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What are the effects of multiplication of two different sound? An neither of them are constant, like two different songs, or one track of instrumental and one of vocals.
A simple Google search came up with this:
http://crca.ucsd.edu/~msp/techniques/v0.11/book-html/node77.html
Did you search for it at all?
But basically what happens is you end up creating an envelope where the second acts as a "coefficient" of sorts.
You also end up with a reduction of sound levels (since a decimal times a decimal is less both of them), so you'll need to amplify the signal a bit to retain volume.
The page I linked gives a lot more explanation and has a lot of the algebra needed to write up a code to implement it. Look there if you have any more questions.
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The general task is to binarize the image so that only the brightest spots remain. But adaptive binarization and the Otsu method do not give an acceptable result due to light traces (shown in the image).
I think that you need to go through the entire image with a small window that will highlight a local minimum in the area.I am counting on the fact that with the correct selection of the threshold, only light spots will remain that need to be found. It should be. But I do not know how to apply the standard opencv threshold function in sliding windows.
UPD:After the proposed adaptive threshold, the image looks like this. Not perfect, but much closer to what I need.It seems that a combination of threshold functions does not always give a better result than a single one.
This is the command:
outputimg = cv.adaptiveThreshold(img,255,cv.ADAPTIVE_THRESH_MEAN_C,cv.THRESH_BINARY,11,0)
further explanation and examples: https://docs.opencv.org/3.4/d7/d4d/tutorial_py_thresholding.html
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What are some ways including machine learning that I can use in my projects to generate things related to another. Like related apps, related websites, related products, etc.
I've been brainstorming these are strategies...
one way i can think of is show items from same category. But that would be too broad.
2nd way improves upon previous step, it's to keep track of what people click next and promote that item. Meanwhile keep bottom list randomized to let other relevant items show up and get clicked.
3rd way is to use machine learning and provide training data somehow and use that.
I want something simple but smart, as it gets better with time.
Collaborative filtering is designed for solving exactly this problem. The problem with this approach is that produces good results having a lot of data only. I mean... A LOT. And it's not a really simple thing to use. However, any machine learning technique is not simple. There are some node.js packages for CF available, but I have no idea how good are they.
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I have a set of timestamps (each corresponding to a student submission), and I wanted to take a look at them graphically. I know criterion uses a KDE and makes a nice plot, and it looks like it depends on the statistics package, which provides a kde function, but I couldn't trace through the code of criterion to see how it's being used.
Ideally, and answer would at least be a snippet of code that produces a picture. An explanation of what criterion does in this case would also be welcome.
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What are applications where search techniques or more specifically planning techniques are used? I am most interested in examples in use.
I know that A* is used for path planning in Robotics, that planning is used in logistics (details would be great) but what other usages are there?
For Search in general Google, etc come to mind with their inverted indices. Again, where else is it used?
For planning examples, including logistics challenges, take a look at this list. Each use case comes with multiple datasets and a problem definition.
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I am wondering how you would code an image vectorization program, al la vectormagic.com? Where would you even begin and would it be possible to create in any web based programming languages?
Behind vectorization programs are complex algorithms (for basic outline look on quite nice paper depixelizing pixel art by guys at Microsoft).
Anyway, it's possible to write almost in any language, that can process images, but those complex algorithms are pretty system resources expensive. So web based languages are quite inappropriate for that type of task.