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9

A "pure" signal, No. A less noisy signal? Possible? Yes but there are several complications that may make this impractical. You basically need to align the 2 recordings and then add them. You might gain 3dB in SNR. but The paths from the source to the 2 locations aren't the same, so they will differ to some extent, so the copies may not add ...


8

Is it possible to reconstruct the original pure signal? No, that is information-theoretical impossible. Also, that signal doesn't exist, probably, to begin with ;) However, you can definitely increase the the SNR simply by averaging; that becomes pretty obvious when you consider the signal of interest to be correlated within your recording, whereas your ...


3

As Stanley Pawlukiewicz said: even under ideal circumstance, you can gain 3 dB of SNR per doubling of recordings. I.e., to increase SNR by, say, 15 dB, you'd need to average $$ 2^{\frac{15}{3}} = 2^{5} = 32$$ recordings. That alone shows that the whole thing isn't really practical: it just doesn't do much unless you use a crazy-high number of recordings. “...


2

I'd do some small adjustments to your idea (You really nailed them). Assumptions The Signal Model - Signal + Additive White Gaussian Noise (AWGN) Probably we could generalize it more but this is beyond the scope of this question. The DFT of the signal contains Peaks with relatively small roll off This is important as we're almost saying the Signal is a ...


2

There are indeed many peak detection algorithms, and no clear consensus on which ones are "good" or "bad". But for what it's worth, your approach makes sense. Using median or other quantiles to detect sparse signals is common, e.g. the "median clipping" stage in Lasseck (2014), Large-scale identification of birds in audio recordings. In effect, you're ...


1

This is a beautiful way of noise/interference cancellation technique, please go through the link that I am sharing here. Certainly, you may find a way to implement the concept. https://drive.google.com/file/d/0B2bUtLEhrWp8Wi1JZzdub0U2Wm9JWlZEX290cHByZi1ES3FZ/view?usp=sharing


1

Basically each pixel is a realization so all you need is to work in the 3rd dimension (Though you can also get better by using the Spatial Data). Method 1 So the trick here is to use the multiple images to estimate the Mean (True value) of each pixel and then calculate the STD on all samples (numRows * numCols * numRealizations). Assuming we have single ...


1

Yes, you can apply deep learning to peak detection. A 1D CNN would be appropriate for this task. Here is an example for such application: Risum, Anne Bech, and Rasmus Bro. "Using deep learning to evaluate peaks in chromatographic data." Talanta 204 (2019): 255-260. You would need to have annotated data. If you decide to stick with the classical ...


1

turn to float first!!!!!!!! turn to float first!!!!!!!! turn to float first!!!!!!!! def compute_psnr(img1, img2): img1 = img1.astype(np.float64) / 255. img2 = img2.astype(np.float64) / 255. mse = np.mean((img1 - img2) ** 2) if mse == 0: return "Same Image" return 10 * math.log10(1. / mse)


1

Median filter is considered good because unlike averaging filter which ruins the edges of an image by blurring it to remove the noise, median filter removes only the noise without disturbing the edges. Well, median filter is the best and only filter to remove salt and pepper noise. Hope this helps:) Thank you!!!


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