endolith
• Member for 10 years, 5 months
• Last seen this week

First I would just try to detect peaks and measure the time between them. If there's too much background noise to measure them reliably, I'd try to identify frequencies that are strong in the pulses ...

Well, you're talking about FFT filtering, right? So you're transforming to the frequency domain, multiplying by a filter, and then inverse transforming back into the image domain ("space domain"?) ...

x = [0 0 0 0 1 1 1 1] is one cycle of a (not bandlimited) square wave, with a DC offset, with a period equal to the period of the window. ....''''....''''....'''' The DC component is the FFT value ...

Well it's not just the gaps; your data is also non-uniformly sampled. Use index_col to use the time column as the index to your dataframe: df = pd.read_table('BD-10d4669.p.1', sep=' ...

No, the output is len(x)*2-1 long, an odd number I don't understand the question The x axis is the delay in samples, and the y axis is the cross-correlation. The number of x samples is odd, and the ...

You should start with a simpler one-dimensional case first and then work your way up to two dimensions. If you slice one row of your graph paper: from matplotlib import image import matplotlib.pyplot ...

Below, the result of a simulation where I have the sum of two sine-waves with frequencies 100 Hz and 201 Hz, respectively: $$x(t) = > \sin(2 \pi 100 t) + \sin(2 \pi 201 t)$$ The signal is ...

Wouldn't mixing them give you a sum and difference frequency and you have to lowpass filter to get the difference frequency only? Also your chirps are the same frequency, just out of phase? So the ...

It doesn't happen with a random signal. Your signal must have low frequency content around 0 Hz that shows up even after you've nulled out 0 Hz itself? import numpy as np from scipy import signal ...

It looks like it's working fine, but your signal contains some content at DC and the Nyquist frequency. DC doesn't survive through the transform, and Nyquist gets altered. If you bandlimit it first, ...

The number of frequencies 'measured' in the Fourier transform of each time frame is exactly equal to the hop size, The number of frequencies is not determined by the hop size, it's determined by the ...

Python doesn't have an FFT, but it's provided by external libraries like NumPy, SciPy, pyFFTW, etc. None of these three libraries care what size the input is. It can process lengths that are power ...

They represent fluctuations from atmospheric pressure. Positive values = increases in pressure = speaker cone moving outward Negative values = decreases in pressure = speaker cone moving inward ...

From Wiktionary: hypervoxel (plural hypervoxels) A multidimensional analogue of a voxel From Digital Design Media - Mitchell & McCullough, 1995:

I would like to approximate the change in gain ($\Delta \text{ Gain}$) as perceived by the listener for an arbitrary audio signal sent through the filter $H(s)$. That's not possible, since the filter'...

The amplitude is correct but it is mirrored and 90 degrees off-phase. I feel I am missing something but I cannot nail it. re_field has np.abs in it, so it's not the same thing. You've rectified the ...

You're doing circular convolution, which wraps both signals around in a circle before sliding them past each other. You're convolving [..., 1, 2, 3, 4, 1, 2, 3, 4, ...] with [..., 5, 4, 3, 2, 5, 4, 3,...

y_sos, zi = signal.sosfilt(sos, list, zi=zi*list[0]) should just be y_sos, zi = signal.sosfilt(sos, list, zi=zi) same as the question you asked yesterday

how do we integrate the SPL of each frame with the previous frames? You're measuring the RMS value of the (filtered) samples, which is sqrt(average(samples^2)), so if you're finding the RMS value in ...

Yes. A high-pass filter removes low frequencies, and a constant baseline is the lowest frequency possible ("DC" = 0 Hz), so it will be completely removed and the baseline will become 0.

I was trying to remove the DC component of a load cell signal The simplest way would be x = x - mean(x), but this won't line up perfectly if you're processing multiple consecutive chunks. Problem ...

Conceptual partial answer: You can use 3 mics to find position in 3D space, except you can't tell if it's above the plane or below the plane (unless you already know there's nothing above the plane, ...

Similar question here Which time-frequency coefficients does the Wavelet transform compute? The picture you've shown is used for DWT such as pywt.wavedec, not CWT. CWT is a continuous function; it ...

"The UCA202 uses the ubiquitous TI PCM2902 integrated USB DAC chip" So this a "Full speed" USB device, and the USB data is being sent at 12 MHz, but it doesn't send data continuously; it sends it in ...

@robert-bristow-johnson explains this very clearly on comp.dsp: you have to oversample to a finite extent. if you represent the (memoryless, i assume) non-linearity as a finite order polynomial (...

The way you do it is you use a window that tapers to 0 at the ends, both before and after the FFT (analysis and re-synthesis windows), along with 50% overlap (needs to be a COLA window), to fade out ...

$y(t) = x(t)^3 - \frac 3 4 x(t)$ (3rd harmonic) $y(t) = x(t)^5 - \frac {10} {16} x(t)$ (harmonics 3, 5) $y(t) = x(t)^7 - \frac {35} {64} x(t)$ (harmonics 3, 5, 7) etc. Presumably this is a sampled ...

More details on the matched filter approach: Matched filter is the same thing as cross-correlation. Input A is a template of what you expect the click to sound like, and input B is the live stream ...