VQ_LBG_newdistortion

基于hmm的识别程序 识别数字0-9，识别率全对(speech recognition based on hmm number 0-9 all right)

基于Matlab的织物疵点检测和识别系统(Fabric defect detection and identification systems based on Matlab)

speech file to be analyzed Format : binary file 16 bit-samples 240 samples per frame (speech file to be analyzed Format : 16 bit binary file-samples of 240 samples per fram e)

HMM隐马尔科夫模型源码，实现模型的训练(Hidden Markov Model HMM source, the realization of model training)

本书系统地阐述了语音信号处理的原理、方法、技术和应用，同时给出了部分内容对应的MATLAB仿真源程序。全书共12章，第1章至第7章是基本理论部分，包括语音信号的数字模型、语音信号的短时时域分析和频域分析、语音信号的同态处理、语音信号线性预测分析和矢量量化；第8章至第12章是应用部分，包括语音编码、语音合成、语音识别、语音增强和语音处理的实时实现。本书内容全面，重点突出，原理阐述深入浅出，注重理论与实际应用的结合，可读性强。(This book describes the speech signal processing principles, methods, techniques and applications, and gives the corresponding part of the contents of the MATLAB simulation source. The book is 12 chapters, Chapter 1 to Chapter 7 is the basic theoretical part, including voice signal digital model, speech signal analysis in time domain and frequency domain analysis, speech signal homomorphic processing, speech signal analysis and linear prediction vector quantified Chapter 8 to Chapter 12 is the application of parts, including speech coding, speech synthesis, speech recognition, speech enhancement and voice processing, real-time implementation. The book is comprehensive, focused and Rationale layman, focusing on the combination of theory and practical application, readable.)

一个通过实际应用的“ 人工鱼群算法”，效果很好的。(Through the practical application of the " artificial fish-swarm algorithm," good effects.)

采用广义旁瓣抵消（GSC）自适应波束形成方法实现时域和频域滤波，采用LMS自适应算法，最终实现语音增强。（文件中包含纯净语音及不同信噪比的带噪语音）(Generalized sidelobe canceller (GSC) adaptive beamforming method to achieve time-domain filtering using the LMS adaptive algorithm, and ultimately the speech enhancement)

lpc提取共振峰和基频，适合初学语音的同学使用，增强认识(lpc formant extraction and baseband, suitable for beginners to use the voice of the students, and enhance understanding)

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.)

多信号分离MUSIC的经典学习程序 对初学者帮助很大(it is a baisc and useful code of the multiple signal classfication for the beginners.)