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Creating Shazam in Java
31–40 of 49 posts
Re: Creating Shazam in Java
#32I mirrored this implementation a while ago since the full source isn't available. It was not nearly as successful as the blogger portrays. For example, if I used a high quality wav mono file to create a fingerprint it would have a hard time identifying a track that is an mp3. It seems the maximums actually get shifted and merged from compression. In other words there's a reason shazam uses entropy based anchor points to help it pick hashing values.
Re: Creating Shazam in Java
#33From where Shazam gets its content - fingerprint database? I mean, did they bought/rent mp3's?
I haven't had a chance to google for a source so take this as anecdotal but I vaguely remember reading an interview with the people behind it (when Shazam first launched in the UK) and in it they said they were ripping thousands of CDs a day/week (can't remember which) and running each track through their algo. Can't remember if they bought the CDs or had some deal in place with the record labels.
Interesting. So as with many other "innovative" startups - the content is the crucial thing.
As already pointed here, audio fingerprinting is not a new thing. Although, they might have added some twists in order to were able to patent it.
Re: Creating Shazam in Java
#34I mirrored this implementation a while ago since the full source isn't available. It was not nearly as successful as the blogger portrays. For example, if I used a high quality wav mono file to create a fingerprint it would have a hard time identifying a track that is an mp3. It seems the maximums actually get shifted and merged from compression. In other words there's a reason shazam uses entropy based anchor points…
I'm wondering if they bound the fingerprint search to human audible frequencies. MP3 compression, as a lossy codec, works by discarding information in the input signal that corresponds to inaudible frequencies. I believe this could be mirrored in the implementation by running the frequency domain peak-pick algorithm only over specific bin ranges.
Re: Creating Shazam in Java
#35I mirrored this implementation a while ago since the full source isn't available. It was not nearly as successful as the blogger portrays. For example, if I used a high quality wav mono file to create a fingerprint it would have a hard time identifying a track that is an mp3. It seems the maximums actually get shifted and merged from compression. In other words there's a reason shazam uses entropy based anchor points…
I'm wondering if they bound the fingerprint search to human audible frequencies. MP3 compression, as a lossy codec, works by discarding information in the input signal that corresponds to inaudible frequencies. I believe this could be mirrored in the implementation by running the frequency domain peak-pick algorithm only over specific bin ranges.
I don't recall if the paper specifies the frequency ranges used but my implementation was bound to audible frequencies. I was going to use hill climbing search to find optimal frequency ranges but came to the conclusion my implementation was too flawed regardless. If I looked at the two graphs side by side(compressed vs uncompressed) they looked nothing alike. For example, the peak might be in the same region but it would be shifted.
Re: Creating Shazam in Java
#36This is very cool. Minimum clear implementation of the algorithm that replicates the effect of Shazam. It's refreshing to see a blog with actual code sample got voted up instead of all the press releases.
+1
Re: Creating Shazam in Java
#37Is there any code changes that you can make to not conflict with the patent?
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Re: Creating Shazam in Java
#38Earlier quoted context omitted.
I'm wondering if they bound the fingerprint search to human audible frequencies. MP3 compression, as a lossy codec, works by discarding information in the input signal that corresponds to inaudible frequencies. I believe this could be mirrored in the implementation by running the frequency domain peak-pick algorithm only over specific bin ranges.
I don't recall if the paper specifies the frequency ranges used but my implementation was bound to audible frequencies. I was going to use hill climbing search to find optimal frequency ranges but came to the conclusion my implementation was too flawed regardless. If I looked at the two graphs side by side(compressed vs uncompressed) they looked nothing alike. For example, the peak might be in the same region but it…
http://www.redcode.nl/blog/2012/03/devoxx-2011-talk-freely-a... has a demo in the video
Re: Creating Shazam in Java
#39After using Shazam, I was kind of hoping there was more to it than just a time windowed frequency domain peak-pick algorithm. The algorithm itself is pretty basic from a signal processing perspective, but I think the key insight here was that the results are unique enough to store off and compare other samples against at some later point in time.
Yeah, the magic (if there is any) is doing the match across a silly amount of songs in a relatively short time. Not groundbreaking exactly, but operationally quite interesting.
Re: Creating Shazam in Java
#40This is interesting I wonder how the work is split between client/server in (actual) Shazam. (I suppose only the key points are sent to the server, but I may be wrong - Siri for example sends the server a compressed audio file of the recorded sound)
You can phone Shazam up and have it make the identification using what it hears live. There's no client processing at all for that.