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2answers
50 views

Shifting signal smaller than discrete step

I have a image that I need to shift with less than a pixel. My plan was to do a Fourier transform and multiply the signal with $e^{-aiu-biv}$ where $a, b$ are the shifts in x and y direction. This ...
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0answers
43 views

Acceleration time series to velocity conversion using DFTs

I have 1000 Hz time series data for acceleration (512 data points), which I want to convert to velocity. I am trying to use the omega arithmetic method to achieve this. Following are the steps I am ...
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1answer
107 views

Discrete Fourier transform of an exponential decay

I have a vector with an exponential decay, using Numpy: t=np.arange(128) k=0.1 decay=np.exp(-k*t) I would like to compute the discrete Fourier transform (DFT) of ...
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vote
1answer
101 views

Amplitude of frequency bin in FFT doesn't match time-domain amplitude

I have a sinusoidal current that I am sampling at about 357k SPS. The current signal is about 3A pk-pk @ 750hz (top figure). I am wondering why when I take the fft using numpy/scipy's fft function, ...
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1answer
68 views

Numpy fft2 frequency shift

I don't understand how to make frequency shift in fft2 or higher dimensions. Could anyone explain it, please? import numpy as np import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import ...
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1answer
65 views

How to interpret the signal in frequency domain

I'm having difficulties understanding some basics regarding FFT. I was hoping you could answer a few questions I have. These questions will probably seem silly to you, sorry for that. I'm a complete ...
5
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1answer
107 views

Morphing between two FFTs

I'm a musician and I'm making a script that takes a wave file and snaps each of its frequencies from the fourier transforms to the nearest musical harmonic. Thanks to help from another question I ...
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1answer
41 views

Unexpected peaks in power density following downsampling and filtering

I first applied a 100 Hz lowpass filter to my data, which was recorded at 30000 Hz: ...
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2answers
199 views

Why is the size of the output of the FFT of a signal is same as the size of the input signal?

I am kind of new to the DSP domain. I was trying to get the frequencies associated with a signal by performing FFT over it. I used numpy.fft.fft for this. ...
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0answers
722 views

AM Modulator-Demodulator in Python

I am trying to make a AM modem using Python Numpy and Matplotlib. I am successful in generating the AM Signal but I cannot demodulate it using a lowpass filter function that I made. The demodulation ...
3
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2answers
183 views

Understanding a simple case of fft2 in python

I have 2D arrays representing surfaces. I have created them by scanning some theoretically flat objects and I ended up with what can be called "imperfections", or else, deviation from the perfectly ...
0
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1answer
76 views

Using fft to calculate vsdft

We are currently implementing the velocity synchronous discrete Fourier transform (vsdft) for order tracking rotating machinery in Python. The formula is similar to dft. Simplified, it is given by $...
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1answer
410 views

Result of inverse FFT is sometimes shifted in real space

I am using the Numpy fft2, ifft2, and related functions and I am sometimes running into a strange situation where the output ...
5
votes
1answer
387 views

How to generate random samples of Gaussian distribution directly in the frequency domain?

One can easily draw (pseudo-)random samples from a normal (Gaussian) distribution by using, say, NumPy: ...
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2answers
2k views

Avoiding “RuntimeWarning: invalid value encountered in log10” when converting FFT data to dBFS

I've attached some code below that I'm having some issues with. Basically, I need to write a function to get the dBFS value of a signal at a specified Hz value, at a given channel in a .wav file. ...
1
vote
1answer
545 views

Hamming Window, python 2.7 [closed]

Hi I have a FFT which is quite noisy. How to apply to my code Hamming window to make it less noisy. Look at my code: ...
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2answers
2k views

Looking for fastest 2D convolution in python on a CPU

Convolutions are essential components of many algorithms in neural networks, image processing, computer vision ... but these are also a bottleneck in terms of computations... In the ...