Linked Questions

23 votes
2 answers
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?
OverLordGoldDragon's user avatar
9 votes
5 answers
2k views

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 ...
Long's user avatar
  • 143
8 votes
2 answers
1k views

Is there an equivalent of Parseval's theorem for wavelets?

Parseval's theorem can be interpreted as: ... the total energy of a signal can be calculated by summing power-per-sample across time or spectral power across frequency. For the case of a signal $x(...
hazrmard's user avatar
  • 225
6 votes
3 answers
2k views

Role of window length and overlap in uncertainty principle?

I am trying to predict epilepsy using spectrograms and a convolutional neural network. So far I have achieved a validation accuracy of 86% which i feel like is pretty good. Lots of the papers doing ...
niklas Munkholm Hjort's user avatar
2 votes
2 answers
929 views

What is "filter periodization"?

A library defines periodize_filter_fourier, which is an equi-spaced averaging formulated by $$ v_f[k] = \sum_{i=0}^{\text{n_periods}-1} h_f[i\cdot N + k], $$ where $v_f$ is periodization of $h_f$, $N=\...
OverLordGoldDragon's user avatar
2 votes
2 answers
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} \...
OverLordGoldDragon's user avatar
1 vote
2 answers
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, ...
qian zhang's user avatar
1 vote
1 answer
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 ...
G. Fougeron's user avatar
3 votes
2 answers
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 ...
M-S's user avatar
  • 31
0 votes
1 answer
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 ...
Jokerp's user avatar
  • 189
-1 votes
2 answers
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.. ...
Lover Coffee's user avatar
3 votes
1 answer
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 ...
WedgeAntilles's user avatar
0 votes
0 answers
63 views

In continous wavelet transform, all wavelets must have the same energy regardless of their scales,why?

In Continuous Wavelet Transform (CWT) To achieve energy normalization and to ensure that all wavelets have the same energy regardless of their scales, each wavelet is divided by the square root of the ...
Hend Ibrahim's user avatar
0 votes
1 answer
57 views

Strided unpadding energy relationship?

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, ...
OverLordGoldDragon's user avatar