endolith
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Use a Gaussian window - the Fourier transform of a Gaussian is a Gaussian Log-scale the spectrum to emphasize peaks and turn the Gaussian peaks into parabolic peaks Use parabolic interpolation to find ...

WAV or FLAC are lossless, so the digital data should be identical when fed to the DAC. Lossy formats like MP3 and OGG don't store a waveform, though. It needs to be reconstructed from sparser data, ...

Cons: Not as accurate This is just compared to the other methods. I was measuring frequency very accurately to look for clock drift, etc: 1000.000004 Hz for 1000 Hz, for instance. For guitar pitch ...

How can i do that? Generate a 370 ms long Hann window, take a 370 ms chunk of your audio, then multiply them together, sample-by-sample. You're just taking a chunk of audio and fading it in and out ...

Ok, in the context of one specific application: If you're trying to find the frequency of a waveform, you can calculate it similarly from the position of the peak in a Fourier transform or the peak of ...

However what is conceptually wrong with using say an infinite sum of time shifted rect functions to represent it? There is nothing conceptually wrong with it. Fourier transforms decompose a signal ...

It works fine for me: from scipy.signal import hilbert import numpy as np from matplotlib.pyplot import plot sensor = np.loadtxt('signal.txt') plot(sensor) analytical_signal = hilbert(sensor) plot(...

I am generating my waves in a "Raw" mathematical way, meaning that i am creating a ramp for a saw wave and my square wave consists of pure 1's and 0's. The issue is that this technique is inherently ...

I only need the total group delay, not spectrum of group delay. Group delay is a spectrum, so this doesn't make sense. The group delay is the derivative of the phase response of the filter, so in ...

I don't think there is any difference. The documentation for dwt2 says Single-level discrete 2-D wavelet transform The dwt2 command performs a single-level two-dimensional wavelet ...

What happens if I choose the length of signal L > NFFT? and what's about choosing L different form NFFT? Did you read the documentation? http://www.mathworks.com/help/techdoc/ref/fft.html Y = ...

As I say further down the page: it turns out that ICA doesn’t actually work well when the signals occur at different delays in the different sensor channels; it assumes instantaneous mixing (that ...

I like these animations of Fourier transforms: The continuous Fourier Transform of rect and sinc functions

complex morlet was added Aug 10, 2007 ricker and cwt were added Sep 20, 2011 There's no indication that cwt is meant to be compatible with morlet. As cwt docstring says: Wavelet function, which ...

The frequency response is the same, yes, but the application is different: With a low-pass filter, your signal is in the passband. The cutoff frequency of the filter is set above the highest ...

I bet your sine wave is zero at the first and last sample? It shouldn't be. It should be lined up so that the next sample after the last sample is zero, so that you can copy and paste copies of the ...

Does this work? from __future__ import division from scipy.signal import butter, lfilter fs = 1E9 # 1 ns -> 1 GHz cutoff = 10E6 # 10 MHz B, A = butter(1, cutoff / (fs / 2), btype='low') # 1st ...

I think using cross-correlation and interpolating the peak would work fine. As described in Is up-sampling prior to cross-correlation useless?, interpolating or upsampling before the cross-...

First off, you should use whichever tool is appropriate for the job. Correlation vs coherence vs wavelet-based correlation are all different things, so this question is kind of like asking "Which is ...

This sounds like blind source separation. In general, you can't unmix things after they've been mixed. If you have two different recordings of two sources with some of each source in each recording, ...

a lowpass with variable cutoff frequency and resonance. RBJ has a cookbook on how to do this: https://www.w3.org/TR/audio-eq-cookbook/ Here the cutoff frequency is called w0, and the resonance is ...

All the standards define dBFS as an RMS measurement, relative to the RMS level of a full-scale sine wave, so the calculation is: value_dBFS = 20*log10(rms(signal) * sqrt(2)) = 20*log10(rms(signal)) + ...

I would bet this is just numerical error in the transfer function. Try using butter_sos = butter(..., output='sos') instead of ba format, and sosfreqz(butter_sos, ...) instead of freqz. Does that ...

Hi, I wrote this. It's probably more complicated/inefficient than it needs to be. :D Practical Bessel filter design involves root-finding of a polynomial to generate second-order sections; I don't ...

So here's a diagram: The signal source is at $(x, y)$, left microphone is at $(-1, 0)$, right microphone is at $(+1, 0)$. $d=2$ is the distance between the microphones. $b$ is the distance from the ...

Sawtooth: Even and odd harmonics, falling off at 1/n Square: Odd harmonics only, falling off at 1/n Triangle: Odd harmonics only, falling off at 1/n2 So while triangle and square have the ...

If the background noise is white-ish, you could measure spectral flatness and consider it to be voice when the amplitude is above some threshold and the spectral flatness is below some threshold. ...

So I know I should apply a window function somewhere, and I'm trying to understand how to do that. Multiply the chunk of signal by the window function before doing the FFT. The FFT operates on the ...