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CRIM's content-based audio copy detection system for TRECVID 2009.

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Title: CRIM's content-based audio copy detection system for TRECVID 2009.
Authors: Gupta, Vishwa, Boulianne, Gilles, Cardinal, Patrick
Source: Multimedia Tools & Applications; Sep2012, Vol. 60 Issue 2, p371-387, 17p
Subject Terms: AUDIO communication, DEMODULATION, COPYING, COMPUTER file sharing, ALGORITHMS
Abstract: We report results on audio copy detection for TRECVID 2009 copy detection task. This task involves searching for transformed audio queries in over 385 h of test audio. The queries were transformed in seven different ways, three of them involved mixing unrelated speech to the original query, making it a much more difficult task. We give results with two different audio fingerprints and show that mapping each test frame to the nearest query frame (nearest-neighbor fingerprint) results in robust audio copy detection. The most difficult task in TRECVID 2009 was to detect audio copies using predetermined thresholds computed from 2008 data. We show that the nearest-neighbor fingerprints were robust to even this task and gave actual minimal normalized detection cost rate (NDCR) of around 0.06 for all the transformations. These results are close to those obtained by using the optimal threshold for each transform. This result shows the robustness of the nearest-neighbor fingerprints. These nearest-neighbor fingerprints can be efficiently computed on a graphics processing unit, leading to a very fast search. [ABSTRACT FROM AUTHOR]
Copyright of Multimedia Tools & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Complementary Index
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  Data: CRIM's content-based audio copy detection system for TRECVID 2009.
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  Data: Multimedia Tools & Applications; Sep2012, Vol. 60 Issue 2, p371-387, 17p
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  Data: <searchLink fieldCode="DE" term="%22AUDIO+communication%22">AUDIO communication</searchLink><br /><searchLink fieldCode="DE" term="%22DEMODULATION%22">DEMODULATION</searchLink><br /><searchLink fieldCode="DE" term="%22COPYING%22">COPYING</searchLink><br /><searchLink fieldCode="DE" term="%22COMPUTER+file+sharing%22">COMPUTER file sharing</searchLink><br /><searchLink fieldCode="DE" term="%22ALGORITHMS%22">ALGORITHMS</searchLink>
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  Data: We report results on audio copy detection for TRECVID 2009 copy detection task. This task involves searching for transformed audio queries in over 385 h of test audio. The queries were transformed in seven different ways, three of them involved mixing unrelated speech to the original query, making it a much more difficult task. We give results with two different audio fingerprints and show that mapping each test frame to the nearest query frame (nearest-neighbor fingerprint) results in robust audio copy detection. The most difficult task in TRECVID 2009 was to detect audio copies using predetermined thresholds computed from 2008 data. We show that the nearest-neighbor fingerprints were robust to even this task and gave actual minimal normalized detection cost rate (NDCR) of around 0.06 for all the transformations. These results are close to those obtained by using the optimal threshold for each transform. This result shows the robustness of the nearest-neighbor fingerprints. These nearest-neighbor fingerprints can be efficiently computed on a graphics processing unit, leading to a very fast search. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Multimedia Tools & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Type: general
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      – SubjectFull: COPYING
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              Text: Sep2012
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