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An acoustic fingerprint is a condensed digital summary, 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 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 bitwise fingerprints, 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.
Summaries of audio, and other content signals are known as 'signal abstracts' (literally, "signal summary") described originally in the following 'Method and device for monitoring and analyzing signals' US Patent No. 7,346,472 http://patft.uspto.gov/netacgi/nph-Parser?Sect1=PTO1&Sect2=HITOFF&d=PALL&p=1&u=%2Fnetahtml%2FPTO%2Fsrchnum.htm&r=1&f=G&l=50&s1=7,346,472.PN.&OS=PN/7,346,472&RS=PN/7,346,472 Edits of the patented invention[s] are politically motivated as patents are presumed valid under U.S. law.
Perceptual characteristics often exploited by audio fingerprints include average zero crossing rate, estimated tempo, average spectrum, spectral flatness, prominent tones across a set of bands, and bandwidth.
Most audio compression techniques (AAC, MP3, WMA, Vorbis) 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.
On the other hand, a good acoustic fingerprint algorithm must be able to identify a particular master recording among all the productions of an artist or group. For use as evidence in a court of law, an acoustic fingerprint method must be forensic in its accuracy.
- ISO IEC TR 21000-11 (2004), Multimedia framework (MPEG-21) -- Part 11: Evaluation Tools for Persistent Association Technologies
- AudioFingerprint at MusicBrainz
- A Review of Algorithms for Audio Fingerprinting (P. Cano et al. In International Workshop on Multimedia Signal Processing, US Virgin Islands, December 2002)
- Wang, Avery Li-Chun (2003). "An Industrial-Strength Audio Search Algorithm" (PDF). Shazam Entertainment. Retrieved 2012-09-04.
- Shazam and many other acoustic fingerprint companies have licensed the underlying patent[s] covering "signal abstracts". See http://www.bluespike.com/ for a listing of some of those co.s, including many that mark their products and services with the licensed patents. Soundmouse of London, UK
is among those co.s