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I'm wondering if I can extract a sequence of musical notes from a recorded sound using Python.
It is the first time I'm considering using Python for this.
Help would be truly awesome :)
What you would want to do is take your audio samples, convert them into the frequency domain with a Fast Fourier Transform (FFT), find the most powerful frequency in the sample, and convert that frequency into a note.
See FFT for Spectrograms in Python for pointers to libraries to help with the first two items. See http://80.68.92.234/sigproc.html for some sample code to get you started.
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I want to play a bunch of movies on a grid let's say of dimension (n, n), where each movie m_ij is an element of the grid. I would like to decide which movie is positioned where on the grid at each second, i.e. movies can switch positions over time. How would one start to do this? I'd preferably use Python, but am open to use another language if appropriate. Thanks a lot for any help, it's for an art project.
I know you prefer Python but first think I can think of to achieve something like that easily would be through using this JavaScript library called p5js or processing which is same library written in Java. It is very good for doing art with code kinda thing.
Here is a video to give you the idea.
Anyways hope this helps. Cheers.
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I need to identify the songs by human hum. what are the best methodology and algorithm that i can use for achieve that. I search for code samples. But I couldn't find. Please help me....
You could begin a python program that uses tensorflow to deep-learn the correspondence between humming and songs - it should fall under the umbrella initiative by Google Brain called Magenta.
Of course for Deep-Learning you would need to have a large corpus of examples to learn from.
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What I am trying to do is to create a Multiple Choice Question (MCQ) generation to our fill in the gap style question generator. I need to generate distracters (Wrong answers) from the Key (correct answer). The MCQ is generated from educational texts that users input. We're trying to tackle this through combining Contextual similarity, similarity of the sentences in which the keys and the distractors occur in and Difference in term frequencies Any help? I was thinking of using big data datasets to generate related distractors such as the ones provided by google vision, I have no clue how to achieve this in python.
This question is way too broad to be answered, though I would do my best to give you some pointers.
If you have a closed set of potential distractors, I would use word/phrase embedding to find the closest distractor to the right answer.
Gensim's word2vec is a good starting point in python
If you want your distractors to follow a template, for example replace a certain word from the right answer with its opposite, I would use nltk's wordnet implementation to find antonyns / synonyms.
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This weekend I'll be taking around 50 CCD images with my Celestron CPC 8in. telescope and would like to use python to analyzie the images. Does anyone have any experience doing this?
Check out the python wrapper for OpenCV:
http://docs.opencv.org/trunk/doc/py_tutorials/py_tutorials.html
This should provide all the power you need to do any image processing tasks you require.
And there are some great tutorials to help you get started.
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Data visualization (python preferred but not essential):
I have some data which fits into daily chunks nicely for a period of a few months, how may I produce an infographic/calender-type graph analogous to this (found online):
My preferred medium is Python (would htmlCalender suffice?) but anything to accomplish this will be fine.
I would suggest that you take a look at D3.js.
If you look at the examples there is a Calendar View that does something closely to what you want to accomplish.
Another example of a similar calendar visualization is Cal-heatmap.
You will find more libraries with similar concepts if you google for "calendar heatmap".