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STFT和ST变换的基本函数,和一个stft的一个例程-This code computes the Stockwell transform (S-Transform) of a one dimensional series without for loop, thereby making it computationally fast and simple. S-Transform was proposed in 1996. A nice tutorial on S-Transform can
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关于短时傅里叶变换进行时间-频率分析的代码,用MATLAB编写的代码。-On short-time Fourier transform time- frequency analysis of the code, the code written in MATLAB.
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Rectangular mask short-time Fourier transform
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MATLAB短时傅里叶变换和morlet小波变换-short-time Fourier transform and morlet wavelet transform based on matlab
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短时傅里叶变化,有结果运行截图,适合初学者-Short-time Fourier transform, the results of running screenshots
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xiaobo.m小波变换方法处理时频信号
duanshifuliye.m短时傅里叶变换方法处理时频信号
three.m三次样条插值
newton.m牛顿迭代法
stokes.c斯托克斯波的生成-xiaobo.m(Wavelet transform method for processing time- frequency signals)
duanshifuliye.m(The short- time Fourier transform method deals with t
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短时傅立叶变换及其逆变换源程序,英文详细注释,希望对大家有所帮助。-source program of Short-time Fourier Transform and iSTFT,detailed notes in English. Hope will help you.
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一种短时傅里叶算法,对研究短时傅里叶和其他相关算法很有帮助-A short-time Fourier algorithm is useful for studying short-time Fourier and other related algorithms
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Short Time Fourier Transform
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利用工具箱实现短时傅里叶变换,并画出等高线图(program for short time Fourier transform using toolbox)
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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
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对非平稳信号进行分段、截取,再做短时傅里叶变换,分析其频谱特性,并找到各个时间节点的频率特性,以便分析不同事件段内的声音特征。(The non-stationary signals are segmented and intercepted, and then the short-time Fourier transform is used to analyze their frequency spectrum characteristics, and the frequency charact
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对非平稳信号进行分段、截取,将非平稳信号转化为平稳信号,再做短时傅里叶变换,分析其频谱特性,并找到各个时间节点的频率特性,以便分析不同事件段内的声音特征。(The non-stationary signals are segmented and intercepted, and then the short-time Fourier transform is used to analyze their frequency spectrum characteristics, and the fre
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对非平稳信号进行分段、截取,再对处理过后的短时信号做短时傅里叶变换,分析其频谱特性,并找到各个时间节点的频率特性,以便分析不同事件段内的声音特征。(The nonstationary signals are segmented, and then the interception, the short-time signal after processing the short-time Fourier transform, the analysis of the spectrum charac
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对非平稳信号进行分段、截取,再对处理过后的短时信号做短时傅里叶变换,分析其频谱特性,并找到各个时间节点的频率特性,以便分析不同事件段内的声音特征,达到区分不同声音的目的。(The nonstationary signals are segmented, and then the interception, the short-time signal after processing the short-time Fourier transform, the analysis of the spe
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对非平稳信号进行分段、截取,再对处理过后的短时信号做短时傅里叶变换,分析其频谱特性,并找到各个时间节点的频率特性,以便分析不同事件段内的声音特征,一图像的形式表达出,最终达到区分不同声音的目的。(The nonstationary signals are segmented, and then the interception, the short-time signal after processing the short-time Fourier transform, the analysi
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对非平稳信号进行分段、截取,再对处理过后的短时信号做短时傅里叶变换,分析其频谱特性,并找到各个时间节点的频率特性,以便分析不同事件段内的声音特征,以图像的形式表达出,最终达到区分不同声音的目的。(The nonstationary signals are segmented, and then the interception, the short-time signal after processing the short-time Fourier transform, the analysi
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MATLAB实现短时傅里叶变换,短时傅里叶逆变换。直接配置好参数就可以用,实用性强。(MATLAB implementation of short time Fourier transform, short time Fourier inverse transform. Direct configuration of parameters can be used, practically.)
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该方法的关键是经验模式分解,它能使复杂信号分解为有限个本征模函数(Intrinsic Mode Function,简称IMF),所分解出来的各IMF分量包含了原信号的不同时间尺度的局部特征信号。经验模态分解法能使非平稳数据进行平稳化处理,然后进行希尔伯特变换获得时频谱图,得到有物理意义的频率。与短时傅立叶变换、小波分解等方法相比,这种方法是直观的、直接的、后验的和自适应的,因为基函数是由数据本身所分解得到。由于分解是基于信号序列时间尺度的局部特性,因此具有自适应性。(The key of thi
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信号处理中短时傅里叶变换(STFT)的实现,可用于实时系统,实时语音处理或者分段的FFT计算,亲测可用,拿走不谢!(The implementation of short time Fourier transform (STFT) in signal processing can be used for real-time system, real-time speech processing or segmented FFT calculation!)
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