Deep Learning and Domain Transfer for Orca Vocalization Detection

Paul Best (1, 2) , Maxence Ferrari (3, 1, 2) , Marion Poupard (1, 2) , Sébastien Paris (1, 2) , Ricard Marxer (1, 2) , Helena Symonds , Paul Spong , Hervé Glotin (1, 2)

1 DYNI - DYNamiques de l’Information
2 LIS - Laboratoire d'Informatique et des Systèmes (LIS) (Marseille, Toulon)
3 LAMFA - Laboratoire Amiénois de Mathématique Fondamentale et Appliquée - UMR CNRS 7352

In this paper, we study the difficulties of domain transfer when training deep learning models, on a specific task that is orca vocalization detection. Deep learning appears to be an answer to many sound recognition tasks in human speech analysis as well as in bioacoustics. This method allows to learn from large amounts of data, and find the best scoring way to discriminate between classes (e.g. orca vocalization and other sounds). However, to learn the perfect data representation and discrimination boundaries, all possible data configurations need to be processed. This causes problems when those configurations are ever changing (e.g. in our experiment, a change in the recording system happened to considerably disturb our previously well performing model). We thus explore approaches to compensate on the difficulties faced with domain transfer, with two convolutionnal neural networks (CNN) architectures, one that works in the time-frequency domain, and one that works directly on the time domain.

Mots clés: End-to-end sound recognition; Orca Vocalizations; Deep Convolutionnal Neural Networks; Spectral sound recognition

Domaines: Machine Learning [stat.ML]; Traitement du signal et de l'image [eess.SP]

Paul Best, Maxence Ferrari, Marion Poupard, Sébastien Paris, Ricard Marxer, et al.. Deep Learning and Domain Transfer for Orca Vocalization Detection. International joint conference on neural networks, Jul 2020, glasgow, United Kingdom. ⟨hal-02865300⟩