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I answered this question in an article and a video form. I could have copied and pasted the article here but to answer your question thouroughly and in plain English it's best to simply check out the above 🙂


You have a gaussian centered at 500 THz. We would expect the convolution to have a single gaussian centered at 1000 THz. A linear convolution of two sequences of N points each will have a length of 2*N-1 samples. You have the added complication that your frequency vectors don't start at 0Hz. One way to fix this would be to have them simply start at zero dt = ...


Is this some sort of correlation or convolution? No. For that you'd need to build products from the elements. You only do differences. It should be meaningful for calculating ifft(X), but I can't see how... I'm not immediately recognizing these terms, and the length of 5 seems very oddÂą; so, this is probably an algorithmic step in a specific FFT algorithm ...


In the continuous time-function formalism, and accepting the framework or distributions or generalized functions, the answer is direct. Taking $\delta$ for the Dirac delta function, for a sufficiently well-behaved function $f$: $$\delta' * f = \delta * f' = f'\,.$$ Therefore, the convolution mask is obvious: it would be the derivative of the Dirac delta. The ...


This is actually an excellent place to start with ML/DNN tools. Noise Reduction, Speech Processing and Recognition are driving a lot of the innovation in sound in this space. Recurrent Neural Networks and LSTM models are good at identifying patterns - which can be useful in this context. If you’ve got an NVIDIA GPU you could ...


df.corr() # Compute pairwise correlation of columns, excluding NA/null values. you can see that in pandas documentation it means you do not need the loop plus you can choose the method of cor

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