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Philips had similar technology that predates Shazam. That said, Shazam's algorithm scales better as they use "combinatorial hashing". This coupled with the fact
by innagadadavida 8y ago
Philips had similar technology that predates Shazam. That said, Shazam's algorithm scales better as they use "combinatorial hashing". This coupled with the fact they can up the hashes in a key-value table makes them better.
- cannam 8y agoFor anyone else still reading, this paper from Philips Research describes the method I think you're referring to. (It's cited in Avery Wang's 2003 Shazam paper.) http://www.ismir2002.ismir.net/proceedings/02-FP04-2.pdf http://www.ismir2002.ismir.net/proceedings/02-FP04-2.pdf This is a really nice paper as well, but the Shazam method is more exciting because of its focus on the search part of the problem. Audio fingerprinting wasn't an entirely new field of course - the Philips paper also cites several prior publications. The big limitation of all these methods is that they are robust to EQ and other degradations but not at all to variations in timing between frames. So they work well for identifying different instances of the same recording, but not at all for matching different performances, or for "query-by-humming".