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2012
In the previous chapter, we mentioned that one of the main limitations of the Fourier transform is that it does not have time resolution. For calculating the Fourier transform, we assume that the signal is stationary and, consequently, that the activity at different frequencies is constant throughout the whole signal.
Walter J. Freeman, Rodrigo Quian Quiroga
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In the previous chapter, we mentioned that one of the main limitations of the Fourier transform is that it does not have time resolution. For calculating the Fourier transform, we assume that the signal is stationary and, consequently, that the activity at different frequencies is constant throughout the whole signal.
Walter J. Freeman, Rodrigo Quian Quiroga
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2016
This chapter is a logical continuation of the previous chapter on signal changes. The consideration of nonstationary signals requires an assortment of analysis tools, to highlight different aspects of importance. Many scientific and technical activities are interested on such, for medical purposes, for earthquake study, for machine maintenance, for ...
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This chapter is a logical continuation of the previous chapter on signal changes. The consideration of nonstationary signals requires an assortment of analysis tools, to highlight different aspects of importance. Many scientific and technical activities are interested on such, for medical purposes, for earthquake study, for machine maintenance, for ...
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THE LINEAR TIME FREQUENCY ANALYSIS TOOLBOX
International Journal of Wavelets, Multiresolution and Information Processing, 2012The Linear Time Frequency Analysis Toolbox is a MATLAB/Octave toolbox for computational time-frequency analysis. It is intended both as an educational and computational tool. The toolbox provides the basic Gabor, Wilson and MDCT transform along with routines for constructing windows (filter prototypes) and routines for manipulating coefficients.
Soendergaard, Peter +2 more
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IEEE Signal Processing Magazine, 1999
It has been well understood that a given signal can be represented in an infinite number of different ways. Different signal representations can be used for different applications. For example, signals obtained from most engineering applications are usually functions of time. But when studying or designing the system, we often like to study signals and
null Shie Qian, null Dapang Chen
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It has been well understood that a given signal can be represented in an infinite number of different ways. Different signal representations can be used for different applications. For example, signals obtained from most engineering applications are usually functions of time. But when studying or designing the system, we often like to study signals and
null Shie Qian, null Dapang Chen
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2017
Fourier-analysis provides a description of a given data set in terms of monochromatic oscillations without any time information. It is thus mostly useful for stationary signals. If the spectrum changes in time it is desirable to obtain information about the time at which certain frequencies appear. This can be achieved by applying Fourier analysis to a
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Fourier-analysis provides a description of a given data set in terms of monochromatic oscillations without any time information. It is thus mostly useful for stationary signals. If the spectrum changes in time it is desirable to obtain information about the time at which certain frequencies appear. This can be achieved by applying Fourier analysis to a
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2011
Let z be a signal in \(L^2(\mathbb{Z}_{N})\). Then we say that z is time-localized near n0 if all components z(n) are 0 or relatively small except for a few values of n near n0. An orthonormal basis B for \(L^2(\mathbb{Z}_{N})\) is said to be time-localized if every signal in B is time-localized.
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Let z be a signal in \(L^2(\mathbb{Z}_{N})\). Then we say that z is time-localized near n0 if all components z(n) are 0 or relatively small except for a few values of n near n0. An orthonormal basis B for \(L^2(\mathbb{Z}_{N})\) is said to be time-localized if every signal in B is time-localized.
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Time-frequency correlation analysis
International Forum on Strategic Technology 2010, 2010This article introduces time-frequency correlation function. This function allows determine relationship at different frequencies between two input signals. Using time-frequency correlation function makes correlation leakage detection method more effective and accurate in complex condition with high background noise without necessary of using frequency
V.S. Avramchuks, null Tran Viet Chau
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The beginning of time-frequency analysis
The Journal of the Acoustical Society of America, 2022The Reflections series takes a look back on historical articles from The Journal of the Acoustical Society of America that have had a significant impact on the science and practice of acoustics.
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Time-Frequency Analysis of Musical Instruments
SIAM Review, 2002Summary: This paper describes several approaches to analyzing the frequency, or pitch, content of the sounds produced by musical instruments. The classic method, using Fourier analysis, identifies fundamentals and overtones of individual notes. A second method, using spectrograms, analyzes the changes in fundamentals and overtones over time as several ...
Alm, Jeremy F., Walker, James S.
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Covariant Time-Frequency Analysis
2003We present a theory of linear and bilinear/quadratic time-frequency (TF) representations that satisfy a covariance property with respect to “TF displacement operators” These operators cause TF displacements such as (possibly dispersive) TF shifts and dilations/compressions.
Franz Hlawatsch +2 more
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