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The multitaper method overcomes some of the limitations of non-parametric Fourier analysis. When applying the Fourier transform to extract spectral information from a signal, we assume that each Fourier coefficient is a reliable representation of the amplitude and relative phase of the corresponding component frequency. This assumption, however ...
1 Fourier analysis. ... 2.3 Special functions and generalized functions. ... Download as PDF; Printable version; In other projects Wikidata item;
The trade-off between the compaction of a function and its Fourier transform can be formalized in the form of an uncertainty principle by viewing a function and its Fourier transform as conjugate variables with respect to the symplectic form on the time–frequency domain: from the point of view of the linear canonical transformation, the ...
The part of this equation involving ^ can be computed directly using the wave function at time , but to compute the exponential involving ^ we use the fact that in frequency space, the partial derivative operator can be converted into a number by substituting for , where is the frequency (or more properly, wave number, as we are dealing with a ...
One of the more popular multidimensional transforms is the Fourier transform, which converts a signal from a time/space domain representation to a frequency domain representation. [1] The discrete-domain multidimensional Fourier transform (FT) can be computed as follows:
The function to be transformed is first multiplied by a Gaussian function, which can be regarded as a window function, and the resulting function is then transformed with a Fourier transform to derive the time-frequency analysis. [1] The window function means that the signal near the time being analyzed will have higher weight.
Spectrograms produced from applying a rec-STFT on a function consisting of 3 consecutive cosine waves. (top spectrogram uses smaller B of 0.5, middle uses B of 1, and bottom uses larger B of 2.) From the image, when B is smaller, the time resolution is better. Otherwise, when B is larger, the frequency resolution is better.
In applied mathematics, the sliding discrete Fourier transform is a recursive algorithm to compute successive STFTs of input data frames that are a single sample apart (hopsize − 1). [1] The calculation for the sliding DFT is closely related to Goertzel algorithm .