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Inverse filtering to undo a convolution blows up

A better approach would be to apply some method to solve problems on the form $$\min_v \|Mv - d\|$$ or possibly $$\min_v \|M(v+d) - d\|$$ Where $M$ is the Gaussian convolution operation, $d$ is the ...
mathreadler's user avatar
1 vote

How do you filter a signal, split in batches, using an IIR filter?

Your code is fine. Before doing anything to a signal, it's good to look at its frequency content. This is the power spectrum of the sample you linked to (I took DC out): Your signal is highly ...
Jdip's user avatar
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1 vote
Accepted

convert ndarray with properties in Matlab

This isn't a signal processing question, so might be outside the scope of acceptable questions. I'll let the mods decide. Two ways depending on your Matlab version and setup: ...
Jdip's user avatar
  • 6,265
1 vote
Accepted

call decimate function from matlab

Matlab uses double values by default. Scipy.signal.decimate expects an integer as the second argument, the decimation factor. ...
Jdip's user avatar
  • 6,265
3 votes

Inverse filtering to undo a convolution blows up

However, in both cases, the result blows up to infinity when I take the IFFT. The result blows up before that, when you do division by 0: add a small constant to <...
Jdip's user avatar
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0 votes

How do you properly cut out negative frequencies from FFT of a real signal if it reduces sample size?

You mention magnitudes. You need to consider the complex numbers. For a real input vector, the $0^{th}$ bin will be real. Then for bins $0 < n < N/2$, the values will be complex, so each will ...
TimWescott's user avatar
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0 votes

How do you properly cut out negative frequencies from FFT of a real signal if it reduces sample size?

By removing the negative frequency components, you're not really losing any information because they are just the complex conjugate of the positive frequency components, so they're trivial to recover. ...
Stephen's user avatar
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