4k views

I believe you simply misunderstand what these functions are supposed to do. You get different results because they are not meant to do the same thing. I think it would be relevant that you read the ...

8k views

If you want to code your own application that plot the magnitude response, you first need to extract the poles and zeros from your transfer function in the $Z$ domain. The process that follows can ...

173 views

The answer to your question can mainly be found in the Matlab documentation. The butter function can design filters either in the analog domain as well as in the discrete domain. In order to make an ...

135 views

Make sure you do not mix up FFT and DFT, they gives identical results, but are different. DFT complexity is about $O(N^2)$ while FFT complexity is $\frac{34}{9}N\log_2(N)$. This makes a huge ...

324 views

First, we call $H(z)$ the transfer function (not transform). Next, I believe you should have a look at Wikipedia first, many of your questions shows no preliminary research. Still, I'll try to ...

8k views

Your overlap is computed right here, in the hop_size variable. hop_size = np.int32(np.floor(fft_size * (1-overlap_fac))) Let's make a small example. You use a 1024 sample fft to compute the STFT of ...

vote
293 views

Your whole problem is due to the fact that you have made a wrong assumption. Increasing the sample rate does not increase the frequency resolution. It will increase the Nyquist frequency (the highest ...

11k views

It looks like you are considering these analysis like part of a universal streamline desing process. These analysis techniques are simply tools that helps you understand the behaviour of your system, ...

vote
477 views

I am not sure if my answer will correctly suits your question as it seems a little confusing. Anyway, I understand that you want to eliminate the initial transition of your filter. What I propose is ...

vote
194 views

I am not familiar with the algorithm you have proposed, but I believe I can answer the question you asked. Once you have computed the DFT of your image, what you get is a 2D array of complex number. ...

vote
157 views

The Discrete Fourier Transform is a summation : $$X_k =\sum_{n=0}^{N-1}x_ne^{-j2\pi kn/N}$$ Which means, the more data you have, the bigger the output mangitude. What you can do is normalizing ...

vote
52 views

If I had to solve your problem, I would do as FAT32 suggested an go with a statistical approach before using the FFT. Here is what I would propose that might give you more meaningful insights on the ...

3k views

Following OP comment requesting more details to Maximilian Matthé's answer: Just like mentioned, the key to your question is the Bilinear Transform, where you substitute $s$ (or $j\omega$ in your ...

5k views