Acoustic fingerprint

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.[1]

Practical uses of acoustic fingerprinting include identifying songs, melodies, tunes, or advertisements; sound effect library management; video file identification; and much more. 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.

Contents

Attributes

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. Note that acoustic fingerprint matching may be a distance measure between feature vectors, and not a straight binary match. Therefore, 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.

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 (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.[citation needed]

Implementations

This is a list of products notable for acoustic fingerprinting. Products on this list should either have an accompanying existing article link which verifies their notability for acoustic fingerprinting, or reliable sources as footnotes against the name showing they are notable for this reason.

  • Acoustid is an open source project that aims to create a free database of audio fingerprints with mapping to the MusicBrainz metadata database and provide a web service for audio file identification using this database.[2]
  • All Media Guide's LASSO is a commercial service that uses acoustic fingerprinting, and other techniques, to recognize music. (U.S. Patent 7,277,766)
  • Audible Magic Corporation is a commercial venture that provides electronic media identification and copyright management solutions using proprietary acoustic fingerprinting technology U.S. Patent 5,918,223 based on original research by Muscle Fish Consulting[3]
  • AudioID is a commercial technology for automatically identifying audio material using acoustic fingerprints. It was developed by the German Fraunhofer Institute.
  • Audio Comparer is a duplicate song finder software. The program uses acoustic fingerprints for similar song detection.
  • Auditude is an online video advertising company. It uses patented Auditude Connect video and audio fingerprinting technology to identify content, serve ads and search content on major video sharing web sites.
  • Gracenote's MusicID is a commercial product that uses acoustic fingerprinting along with other methods to identify music.
    • The Nero Multimedia Suite Nero (software suite) version 9 and 10 uses Gracenote to add metadata like author, title and genre to an audio file.
    • Sony Ericsson's TrackID software uses Gracenote to identify songs being recorded via cell phone in a way similar to Shazam.
    • Winamp version 5.5 uses Gracenote to power automatic playlist generation with "Nullsoft Playlist Generator" plugin that comes with the software.
  • Echoprint is an open source music fingerprint and resolving framework powered by the The Echo Nest.
  • Last.fm have begun their own method of acoustic fingerprinting in 2007 via the Fingerprinter application.[4] The technology is now included in the Last.fm client software.
  • Midomi is a commercial service that can match music clips, as well as identifying a song that is sung or hummed
  • Moodagent is a commercial service from Syntonetic that combines digital signal processing and AI techniques to create music profiles that incorporate characteristics such as mood, emotion, genre, style, instrument, vocals, orchestration, production, and beat/tempo.
  • MusicBrainz, a free and open content project for a music database that uses MusicIP's Open Fingerprint Architecture for fingerprinting and the MusicDNS service for identifying audio files.
  • SoundHound, an acoustic fingerprint-based service with web and mobile applications (Android and iPhone) that allows songs or hummed tunes to be identified using the Midomi service.
  • Shazam, an acoustic fingerprint-based service allows for songs to be identified via cell phone.
  • Tunatic by Wildbits is an application that allows identifying music while being played, analyzing the songs and comparing with the information on a server

See also

References

  1. ^ ISO IEC TR 21000-11 (2004), Multimedia framework (MPEG-21) -- Part 11: Evaluation Tools for Persistent Association Technologies
  2. ^ acoustid.org
  3. ^ "Content-Based Classification, Search, and Retrieval of Audio," IEEE MultiMedia, vol. 3, no. 3, pp. 27-36, Sept., 1996.
  4. ^ Last.fm blog

External links


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