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语音识别中的通道自相关函数，matlab版。自己编的，通过测试-Speech Recognition access autocorrelation function, Matlab version. His series, testing
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自相关函数的应用，进行基音检测，在MATLAB下进行仿真-Application of autocorrelation function for pitch detection, carried out in the MATLAB simulation
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this programme shows that how to calculate the autocorrelation and lpc coeffecients of a speech signal-this programme shows that how to calculate the autocorrelation and lpc coeffecients of a speech signal..
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语音信号处理中基音周期提取算法。运用传统的自相关方法。并加入了三电平削波的处理。-Speech Signal Processing pitch extraction algorithm. Using the traditional autocorrelation method. And joined the three-level clipping processing.
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matlab写的关于语音信号的时域分析，包括能量，幅度，自相关函数，过零率等的程序-The time domain analysis of speech signal with Matlab, including energy, amplitude, autocorrelation function, and zero crossing rate.
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语音信号的线性预测（LPC）方程的自相关解法及其matlab实现。-Speech signal linear prediction (LPC) the autocorrelation Solution of the equation matlab.
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短时自相关和短时平均幅度差计算基音周期的matlab程序，附带有原语音信号-Short-term autocorrelation and short-term average deviation of pitch period matlab program, comes with the original speech signal
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本实验要求掌握时域特征分析原理，并利用已学知识，编写程序求解语音信号的短时过零率、短时能量、短时自相关特征，分析实验结果，并能掌握借助时域分析方法所求得的参数分析语音信号的基音周期及共振峰。基于MATLAB的语音信号时域特征分析-In this study, the analysis required to master the principles of time-domain characteristics, and use the learned knowledge to write pr
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语音信号的短时分析，主要包括：分帧、短时能量、短时平均幅度、短时过零率、短时自相关函数、短时幅度差、倒谱、复倒谱、lpc系数、lpc谱估计等(The short-time analysis of speech signal mainly includes: frame, short-time energy, short time average amplitude, short-time zero crossing rate, short-time autocorrelation functio
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corr_signal.m文件可以通过自相关运算，将文件名为test的语音文件的音阶读出。
work1.m是一个GUI函数，图形化界面检测test.wav的音阶。
可用作展示自相关简单应用的过程。(The corr_signal.m file can read the phonetic order of the voice file named test by autocorrelation operation.
Work1.m is a GUI function, the graphic
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This program computes autocorrelation for a speech .wav file
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语音信号的时域、频域与倒谱域分析。
1.分析一帧清音和浊音的自相关函数和倒谱系数
2.用Matlab画出该段语音的时域波形、短时能量、短时平均幅度、短时过零率、短时过电平率
3.选择一帧无声、清音和浊音的语音，用Matlab画出它们的对数幅度谱(Time domain, frequency domain and cepstrum domain analysis of speech signals.
1. Analyze the autocorrelation function and c
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