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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)

熵值越大则每个符号包含的平均信息量越大。有研究发现，在有噪声的语音信号中，语音信号的熵和噪声信号的熵存在着较大的差异，对噪声信号来说在整个频带内分布相对平坦，熵值小，语音信号集中在某些特定频段内，熵值大。因此利用这个差异可以区分噪音段和语音段。(The greater the entropy is, the greater the average information of each symbol is. It is found that, in noisy speech signals, the entropy of speech signals and the entropy of noise signals are quite different. For noisy signals, the distribution is relatively flat in the whole frequency band, and the entropy value is small. The speech signal is concentrated in some specific frequency bands, and the entropy value is large. So the difference can be used to distinguish the noise segment and the speech segment.)

基于DTW算法实现孤立数字及连续数字语句的识别，识别率还可以（内含语音数据集）(Based on DTW algorithm, isolated number and continuous number statements can be recognized, and the recognition rate can also be (including voice data set))

辅助计算SDR 评测语音质量参数 评估语音质量的好坏(caculate SDR)

关于lms经典算法汇总，包括lms,nlms,LMS_Equalizer,Basic_LMS,LMS toolbox等等。(classical algorithm for matrix lms, including lms, nlms. LMS_Equalizer, Basic_LMS, LMS toolbox, and so on.)

1.单通道语音增强算法； 2.一种能够快速跟踪噪声变化的，基于噪声统计特性的噪声估计算法，结合谱减法进行消除噪声； 3.算法复杂度适中，可以满足实时性(1. Single-channel speech enhancement algorithm 2. A fast-track noise can change, based on the noise statistical properties of the noise estimation algorithm, combined with spectral subtraction to eliminate noise 3. Algorithm moderate complexity to meet the real-time)

实验示例是基于语音中的mfcc，语音倒谱特征来进行聚类，先利用训练样本来计算训练样本聚类中心（用到了lbg算法），之后再进行分类。 注意：使用代码时需要自己更改文件路径。(This example is based on the MFCC in speech and the feature of speech Cepstrum to cluster. First, the training sample is used to calculate the training sample clustering center (using the LBG algorithm), then the classification is then carried out.)

function [Coefficients,residual,pitch,Gain,parcorCoefficients,stream,B,crossmat,error_stream] = lpcanalysis(speechtodecode,samplerate,L,windowtime,preemp)

提取MFCC的matlab代码，用于提取MFCC-梅尔倒普系数(MFCC extraction of matlab code, for the extraction of MFCC-Mel Pu inverted coefficient)

采用矢量量化对语音信号量化，并与标量量化对比分析，绘制了语音信号的概率密度曲线(The use of vector quantization for speech signal quantization and scalar quantization and comparative analysis, rendering the speech signal probability density curve)