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Sparse Representation-Based Classification of Mysticete Calls

Thomas Guilment François-Xavier Socheleau 1, 2 Dominique Pastor 3, 2 Simon Vallez 4
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
Abstract : This paper presents an automatic classification method dedicated to mysticete calls. This method relies on sparse representations which assume that mysticete calls lie in a linear subspace described by a dictionary-based representation. The classifier accounts for noise by refusing to assign the observed signal to a given class if it is not included into the linear subspace spanned by the dictionaries of mysticete calls. Rejection of noise is achieved without feature learning. In addition, the proposed method is modular in that, call classes can be appended to or removed from the classifier without requiring retraining. The classifier is easy to design since it relies on a few parameters. Experiments on five types of mysticete calls are presented. It includes Antarctic blue whale Z-calls, two types of "Madagascar" pygmy blue whale calls, fin whale 20 Hz calls and North-Pacific blue whale D-calls. On this dataset, containing 2185 calls and 15000 noise samples, an average recall of 96.4% is obtained and 93.3% of the noise data (persistent and transient) are correctly rejected by the classifier.
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Submitted on : Tuesday, October 9, 2018 - 6:22:32 PM
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Thomas Guilment, François-Xavier Socheleau, Dominique Pastor, Simon Vallez. Sparse Representation-Based Classification of Mysticete Calls. Journal of the Acoustical Society of America, Acoustical Society of America, 2018, 144 (3), pp.1550. ⟨10.1121/1.5055209⟩. ⟨hal-01891652⟩



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