WienerScalart

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.)

matlab的语音去噪程序，很好用的，有测试文件(Matlab speech denoising procedure is useful)

基于MFCC的GMM的说话人识别，是很好的语音处理程序(MFCC of the GMM based speaker recognition, speech processing is a very good program)

基于麦克风阵列的声源定位一维music算法源代码(Based on the microphone array beamformer one-dimensional music algorithm source code)

智能天线波束形成的LMS、SMI、RLS算法的程序(Smart antenna beamforming of LMS, SMI, RLS algorithm procedure)

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)

这是一个在线签名的程序，使用了AR实现算法和DTW算法(This is an online signature process, using the AR algorithm and DTW algorithm)

一个LMS自适应信号增强。输入信号是一个正弦 波将白噪声。 详细介绍了程序源代码(An LMS adaptive signal enhancement. The input signal is a sine wave with added white noise. The detailed description is in the program source code)

Chapter 2 Low Bit-Rate Speech Coding In this chapter an overview is given of speech coding techniques at several bit rates. Most of them use Linear Prediction. This overview is not meant to be complete its purpose is to make the reader somewhat familiar with Linear Predictive Coding which is necessary for a proper understanding of later chapters. Section 2.1 treats the subject of quantisation and coding. In section 2.2 a description of speech production and speech sounds is given. Coders based on linear prediction can be considered as being based on a simple speech production model. This model is explained in section 2.3. Section 2.4 describes various speech coding algorithms and techniques. Section 2.5 briefly describes some measures for the quality of coded speech.(In this chapter an overview is given of speech coding techniques at several bit rates. Most of them use Linear Prediction. This overview is not meant to be complete its purpose is to make the reader somewhat familiar with Linear Predictive Coding which is necessary for a proper understanding of later chapters. Section 2.1 treats the subject of quantisation and coding. In section 2.2 a description of speech production and speech sounds is given. Coders based on linear prediction can be considered as being based on a simple speech production model. This model is explained in section 2.3. Section 2.4 describes various speech coding algorithms and techniques. Section 2.5 briefly describes some measures for the quality of coded speech.)

比较经典的用lms算法实现自适应均衡器的matlab的程序，适合初学者学习(Comparison of the classical LMS algorithm with adaptive equalizer matlab procedures, suitable for beginners to learn)