system.)

labview做的分析语音信号的程序，做简单的频谱分析，识别(failed to translate)

espeak语音生成库，内附有移植使用文档《espeak移植使用》(espeak speech generation library, containing transplanted using the document espeak transplant use )

一个盲信号分离的卷积方法，适合分离实际卷积混合的模型，希望对初学者有帮助。(Convolution of a blind signal separation method, suitable for separation of the real convolution mixture model, want to help beginners.)

语音信号处理课件：语音基础知识、短时时域分析、频域分析、倒谱域分析、线性预测技术、语音增强、语音识别(Speech signal processing courseware: basic knowledge of speech, short-term time domain analysis, frequency domain analysis, cepstrum domain analysis, linear prediction technology, speech enhancement, speech recognition)

本文介绍了基于麦克风阵列的声源定位算法，给初学者有很大的帮助(This article describes the microphone array based sound source localization algorithm, a great help for beginners)