Description: 本文提出了一种利用二进制小波变换和短时自相关函数相结合的算法来检测语音信号的基音周期。它克服了单纯的短时自相关函数法对含噪语音检测时阈值难以确定的缺陷。-This paper proposes the use of a binary wavelet transform and short-time auto-correlation function of combining voice algorithm to detect letter Pitch No. cycle. It overcomes the simple short autocorrelation function of the noisy voice detection threshold when it is difficult to determine deficiencies. Platform: |
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Author:李一 |
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Description: Speech analysis and parameter extraction Short-term analysis, frames and windows Time-domain analysis: energy, zero-crossings, statistic parameters, autocorrelation
Frequency-domain analysis: spectra and spectrograms Cepstral analysis Linear prediction analysis Pitch and formant estimation
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Author:栗子 |
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Description: 自相关虽然能够判断清/浊音，并估计浊音的基音周期，但是其运算量很大，用乘法运算所需时间较长。为了避免乘法，采用短时平均幅度差函数（AMDF），验证AMDF与自相关有类似的作用，即平均幅度差函数能够代替自相关函数进行语音分析。-Although autocorrelation can differ from the Voiced/Unvoiced and voiced the pitch period estimate, but the operation is large enough, with the multiplication time was needed. In order to avoid the multiplication using the short time average magnitude difference function (AMDF), verify the AMDF and autocorrelation have a similar effect, that the average magnitude difference function can replace the voice of the autocorrelation function. Platform: |
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Author:吴艳花 |
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Description: 详细说明：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 绝对保证质量，是保研后导师布置的一些基础程序-Details: short-time speech signal analysis, including: framing, short-term energy, short-term average rate, short-time zero crossing rate, short-time autocorrelation function, short-term magnitude difference, cepstrum, complex cepstrum, lpc coefficient, lpc absolute guarantee of the quality of spectral estimation, is the security arrangement of some of the research base after the mentor program Platform: |
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Author:林溪 |
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Description: 完成对一段语音信号的短时分析，语音信号要包括清音、浊音和无声段，求其短时平均能量、短时平均过零率、短时自相关函数和短时功率谱；-Complete short-term analysis of a voice signal, the specific requirements: a voice signal to include the voiceless, voiced, and silent segments 2, find its short-term average energy, short-time average zero crossing rate, short-term autocorrelation function and short-term power spectrum 3, drawing, and the differences in the comparative analysis of three types of audio signals in the four short-term parameters 4, estimated using short-time autocorrelation voiced fundamental frequency using matlab to write and draw Platform: |
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Author:houjam |
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Description: 首先通过手机录制一段名为“yuyin.wav”的语音，然后通过MATLAB编写程序对这段语音进行分析，分别求出这段语音短时能量、短时过零率、短时幅度以及清音和浊音的自相关函数-First phone to record a section called " yuyin.wav" the voice, and then through the MATLAB programming analysis of this voice, this voice were calculated short-term energy, short-time zero-crossing rate, short-term rate and the voiceless and voiced the autocorrelation function Platform: |
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Author:Colin |
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Description: 基于Matlab的语音信号的时域分析，包括短时平均能量，短时平均过零率，短时自相关系数，短时平均幅度-Based on Matlab speech signal time-domain analysis, including short-term average energy, short-time average zero-crossing rate, short-term autocorrelation coefficient, the short-term average Platform: |
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Author:海日水 |
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Description: 语音处理是信号与信息处理的重要内容之一，通过本课程设计，使学生理解数字信号处
理的有关理论和方法在语音处理中的具体应用。课程设计的目的归纳如下：
1、掌握语音信号的特点；
2、掌握语音处理的基本理论和方法；
3、掌握基于Matlab编程实现语音的获取、显示、频谱分析、短时能量、短时自相关以及倒
谱复倒谱的分析方法；
4、掌握语音基音频率及共振峰频率的检测方法。-Signal and speech processing is an important part of information processing, through the curriculum design, so that students understand the digital signal
The theory and methods of management in voice processing in the specific application. Curriculum is designed to be summarized as follows:
A master speech signal characteristics
2, master the basic theory and speech processing methods
3, master Matlab programming based voice access, display, spectrum analysis, short-time energy, short-term autocorrelation and inverted
Spectral analysis of the complex cepstrum method
4, master voice pitch frequency and formant frequency detection method.
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Author:立枣酒 |
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Description: 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 programs to solve short-time speech signal zero-crossing rate, short-time energy, short-term autocorrelation features, analysis of the experimental results, and can grasp the time domain by means of analytical methods pitch the determined parameter analysis of the speech signal and formant. Based on time-domain characteristics of the speech signal analysis MATLAB Platform: |
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Author:田飞 |
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Description: 采用MATLAB 2015b综合运用各种函数调用等来实现语音信号时域的短时能量，短时过零以及短时自相关函数分析和频域的傅里叶变换，功率谱估计，短时傅里叶变换，小波分析，语音信号的短时综合，语谱图各种分析，充分了解熟悉语音信号的相关分析方法。(This experiment uses the MATLAB 2015 b, the integrated use of various kinds of function call to realize speech signal in time domain of short-time energy and short-time zero crossing and short-time autocorrelation function analysis and frequency domain Fourier transform, power spectrum estimate, short-time Fourier transform, wavelet analysis, the short synthesis speech signal, the spectra of various analysis, fully understand the familiar voice signal correlation analysis method.) Platform: |
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Author:Liu0_
|
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Description: MATLAB语音信号短时分析，包括自相关，能量谱，过零率等(Short time analysis of MATLAB speech signals, including autocorrelation, energy spectrum, zero crossing rate, etc.) Platform: |
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Author:光之风 |
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Description: 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 cepstrum coefficient of unvoiced and voiced sounds
2. Use Matlab to plot the time domain waveform of the speech segment, short-term energy, short-term average amplitude, short-term zero-crossing rate, and short-term overshoot ratio.
3. Select an unvoiced, unvoiced, and voiced sound and use Matlab to plot its logarithmic amplitude spectrum) Platform: |
Size: 146432 |
Author:jacek |
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Search - short time autocorrelation matlab