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This is amazing! truly great explanation in the related blog post here http://willdrevo.com/fingerprinting-and-audio-recognition-wi... Does anyone know of a good explanation of locality sensitive hashing? I know there are other applications.
How well would this work for spoken word instead of music?
We were doing recognition of TV shows where you are holding a cell phone in your hand some distance from the TV. A prototype with this algorithm was okay, but very easily confused especially as the number of items in the database increases. You also end up with a lot less amplitude at that distance from the TV which makes the source messier. In theory phase shouldn't have had an effect, but in practise it did so things had to be run multiple times at different offsets to improve matching.
Our final algorithm was way better. It was based on what audio codecs do. It even worked reliably with a 60db signal while there was a 70db interferer signal!
Rather then just fingerprinting recorded audio can this thing fingerprint words and passphrases that the user just says outloud?
Try using an app that shows the FFT and see if you can get it to show the same thing twice when speaking. For example on Android this works https://play.google.com/store/apps/details?id=org.hermit.aud...
This is amazing! truly great explanation in the related blog post here http://willdrevo.com/fingerprinting-and-audio-recognition-wi... Does anyone know of a good explanation of locality sensitive hashing? I know there are other applications.
In short, LSH is an algorithm that hashes points that are nearby in a feature space into the same bin with high probability. Contrast that with cryptographically secure hashes where the tiniest change in the input is designed to yield a completely different hash. The point is that, in domains like multimedia, you want to tolerate some distortions to your signal, e.g. microphone noise, blur, etc. These minor distortions shouldn't affect your characterization of the data, e.g. "is this a guitar", "is this a cat", etc.
The advantages are that it's simple to implement, and it has mathematically provable probability bounds and query complexity.
[1] http://stackoverflow.com/questions/5751114/nearest-neighbors...
[2] http://www.cs.princeton.edu/courses/archive/spr05/cos598E/bi...
[3] http://www.vldb.org/conf/1998/p194.pdf
[4] http://www.vldb.org/conf/1999/P49.pdf
[5] http://web.iitd.ac.in/~sumeet/Slaney2008-LSHTutorial.pdf
I made a cool writeup about it here: http://willdrevo.com/fingerprinting-and-audio-recognition-wi...
It's a great little library for doing audio recognition, stream radio advertisement verification, and all sorts of interesting people email about all the time that I never would have thought of.
It's certainly not as speedy as Echoprint, which is both written in C++ and doesn't use an FFT for the locality sensitive hashing, but is quite user friendly. The benefit of doing constellation or time delta based LSH methods like in Dejavu is that you can actually recover the time at which you matched.
If you love it, feel free to dig in and contribute!
Rather then just fingerprinting recorded audio can this thing fingerprint words and passphrases that the user just says outloud?