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

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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.

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Notes

  1. These features are not strictly fingerprints, as their value changes when we change the query.

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Authors and Affiliations

  1. Centre de recherche informatique de Montréal (CRIM), 405, avenue Ogilvy, bureau 101, Montréal, Quebec, Canada, H3N 1M3

    Vishwa Nath Gupta, Gilles Boulianne & Patrick Cardinal

Authors
  1. Vishwa Nath Gupta
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  2. Gilles Boulianne
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  3. Patrick Cardinal
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Correspondence to Vishwa Nath Gupta.

Additional information

This work was supported in part by the Natural Science and Engineering Research Council of Canada (NSERC).

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Gupta, V.N., Boulianne, G. & Cardinal, P. CRIM’s content-based audio copy detection system for TRECVID 2009. Multimed Tools Appl 60, 371–387 (2012). https://doi.org/10.1007/s11042-010-0608-x

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  • DOI: https://doi.org/10.1007/s11042-010-0608-x

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