mel-pca

lms alghoiritm method adaptive system

一种加权K近邻语音情感识别程序，包括部分柏林语音情感库(A Weighting K-nearest neighbor speech emotion recognition program, including some libraries Berlin Speech Emotion)

基于LMS(最小均方误差算法)的自适应滤波 基于LMS(最小均方误差算法)的自适应滤波(based on the LMS (MMSE) algorithm based on the LMS adaptive filtering (minimum mean square error algorithm) the adaptive filtering based on the LMS (minimum mean square error algorithm) Adaptive Filter)

传统语音增强算法，谱减法和MCRC算法，matlab源代码。(Traditional speech enhancement algorithm, spectral subtraction and MCRC algorithm, matlab 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.)

实现多个人的说话人识别，基于gmm模型，用mfcc参数训练(voice conversion based on gmm)

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)

C语言fft和ifdct的函数，可以直接使用(C language functions fft and ifdct can be directly used)

一个完整的基于Matlab的DTW模型算法及高效算法程序，能快速识别数字0-9,运行testdtw即可。(An excellent MATLAB program for the algorithm as a DTW model. It can recognise the number of 0-9. Try it, just run testdtw.)

微软研究院的说话人识别工具包，包括GMM-UBM、I-Vector。其中demo_gmm_ubm_artificial.m和demo_ivector_plda_artificial.m为生成模拟特征参数进行训练与识别的教学示例，十分适合初学者学习说话人识别基础算法。具体使用方法请看内部文档。(Microsoft Research s speaker recognition toolkit, including GMM-UBM, I-Vector. Demo_gmm_ubm_artificial.m and demo_ivector_plda_artificial.m which generates an analog characteristic parameters for example teaching training and recognition, very suitable for beginners to learn the basic algorithm for speaker recognition. See the specific use of internal documents.)