13k views

### Synchrosqueezing Wavelet Transform explanation?

How does Synchrosqueezing Wavelet Transform work, intuitively? What does the "synchrosqueezed" part do, and how is it different from simply the (continuous) Wavelet Transform?
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### Identify abrupt changes in an audio waveform

I have an audio file which was created by substituting multiple speech segments into an original utterance. I want to find the timestamps where such substitutions happened, so I guess that one thing I ...
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829 views

### One integral inverse CWT

MATLAB's icwt docs state inversion to be done by a single integral:  f(t) = 2 \Re e\left\{ \frac{1}{C_{\psi, \delta}} \int_0^\infty \left< f(t), \psi(t) \right> \frac{da}{a} \tag{1} \...
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1 vote
824 views

### Relationship between energy, power and sampling rate?

Excuse my silly question, but I think the energy increases as the sampling rate increases. In the time domain, the number of samples increases so the energy increases; In the frequency domain, ...
1 vote
651 views

### Why is inverse CWT inexact / inaccurate?

I'm all new to wavelet analysis. I'm trying to get a working understanding of the continuous wavelet transform and its inverse. By "working understanding", I really mean "getting some ...
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380 views

### Calculating signal power from Continuous Wavelet Transform in MATLAB

I would like to ask a question about the calculation of the signal power using CWT in Matlab. Assume a signal of length N points with sampling frequency $f_{s}$. Using conventional approach, the power ...
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471 views

### Inverse continuous wavelet transform off by constant factor in the y axis

I have implemented the Continuous Wavelet Transform using the pycwt library(https://github.com/regeirk/pycwt/blob/master/pycwt/wavelet.py) and its inverse using Morlet wavelets, however, upon ...
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306 views

### python plt.specgram the number of xaxis' value when I change nfft, overlap

I am trying to use plt.specgram But I found something weird in the x-axis setting. My data is as below.. ...
123 views

### Littlewood-Paley Conditions, Scattering, and Multi-Resolution Analysis

So I am trying to understand the scattering transform on graphs (GST). There are many formulations but I am stuck on equation 8 in this paper. Here they give a condition on a wavelets that looks like ...
If x1 = x0[::2] is unaliased subsampling, then $E(x_1) = E(x_0)/2$, which can be proven via Parseval's theorem. For same $x_0, x_1$, however, ...