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none of them are showing WHAT TO DO AFTER I PERFORM DTW!. There are two quotes, at least in the page that you have linked that hint at what you do next, these are: "We assume that you are familiar with the algorithm and focus on the application. Further information about the algorithm can be found in the literature..." The linked reference is not a bad ...


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Just to clarify the issue I was having and the outcome for future reference: I was using Ubuntu 16.04 and trying to compile GNU Radio 3.8. Although GRC was present as an executable it did not open/run. This turned out to be because of some Python incompatibility which is referenced briefly on the UbuntuInstall section of the GNU Radio Wiki: "Building GNU ...


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you can create flowgraph in c++ like gqrx and i think GNU Radio Manual and C++ API Reference documents will help you. This is gnuradio c++ document for top_block if you could not use it let me know to write a sample for you. These documents are for gnuradio version 3.7 and in gnuradio 3.8 there is c++ code generation and gnuradio blocks are porting to c++ ...


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I came back to this and tried deriving the discrete version which helped make things more sense: Somehow $f_k t_n = f(n, k, N)$ $ f_k = \frac{f_s}{N}k$ and $ t_n = \frac{T}{N}n $ $f_s = \frac{N}{T}$ So $f_k t_n = \frac{f_s}{N}k\frac{T}{N}n = \frac{N}{TN}k\frac{T}{N}n = \frac{kn}{N}$ Done!


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I figured out my problem. The kernel needs to be shifted so the 'center' is on the corner of the image (which acts as the origin in an FFT). The built-in ifftshift function works just fine for this. (Note, there are some subtleties here depending on whether you have even or odd shapes or differences in shape that can result in one row or column shifts. I ...


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You can find a nice tutorial for time-frequency analysis in Numerical python by Johansson, chapter 17. link to github repository. You can also check the scipy.signal.spectrogram. import numpy as np from scipy import signal from scipy.fft import fftshift import matplotlib.pyplot as plt # Generate a test signal, a 2 Vrms sine wave whose frequency # is ...


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A better approach for use in the presence of noise and distortion is to use the Wiener-Hopf equations which will provide a least-mean-square solution of the effective "channel" between the microphones. The group delay can be determined using scipy.signal.group_delay. Further details on this approach specific to microphone captures including Matlab/Octave ...


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The goal is to predict a genetic distance of species from the spectra. For instance if the genetic distance of species 1 and species 2 is very height I would expect a different spectra. If the distance is 0 or very low,m the spectra are the same /very similar. In that case, defining the distance between two spectra based on the spectra makes no sense – you ...


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In Short if $X[k]$ represents the $N$-point FFT of an $N$-point signal $x(n)$ , then $N * x(n)$ (sequence $x(n)$ scaled by $N$) can be obtained by performing FFT on the sequence $X[0], X[N-1], X[N-2], \cdots ,X[1]$. Explanation: Consider the equation of IFFT $$IFFT: x[n] = \frac{1}{N} ( \sum_{k=0}^{N−1}X[k]*e^{j2πnk/N} )$$ It can be split and written in ...


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Actually, as mention by @applesoup your question is not clear but as far as I understood you can repeat the signal using numpy.matlib.repmat(a, m, n) Repeat a 0-D to a 2-D array or matrix MxN times. Parameters: a : array_like The array or matrix to be repeated. m, n : int The number of times a is repeated along the first and second axes. ...


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