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Interpolation from discrete time fourier transform in python

I found this post from googling "numpy fft interpolation", and since the accepted answer does not do what the question asked I thought I would supply my solution for FFT interpolating ...
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Zero Padding in Implementing FFT from scratch

Zero padding inserts new samples in between every sample in the DFT result. Zero padding just gives you more samples on the signal’s Discrete Time Fourier Transform (DTFT) which is a continuous ...
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Fractional Frequency Shifting a Discrete Signal in the Frequency Domain

The frequency domain equivalent of frequency shifting is NOT sinc interpolation but circular convolution with a spectrum of $x[n] = e^{j2\pi\frac{n+\delta}{N}}$, where $\delta$ is your fractional ...
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Zero Padding in Implementing FFT from scratch

Zero padding changes the frequency grid. If your signal is sampled at 1kHz and you apply and DFT of length 100, you evaluate the DFT at 0Hz, 10Hz, 100Hz etc. If you zero pad to 128, you evaluate at ...
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Break a signal into segments, add them together and then perform an FFT on the result

was looking for a naming of this procedure .. any idea? if you don't need the frequency resolution, one single FFT would provide, it's OK to go this way (of adding time segments before calculating ...
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2 votes

FFT of a gaussian signal in Python

There are two issues: The time axis is not long enough to capture a sufficient length of the Gaussian. The FFT is not properly scaled. For the first item mentioned regarding the time axis, the ...
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2 votes
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Average of discrete periodic signal

It appears the OP would like to estimate the mean of the signal efficiently. A moving average will provide the best estimate under condition of white noise; assuming that is the case, the CIC (Cascade-...
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4 votes

Can anyone explain how dft works as a filter bank?

To add to Richard's good answer, I specifically want to show the difference between: Down-conversion and low-pass filtering (heterodyne the signal). Bandpass filtering the signal directly (...
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6 votes
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Can anyone explain how dft works as a filter bank?

@Ahmet. The DFT equation you posted does not compute an array of complex numbers. For any single given integer value of frequency-domain index $k$ the equation tells us how to compute the single ...
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1 vote

Sampling, filters, windowing, FFT. From theory to help on this coding list

There is a lot of detail in the question and I am not sure of all the requirements and desired results that would affect the processing after the 16 KHz decimated samples are produced. However I can ...
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2 votes

Given a signal that is not bandlimited, how do you properly take the FFT?

In real life (as opposed to mathematical fictions) there is always noise (thermal and quantum at the limit), measurement errors, finite durations of operation, finite precision data types and ...
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1 vote
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Adding Fractional STO to an OFDM Signal using Frequency Domain Zero Padding

I believe the distortion is from using the FFT of the complete OFDM symbol including the CP to introduce time delay with a zero-padded FFT. Even before zero-padding is added the FFT result will no ...
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10 votes

Given a signal that is not bandlimited, how do you properly take the FFT?

In the real world, there is always some amount of aliasing, because no real signal is actually bandlimited. In many cases, the signal spectrum tends to zero relatively quickly as the frequency ...
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4 votes

Given a signal that is not bandlimited, how do you properly take the FFT?

If your signal is not band-limited prior to sampling, then without any further information (such as a copy of the signal sampled at a time offset, which could synthesize a higher sampling rate), ...
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1 vote

Does Zero padding cause noise in the high frequency region?

If you've got it in the frequency domain anyway, why not phase shift it by (delay)(frequency)? It may still do odd things (like, shift whatever's at the end of the sample to the beginning), but you'...
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3 votes
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Does Zero padding cause noise in the high frequency region?

Is that correct? No. This zero padding just leads to interpolation with a (cyclic) sinc kernel. It affects all subcarriers the same (as you can see in your own DFT!). So, this has to be a problem ...
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Order analysis on sample vibration data to detect unbalance in python

I think you are doing basically right, you can selectively add two more steps: (1) Check max order for a new sampling rate in order domain, make sure you avoid aliasing. (2) Add some flexibility to up-...
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Are there any order analysis functions in Python?

I am not sure if you can find an implementation to those functions (you might do if you will look long enough). Main point, each of those functions might be implemented in several lines so there is no ...
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1 vote

Lowering Spectral Resolution of FFT

As @MBaz says in the comments, just do: ...
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In what cases can you get aliasing below the Nyquist frequency?

Aliasing is typically from other Nyquist zones into the first Nyquist zone when we low pass in the continuous time domain. If we were to band pass a higher Nyquist zone in the continuous time domain, ...
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understanding a spectrum of a modulated signal

The spectrum will be identical for all cases of oversampling. (The frequency axis just scales accordingly). Proper oversampling does not modify the spectrum in band in any way whatsoever, but on the ...
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1 vote

Is it possible to implement a block-wise Hilbert transformer using FFT

If the issue is in implementing the Hilbert block by block in either case (using MATLAB's hilbert or own method) then this would properly be done using overlap-add ...
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2 votes
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Sparse signal FFT

Partial FFT Sparse FFT Can also subsample the input, which will alias (fold) the high frequencies onto lower, then take FFT at the lower length, and then shift the result back onto higher frequencies ...
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Power Spectrum for feature extraction

Sorry, but it'll be very difficult to extract anything useful from this data. It has a very large bias It has very high quantization noise. The Signal to Noise ratio is maybe 12 dB (at best). It's ...
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2 votes

Should I be using FFT

According to the scipy.fft doc page, if you don't specify the FFT size it will use FFT size equal to the length of the input. At sampling rate of 256 Hz and input size of 100 samples, each frequency ...
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1 vote
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Butterworth filter cutoff attenuation is not exactly 0.707(-3dB)

Filter needs time to settle down. This settling process altered the beginning of time domain data and created the small difference. I took the second half of time domain filtered data and got a ratio ...
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1 vote

Butterworth filter cutoff attenuation is not exactly 0.707(-3dB)

What you see there are margin issues. By not applying a window function to your signal before the FFT, you effectively convolute your spectrum with an $\text{si}$ function, which leads to artifacts ...
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Find offsets of two audio files with several sections in common

I wanted to provide my (partial) solution after doing a lot more experimentation. After trying and failing to get better results by tweaking window sizes, sample rates, etc., I finally decided to ...
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4 votes

FFT with High Time and Frequency Resolution

Time frequency resolution is a long debate in the DSP communities. But modern models has proved that the resolution isn't limited by the DFT because usually we know more about the signal but its ...
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FFT with High Time and Frequency Resolution

I disagree with the following: A) Heisenberg applies no matter what B) Heisenberg can only be broken with assumptions Synchrosqueezing proves this. I'll avoid going into detail, but B is true in the ...
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FFT with High Time and Frequency Resolution

As others stated: without extra information you cannot increase the time resolution and the frequency resolution at the same time. But imagine there is only one sinusoid and you know it, then you can ...
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FFT with High Time and Frequency Resolution

As Jazzmaniac said in his comments: this is a marketing video which is heavy on hype and light on technical details. There is no way around the basic limitation of frequency and time resolution. The ...
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