I want to measure wave lengths of songs as I mentioned in the question. I want to find a number and according to this number I will try to decide what kind of music it is.
I found Acoustic fingerprint but I am not sure if it is going to help me.
Does anyone has ideas?
If a song had just one wavelength it wouldn't really be a song. (just a monotone noise)
I suggest you read up on signal analysis. There are lots of thing that define the character of a song.
EDIT: If you want an adverage of the wavelengths in a song I suggest you do a forier transform in matlab. (here is a guide)
Related
How would I take a song input and output the same song without certain frequencies?
Based on my research so far, the song should be broken down into chucks, FFT it, reduce the target frequencies, iFFT it, and stitch the chunks back together. However, I am unsure if this is the right approach to take, and if so, how I would convert from the audio to FFT input (what seems to be a vector matrix), how to reduce the target frequencies, and how to convert back from the FFT output to audio, and how to restitch.
For background, my grandpa loves music. However, recently he cannot listen to it, as he has become hypersensitive to certain frequencies within songs. I'm a high school student who has some background coding, and am just getting into algorithmic work and thus have very little experience using these algorithms. Please excuse me if these are basic questions; any pointers would be helpful.
EDIT: So far, I've understood the basic premise of fft (through basic 3blue1brown yt videos and the like) and that it is available through scipy/numpi, and figured out how to convert from youtube to 0.25 second chunks in a wav format.
Your approach is right.
Concerning subquestions:
from the audio to FFT input - assign audio samples to real part of complex signal, imaginary part is zero
how to reduce the target frequencies - multiply FFT results near needed frequency by smooth (to diminish artifacts) function that becomes zero at that freqency and reaches 1.0 some samples apart
how to convert back - just make inverse FFT (don't forget about scale multiplier like 1/N) and copy real part into audio channel
Also consider using of simpler digital filtering - band-stop or notch filter.
Arbitrary found example1 example2
(calculation of parameters perhaps requires some understanding of DSP)
I need to write a program that recognize some patterns in different photos. I wrote a program that take a photo as input and creates another image with the edges of previous photo. Now I'm stuck with pattern detection. I tried to take 2d arrays of pixel and mark each possible pattern by giving each pixel a value from 0 to n(maximum number of pixel in a sequence). Then I take the objects that the program already knows and see which one contains the more patterns that were found.
The problem is that beside efficiency, the program won't work if the image is upside down(If I train it with a photo and then flip the photo, the program won't recognize it).
Can you tell me some methods to fulfill my task, or some good tutorials or courses that explain the process a lot deeper than just:"search for patterns"?
Your problem description is very general. To get better answers provide some input data characteristics as well as describe what kind of patterns you're looking for.
What could be useful in general problem of pattern recognition, is using neural networks.
For example you could check the first chap. of this book http://neuralnetworksanddeeplearning.com/chap1.html
There's simple example of pattern recognition for handwritten digits.
In your case for solving rotation problem, you'd probably have to rotate training example as well.
I'm trying to figure out what are currently the two most efficent algorithms that permit, starting from a L/R pair of stereo images created using a traditional camera (so affected by some epipolar lines misalignment), to produce a pair of adjusted images plus their depth information by looking at their disparity.
Actually I've found lots of papers about these two methods, like:
"Computing Rectifying Homographies for Stereo Vision" (Zhang - seems one of the best for rectification only)
"Three-step image rectification" (Monasse)
"Rectification and Disparity" (slideshow by Navab)
"A fast area-based stereo matching algorithm" (Di Stefano - seems a bit inaccurate)
"Computing Visual Correspondence with Occlusions via Graph Cuts" (Kolmogorov - this one produces a very good disparity map, with also occlusion informations, but is it efficient?)
"Dense Disparity Map Estimation Respecting Image Discontinuities" (Alvarez - toooo long for a first review)
Anyone could please give me some advices for orienting into this wide topic?
What kind of algorithm/method should I treat first, considering that I'll work on a very simple input: a pair of left and right images and nothing else, no more information (some papers are based on additional, pre-taken, calibration infos)?
Speaking about working implementations, the only interesting results I've seen so far belongs to this piece of software, but only for automatic rectification, not disparity: http://stereo.jpn.org/eng/stphmkr/index.html
I tried the "auto-adjustment" feature and seems really effective. Too bad there is no source code...
I would like some help from the aficionados of openCV here.
I would like to know the direction to take (and some advices or piece of code) on how to morph 2 faces together with a kind of ratio saying 10% of the first and 90% of the second.
I have seen functions like cvWarpAffine and cvMakeScanlines but I am not sure how to use them.
So if somebody could help me here, I'll be very grateful.
Thanks in advance.
Unless the images compared are the exact same images, you would not go very far with this.
This is an artificial intelligence problem and needs to be solved as such. Typical solution involves:
Normalising the data (removing noise, skew, ...) from the images
Feature extraction (turn the image into a smaller set of data)
Use a machine learning (typically classifiers) to train the data with your matches
Test the result
Refine previous processes according to the results until you get good recognition
The choice of OpenCV functions used depends on your feature extraction method. Have a look at Eigenface.
Can anybody explain me (simplified) what happen if I do an image comparison with FFT? I somehow don't understand how it's possible to convert a picture into frequencies and how this is used to differentiate between two images. Via Google I can not find a simple description, which I (as non mathematic/informatic) could understand.
Any help would be very appreaciated!
Thanks!
Alas, a good description of an FFT might involve subjects such as the calculus of complex variables and the computational theory of recursive algorithms. So a simple description may not be very accurate.
Think about sound. Looking at the waveform of the sound produced by two singers might not tell you much. The two waveforms would just be a complicated long and messy looking squiggles. But a frequency meter could quickly tell you that one person was singing way off pitch and whether they were a soprano or bass. So you might be able to determine that certain waveforms did not indicate a good match for who was singing from the frequency meter readings.
An FFT is like a big bunch of frequency meters. And each scan line of a photo is a waveform.
Around 2 centuries ago, some guy named Fourier proved that any reasonable looking waveform squiggle could be matched by an appropriate bunch of just sine waves, each at a single frequency. Other people several decades ago figured out a very clever way of very quickly calculating just which bunch of sine waves that that was. The FFT.
Discrete FFT transforms a (2D) matrix of let's say, pixel values, into a 2D matrix in frequency domain. You can use a library like FFTW to convert an image from the ordinary form to the spectral one. The result of your comparison depends on what you really compare.
Fourier transform works in other dimensions than 2d, as well. But you'll be interested in a 2D FFT.