Audio Fingerprinting with Python and Numpy (2013)
In the past, I've been in discussions on using a fingerprinting technique for videos. Provide a small clip of a movie, and out pops the title of the movie. This was always intended to be used on a small and well defined library, and never intended to be used on something like youtube.One major problem with video is that you could have SD/HD versions and/or full frame/original aspect ratio types of differences of the same movie. One idea I wanted to play with was to detect edit points. The number of frames between edits could be used as the fingerprints. The entire concept was never anything more than a thought exercise. For the purpose of the exercise, we had to assume that there is no audio with the picture.There are a lot of FFT libraries to process image data since most image compression techniques use some sort of FFT. Would this same type of fingerprinting be able to be used for a visual image. Could the amplitude of the RGB frequencies be used over time? The data set would increase with 3 channels of color, but would it also not help decrease false positives by making the combinations more unique?