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227:, which are 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 similarly.
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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
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Any piece of audio can be translated into a spectrogram. Each piece of audio is split into 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
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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
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301:'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
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A Review of
Algorithms for Audio Fingerprinting (P. Cano et al. In International Workshop on Multimedia Signal Processing, US Virgin Islands, December 2002)
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identification. Media identification using acoustic fingerprints can be used to monitor the use of specific musical works and performances on
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309:, 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
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317:. So their database key is not just a single frequency, it is a hash of the frequencies of both points. This leads to fewer
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Content-Based
Retrieval of Music and Audio by Jonathan Foote, ISS, National University of Singapore.
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Multimedia framework (MPEG-21) -- Part 11: Evaluation Tools for
Persistent Association Technologies
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networks. This identification has been used in copyright compliance, licensing, and other
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Perceptual characteristics often exploited by audio fingerprints include average
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has on audio identification. Shazam builds their fingerprint catalog out as a
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monitoring, acoustic fingerprints should also be insensitive to analog
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282:. One common technique is creating a time-frequency graph called a
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Practical uses of acoustic fingerprinting include identifying
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Condensed digital summary generated from an audio signal
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Generating a signature from the audio is essential for
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For acoustic emissions of ships and submarines, see
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427:An Industrial-Strength Audio Search Algorithm
321:improving the performance of the hash table.
399:"How does Shazam work to recognize a song?"
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127:Learn how and when to remove this message
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537:Computational auditory scene analysis
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159:or quickly locate similar items in a
65:adding citations to reliable sources
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397:Surdu, Nicolae (January 20, 2011).
155:, that can be used to identify an
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262:reduced significantly. For use in
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143:is a condensed digital summary, a
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290:amplitude (intensity) vs time.
52:needs additional citations for
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336:Automatic content recognition
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384:ISO IEC TR 21000-11 (2004),
341:Digital video fingerprinting
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542:Music information retrieval
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610:Fingerprinting algorithms
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186:library management; and
605:Acoustic fingerprinting
584:3D sound reconstruction
18:Acoustic fingerprinting
76:"Acoustic fingerprint"
579:3D sound localization
433:, Columbia University
424:Li-Chun Wang, Avery,
527:Acoustic fingerprint
141:acoustic fingerprint
61:improve this article
562:Speaker recognition
145:digital fingerprint
567:Speech recognition
451:"How Shazam Works"
356:Perceptual hashing
346:Feature extraction
280:searching by sound
151:generated from an
32:Acoustic signature
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574:Sound recognition
552:Speech processing
516:Computer audition
453:. 10 January 2009
366:Sound recognition
259:audio compression
244:spectral flatness
149:deterministically
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16:(Redirected from
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532:Audio mining
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455:. Retrieved
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407:. Retrieved
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351:Parsons code
315:anchor point
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212:monetization
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184:sound effect
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157:audio sample
153:audio signal
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59:Please help
54:verification
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409:12 February
331:Chromaprint
284:spectrogram
274:Spectrogram
270:artifacts.
599:Categories
457:2018-04-02
437:2018-04-02
372:References
307:hash table
238:, average
218:Attributes
188:video file
87:newspapers
252:bandwidth
214:schemes.
117:June 2011
325:See also
240:spectrum
172:melodies
196:records
101:scholar
299:Shazam
294:Shazam
250:, and
206:, and
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431:(PDF)
257:Most
236:tempo
178:, or
176:tunes
168:songs
108:JSTOR
94:books
411:2018
80:news
200:CDs
139:An
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