ADPCM（自适应脉冲编码解码模块）将提供给大家…

将音频编码为Wav格式的DirectShow filter :WaveDest PCMAuido -> Wave Audio -Wav audio encoding format for the DirectShow filter: WaveDestPCMAuido-

语音频率分析使用matlab实现，很经典了-Voice frequency analysis using matlab implementation, it is the classic of the Hehe

录音部分是参照vckbase的录音api代码,结合了一小段socket(TCP)就可以用来单方说话录音了,程序分两部分一部分是(录音机+网络发送代码),一部分是(接收数据+播放波形音乐代码),由于程序只是为了自己用,很多地方没有注意错误的识别,结构也比较乱,本程序当初最头疼的就是控制损耗内存,结果用了双缓存来存储波形数据来交替的存储/清除. -part of the recording light vckbase recording api code combination of a small socket (TCP) can be used for recording a unilateral words, part of the procedure is divided into two parts (recorders network code), part of the (receiving data waveform music broadcast code), as a result of procedures only for their own use. many places no attention to the wrong identification, the structure was chaotic, most of the first procedure is a headache for the control of memory loss, Results of the double-caching to store waveform data to the turn of the storage/removals.

8kHz采样语音的基音检测程序，VC6.0环境下运行，附有matlab画图程序-8kHz sampling voice pitch detection procedures, VC6.0 environment running matlab with Paint program

计算声音信号的总声压级，1/3/倍频程声压级，总响度，特性响度，尖锐度-Calculation of the total sound pressure level of the voice signal, 1/3/octave sound pressure level, the total loudness, loudness characteristics, acute severe

语音识别中的MFCC参数提取的java源码和FFT的java源码-Speech Recognition MFCC parameter extraction of the java source and FFT of the java source code

语音识别系统：根据采集的语音进行文字的输出！-Speech Recognition System: Based on collected voice to text output!

c++实现HMM，向前向后算法，Viterbi算法，Baum-Welch算法。其中包括用c++定义的HMM数据结构。全部是cpp和h的文件-c achieve HMM, forward backward algorithm, Viterbi algorithm, Baum-Welch algorithm. C including the use of the HMM definition data structure. Cpp all the documents and h

LMS算法实现自适应滤波 clear close all clc N=10000 设置仿真长度 信号产生参数设定 a1=-0.195 a1=-1.5955 a2=0.95 R0=[1,a1,a2 a1,1+a2,0 a2,a1,1] p=[1,0,0] r=inv(R0)*p 计算理论自相关函数 R=[r(1),r(2) r(2),r(1)] 生成理论自相关矩阵 p1=[r(2),r(3)] 生成互相关 h=inv(R)*p1 计算维纳解 Jmin=r(1)-h *p1 计算维纳解时最小均方误差 u=1/sum(eigs(R- LMS算法实现自适应滤波 clear close all clc N=10000 设置仿真长度 信号产生参数设定 a1=-0.195 a1=-1.5955 a2=0.95 R0=[1,a1,a2 a1,1+a2,0 a2,a1,1] p=[1,0,0] r=inv(R0)*p 计算理论自相关函数 R=[r(1),r(2) r(2),r(1)] 生成理论自相关矩阵 p1=[r(2),r(3)] 生成互相关 h=inv(R)*p1 计算维纳解 Jmin=r(1)-h*p1 计算维纳解时最小均方误差 u=1/sum(eigs(R))

对不同特征进行融合，得到更好的语音特征，识别率高。-Integration of different features, better voice features, a high recognition rate.