Category : signal-processing

I would like to use Python to generate different colors of noise, just like Wikipedia mentions : https://en.wikipedia.org/wiki/Colors_of_noise. For example, White, Pink, Brownian, Blue and Violet noise. And would like to have similar spectrums just like the website. It would be a great help if I could just adjust a few parameters to get it ..

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I have been working to develop an algorithm in python in which certain tasks need to be computed in parallel. I am using threadpoolexecutor to do it. My specific section of code is: with concurrent.futures.ThreadPoolExecutor(max_workers=number_of_threads) as executor: for chunk in NearEndChunks: test = ED.EchoDetection() futures = {executor.submit(test.echoDetection, chunk, FarEndChunks, i, i + chunk_shift): i for ..

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On Kaggle I have found algorithms used for signals denoising. Such as Golay filters, spline functions, Autoregressive modelling or KNeighborsRegressor itself. Link: https://www.kaggle.com/residentmario/denoising-algorithms How exactly does it work as I cannot find any article explaining its use for signal denoising? What kind of algorithm is it? I would like to understand how it works Source: ..

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I’m trying to write a simple metronome with librosa.clicks import librosa import numpy as np bpm = 200 exercise_duration = 10 sr = 22050 seconds_per_beat = 60/bpm beats_number = round(bpm/ (60/exercise_duration)) metronome_clicks = [0] for i in range(beats_number – 1): metronome_clicks.append(seconds_per_beat + metronome_clicks[i]) click = librosa.clicks(times=metronome_clicks,sr=sr, length=22050 * exercise_duration, click_duration=0.1) hop_length = 512 onset_samples = ..

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This is my first post so apologies for any formatting related issues. So I have a dataset which was obtained from an atomic microscope. The data looks like a 1024×1024 matrix which is composed of different measurements taken from the sample in units of meters, eg. data = [[1e-07 … 4e-08][ … … … ][3e-09 ..

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I am designing a 3rd order Butterworth filter in Python which I am testing on a composition of sinusoids – t=np.linspace(0,100,1000) sig=np.sin(2*np.pi*10*t)+np.sin(2*pi*20*t)+np.sin(2*pi*30*t) sos=signal.butter(3,15,’hp’,analog=True,output=’sos’) filtered=signal.sosfilt(sos,sig) plt.subplot(2,1,1) plt.plot(t,sig) plt.subplot(2,1,2) plt.plot(t,filtered) plt.show() After executing the above, I see a output waveform like this – Original signal and filtered signal Please point out the mistake in my code. Source: ..

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