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- Wavelet Scattering explanation? - Signal Processing Stack Exchange
Wavelet Scattering is an equivalent deep convolutional network, formed by cascade of wavelets, modulus nonlinearities, and lowpass filters It yields representations that are time-shift invariant, robust to noise, and stable against time-warping deformations - proving useful in many classification tasks and attaining SOTA on limited datasets Core results and intuition are provided in this
- PyWavelets CWT implementation - Signal Processing Stack Exchange
I seek to understand PyWavelets' implementation of the Continuous Wavelet Transform, and how it compares to the more 'basic' version I've coded and provided here In particular: How is integrated
- Wavelet thresholding - Signal Processing Stack Exchange
The soft thresholding is also called wavelet shrinkage, as values for both positive and negative coefficients are being "shrinked" towards zero, in contrary to hard thresholding which either keeps or removes values of coefficients In case of image de-noising, you are not working strictly on "intensity values", but wavelet coefficients
- Power Energy from Continuous Wavelet Transform
How can power or energy be computed from Continuous Wavelet Transform? Is it just $\sum |\text {CWT} (x)|^2$, or are there other considerations, particularly if interested in a subset of frequencies?
- Whats the difference between the Gabor and Morlet wavelets?
The Gabor wavelet is basically the same thing It's apparently another name for the Modified Morlet wavelet Quoting from : That book is a collection of papers, and that paper ("The Wavelet Transform and Time-Frequency Analysis") is by Leon Cohen (of time-frequency distribution "Cohen class" fame), so I think it's reasonably authoritative At the very least, it sounds like the confusion is
- python - Feature extraction reduction using DWT - Signal Processing . . .
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) -
- Discrete wavelet transform; how to interpret approximation and detail . . .
Discrete wavelet transform; how to interpret approximation and detail coefficients? Ask Question Asked 8 years, 1 month ago Modified 2 years, 9 months ago
- wavelet - CWT at low scales: PyWavelets vs Scipy - Signal Processing . . .
Low scales are arguably the most challenging to implement due to limitations in discretized representations Detailed comparison here; the principal difference is in how the two handle wavelets at
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