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Article Dans Une Revue Applied Artificial Intelligence Année : 2011

A Public Audio Identification Evaluation Framework for Broadcast Monitoring

Résumé

This paper presents the first public framework for the evaluation of audio fingerprinting techniques. Although the domain of audio identification is very active, both in the industry and the academic world, there is nowadays no common basis to compare the proposed techniques. This is because corpuses and evaluation protocols differ between the authors. The framework we present here corresponds to a use-case in which audio excerpts have to be detected in a radio broadcast stream. This scenario indeed naturally provides a large variety of audio distortions that makes this task a real challenge for fingerprinting systems. Scoring metrics are discussed, with regard to this particular scenario. We then describe a whole evaluation framework including an audio corpus, along with the related groundtruth annotation, and a toolkit for the computation of the score metrics. An example of application of this framework is finally detailed. This took place during the evaluation campaign of the Quaero project. This evaluation framework is publicly available for download and constitutes a simple, yet thorough, platform that can be used by the community in the field of audio identification, to encourage reproducible results.
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Dates et versions

hal-01987797 , version 1 (21-01-2019)

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  • HAL Id : hal-01987797 , version 1

Citer

Mathieu Ramona, Sébastien Fenet, Raphaël Blouet, Hervé Bredin, Thomas Fillon, et al.. A Public Audio Identification Evaluation Framework for Broadcast Monitoring. Applied Artificial Intelligence, 2011. ⟨hal-01987797⟩
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