Results 251 to 260 of about 37,062,975 (290)
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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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Time series analysis in the frequency domain
IEEE Transactions on Signal Processing, 1999This correspondence presents a parametric frequency domain identification algorithm for autoregressive moving average (ARMA) processes that does not suffer from spectral leakage errors. It is based on an extended transfer function model that takes into account the begin and end effect of the finite data record.
Rik Pintelon, Johan Schoukens
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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 analysis of musical signals
Proceedings of the IEEE, 1996The major time and frequency analysis methods that have been applied to music processing are traced and application areas described. Techniques are examined in the context of Cohen's class, facilitating comparison and the design of new approaches. A trumpet example illustrates most techniques.
William J. Pielemeier +2 more
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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 ...
openaire +2 more sources
2017
Signals are in general nonstationary. A complete representation of nonstationary signals requires frequency analysis that is local in time, resulting in the time-frequency analysis of signals. The Fourier transform analysis has long been recognized as the great tool for the study of stationary signals and processes where the properties are ...
Lokenath Debnath, Firdous A. Shah
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Signals are in general nonstationary. A complete representation of nonstationary signals requires frequency analysis that is local in time, resulting in the time-frequency analysis of signals. The Fourier transform analysis has long been recognized as the great tool for the study of stationary signals and processes where the properties are ...
Lokenath Debnath, Firdous A. Shah
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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
openaire +1 more source