Discrete wavelet transform; how to interpret approximation and detail coefficients? Ask Question Asked 8 years, 1 month ago Modified 2 years, 9 months ago

Continuous wavelet transform is suitable for a scalogram because the analysis window can be sized and placed at any position. This flexibility allows for the generation of a smooth image in …

I am in need of an open source library for computing Fast wavelet transforms (FWT) and Inverse fast wavelet transforms (IFWT) - this is to be part of a bigger code I am currently writing. The thi...

Since wavelet transform can be used for different kinds of data, not only time-domain signals, we use the word "scale" for the inverse of the domain of our signal. If the signal is indeed in time …

The Gabor wavelet is a kind of the Gaussian modulated sinusoidal wave (source)   Gabor wavelets are formed from two components, a complex sinusoidal carrier and a Gaussian …

For a given time series which is n timestamps in length, we can take Discrete Wavelet Transform (using 'Haar' wavelets), then we get (for an example, in Python) -

It is known that a) the STFT gives a rectangular tiling of the time-frequency plane b) the Wavelet transform gives a non-linear tiling (better frequency resolution for low-frequencies, and better...

A wavelet transform is defined for infinite length signals. Finite length signals must be extended in some way before they can be transformed. I know that periodic replication and zero padding are

The oscillations vary in frequency in the time domain, so wavelet shrinkage seems to be a reasonable option, but most of the literature on wavelet shrinkage is applied to denoising, …

1 I'm currently studying wavelets and had an interesting thought experiment: If you were to calculate the wavelet transform of a signal using a wavelet of a fixed frequency, you would get …