dejavu

Bit technical, generate an audio verson of unique hash tag id. compare audio hash tags to find duplicates. "turnkey" setup : musicbrainz picard[0] incorporate into own project : acoustid[1], dejavu[2] ------------ [0] : https://picard.musicbrainz.org/ [1] : https://acoustid.org/ [2] : https://github.com/worldveil/dejavu

The correct url for the repo is https://github.com/worldveil/dejavu The link has `(2014)` appended to it.

Incredibly done in 2003. There's a python library called dejavu[0] that does similar hashing. It mostly works to make your own Shazam (assuming you can source the data to hash!) [0] https://github.com/worldveil/dejavu

Does anyone know how this approach differs from the approach taken by the popular(ish) dejavu project? https://github.com/worldveil/dejavu

The technique you're looking for is Acoustic Fingerprinting [1]. There's a pretty easily understandable python implementation[2] of it if you're interested. In Shazam's case it is looking for peaks in the sound's spectrograph, so I don't think it would likely confuse two songs based on melody alone. It would have to be very close in rhythm, melody, and possibly even be in the same key. [1] - https://en.wikipedia.org/wiki/Acoustic_fingerprint [2] - https://github.com/worldveil/dejavu

Very cool stuff! It seems that all those solutions are based on the analysis of visual representations of spectrograms. Is this common or could you just use 2d arrays which encode the same information - would this be more performant? Nice blog post about this stuff: http://willdrevo.com/fingerprinting-and-audio-recognition-wi... - https://github.com/worldveil/dejavu

Essentia does audio fingerprinting with Chromaprint, which is essentially that. There are also other open source projects using Chromaprint focused specifically on that - https://github.com/worldveil/dejavu

Poor name choice. https://en.wikipedia.org/wiki/D%C3%A9j%C3%A0_Vu_(software) https://github.com/worldveil/dejavu https://github.com/appbaseio/dejavu https://github.com/IndigoUnited/js-dejavu

Glad to see it's working well for you! I'd be curious as well to see how the performance holds up getting into the terabytes as I haven't tested that. Remember too that there are a lot of parameters for the matching algorithm here (https://github.com/worldveil/dejavu/blob/master/dejavu/finge...) which allow you to trade off accuracy, speed, and storage in different ways. I've tried to document it throughly. Finding duplicates is a great one! Actually generating a checksum for each audio file (minus the header and ID3 tags) and adding this as a column in the songs table for all the different filetypes Dejavu supports (mp3, wav, etc) would probably be the best way to do this. I say this because so many songs today are built on sampling. Mashups and EDM music often samples from other work, and as such, the fingerprints and their alignment ca

Why fingerprint audio with Shazam when you can do it yourself? I've made my own audio fingerprinting software in Python under the MIT license, check it out! https://github.com/worldveil/dejavu

I've made my own audio fingerprinting software in Python under the MIT license, check it out! https://github.com/worldveil/dejavu