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An acoustic fingerprint is a condensed digital summary, a fingerprint, deterministically generated from an audio signal, that can be used to identify an audio sample or quickly locate similar items in an audio database.
Practical uses of acoustic fingerprinting include identifying songs, melodies, tunes, or advertisements; sound effect library management; and video file identification. Media identification using acoustic fingerprints can be used to monitor the use of specific musical works and performances on radio broadcast, records, CDs, streaming media and peer-to-peer networks. This identification has been used in copyright compliance, licensing, and other monetization schemes.
A robust acoustic fingerprint algorithm must take into account the perceptual characteristics of the audio. If two files sound alike to the human ear, their acoustic fingerprints should match, even if their binary representations are quite different. Acoustic fingerprints are not hash functions, which must be sensitive to any small changes in the data. Acoustic fingerprints are more analogous to human fingerprints where small variations that are insignificant to the features the fingerprint uses are tolerated. One can imagine the case of a smeared human fingerprint impression which can accurately be matched to another fingerprint sample in a reference database; acoustic fingerprints work in a similar way.
Perceptual characteristics often exploited by audio fingerprints include average zero crossing rate, estimated tempo, average spectrum, spectral flatness, prominent tones across a set of frequency bands, and bandwidth.
Most audio compression techniques will make radical changes to the binary encoding of an audio file, without radically affecting the way it is perceived by the human ear. A robust acoustic fingerprint will allow a recording to be identified after it has gone through such compression, even if the audio quality has been reduced significantly. For use in radio broadcast monitoring, acoustic fingerprints should also be insensitive to analog transmission artifacts.
Any piece of audio can be translated to a spectrogram. Each piece of audio is split into some segments over time. In some cases adjacent segments share a common time boundary, in other cases adjacent segments might overlap. The result is a graph that plots three dimensions of audio: frequency vs amplitude (intensity) vs time.
Shazam's algorithm picks out points where there are peaks in the spectrogram which represent higher energy content. Focusing on peaks in the audio greatly reduces the impact that background noise has on audio identification. Shazam builds their fingerprint catalog out as a hash table, where the key is the frequency. They do not just mark a single point in the spectrogram, rather they mark a pair of points: the peak intensity plus a second anchor point. So their database key is not just a single frequency, it is a hash of the frequencies of both points. This leads to fewer hash collisions improving the performance of the hash table.
- ISO IEC TR 21000-11 (2004), Multimedia framework (MPEG-21) -- Part 11: Evaluation Tools for Persistent Association Technologies
- Surdu, Nicolae (January 20, 2011). "How does Shazam work to recognize a song?". Archived from the original on 2016-10-24. Retrieved 12 February 2018.
- Li-Chun Wang, Avery, An Industrial-Strength Audio Search Algorithm (PDF), Columbia University, retrieved 2018-04-02
- "How Shazam Works". Retrieved 2018-04-02.