Abstract
In this talk, I will give an overview of the Shazam audio recognition technology. The Shazam service takes a query comprised of a short sample of ambient audio (as little as 2 seconds) from a microphone and searches a massive database of recordings comprising up to 40 million songs. The query audio may be distorted with significant additive noise (<0 dB SNR), environmental acoustics, as well as nonlinear distortions. The computational scaling is such that a query may cost as little as a millisecond of processing time. Previous algorithms could index hundreds of items, required seconds of processing time, and were less tolerant to noise and distortion by 20-30 dB SNR. In aggregate, the Shazam algorithm represented a leap of more than 1E + 9 in efficiency. I will discuss the various innovations leading to this result.
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