"无损压缩算法代码包"是大家学习压缩技术的入门好帮手。

HTS-2.0RC2，HTS语音合成器的最新测试版。首先需要安装HTK3.4。与HTS1.1相比功能更加强大。嵌入式训练算法更新。-HTS- 2.0RC2 HTS voice synthesizer to the latest test version. HTK3.4 first need to install. HTS1.1 compared with the more powerful functions. Embedded training algorithm update.

语音信号的基音检测算法（ amdf法：平均幅度差算法）-pitch detection

一个语音开发包动态连接库，很容易使用。- A pronunciation development package dynamic connection storehouse, is very easy to use.

极小边际熵等价于叉四阶累积量的平方和最小。通过迭代使四阶累积矩阵对角化，实现交叉四阶累积量的平方和的极小化。他是语音识别的重要预处理算法-minimum entropy equivalent to the marginal four bands fork cumulative amount of square and smallest. Through iterative four bands so that the cumulative matrix diagonalization, four bands to achieve cross-cumulative amount of square and the minimization. He is an important voice recognition algorithm pretreatment

语音识别，跳用JAVA里写的类，a simple test project-voice recognition, dancing with Java, written category, a simple test project

OPNET dynproc_new_lab1_ref-root_proce-OPNET dynproc_new_lab1_ref-root_process

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))

Speech Quantization_Compare SNRs of speech cs5 with mu-law companding and uniform quantization -Speech Quantization_Compare remnants of spee ch cs5 with mu-law companding and uniform quant ization

PCM编解码源程序 很好的程序。谢谢大家进行参考。-PCM codec good source procedures. Thank you for your reference.

大规模连续语音识别工具，比较实用。可以做实际建模-large-scale continuous speech recognition tools, more practical. Modeling can do practical