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Speech_signal_short_time_analysis
- 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
语音信号的短时分析
- 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
Endpoint-detection-of-speech-signal
- 语音信号的端点检测分析，有短时能量分析,短时过零率分析,和终点检测-Endpoint detection of speech signal, a short-term energy analysis, short-term rate of zero, and the end point detection
F2_6764
- 端点检测是指用数字处理技术来找出语音信号中的各种段落（如音素、音节、词素、词等）的始点和终点的位置。语音段起止端点检测是语音分析、语音合成和语音识别中的一个必要环节。传统的端点检测方法是从wav文件中获取语音采样，将其分帧并计算短时能量和过零率参数，然后进行端点检测。这种工作方式被称为离线处理方法 ，无法实现语音信号的实时处理，对于语音信号分析具有一定的局限性。本文通过开发ActiveX控件，在MATLAB环境下将其嵌入到figure窗口中，以GUI程序的方式使用，实现语音信号端点检测的实时处
yuyinxinhaojiance
- 基于MATLAB的语音信号端点检测，短时能量分析，短时过零率，和GUI界面实现-aaaaa
new
- 语音信号的短时能量分析程序，可以从波形中清楚地看出-Short-time energy of speech signal analysis program that can be clearly seen from the waveform
Speech-signal-short-time-analysis
- 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 a
speech-
- 语音信号处理短时分析的实验以实验报告，处理有语音信号的短时幅值、自相关函数、短时能量和分辨清音和浊音-The analysis of the short speech signal processing experiments to test report, with the short speech signal amplitude, the autocorrelation function, short-term energy and distinguish the voiced and u
short-term-analysis-
- 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, sh
yuyinxinhaochuli
- 语音信号处理实基于MATLAB的语音信号时域特征分析 要求： 按所学相关语音处理得的知识，通过网上学习、资料查阅，自己设计程序，给出某一语音信号的短时过零率、短时能量、短时自相关特征的分析结果，并借助时域分析方法检测所分析语音信号的基音周期-yu yin xin hao chu li
Speech-signal-short-time-analysis
- 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-Short-time speech signal analysis, mainly including: sub-frame, short-time energy, short-term average, short-time zero-crossing rate, short-time auto-correlation function, short-t
timeDomainAnalysis
- 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
mian
- 语音信号的时域分析，有短时能量，短时平均幅度，短时过零率，清音自相关序列，浊音自相关序列，傅里叶分析。-Speech signal time-domain analysis, short-time energy, short-term average, short-term zero-crossing rate, voiceless autocorrelation sequence, voiced autocorrelation sequence, Fourier analysis.
edp
- 语音信号的是与分析 有短时能量短时过零等分析-Speech signal analysis There is a short energy short-time zero crossing analysis, etc
yuyinxinhaochuli
- 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
语音信号处理
- 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 dom
matlab
- 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
short enery
- 在窗长为80和160个样点的情况下，分别采用矩形窗和哈明窗编程计算该段语音信号的短时平均能量，绘出短时平均能量曲线，并进行观察和比较(the window length is 80 and 160 sample points, short time average energy respectively using rectangular window and Hamming window programming calculation of the speech signal, short-t
short_energy
- 语音信号的短时能量求取MATLAB代码，语音信号分析的基础教程(MATLAB code for short time energy of speech signal and a basic course for speech signal analysis)
audio_tezheng
- 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