title: Classification of mysticete sounds using machine learning techniques.
abstract: Automatic classification of mysticete sounds has long been a challenging task in the bioacoustics field. The unknown statistical properties of the signals as well as the use of different recording apparatus and low signal-to-noise ratio conditions often lead to non-optimal systems. The goal of this paper is to design methods for the automatic classification of mysticete sounds using a restricted Boltzmann machine and a sparse auto-encoder that are widely used in the field of artificial intelligence. Experiments on five species of mysticetes are presented. The different methods are employed on the subset of species whose frequency range overlaps, as well as in all five species' calls. Moreover, results are offered with and without the use of a noise class. Overall, the systems are able to achieve an average classification accuracy of over 69% (with noise) and 80% (without noise) given the different architectures.
authors.affiliation: DYNI team, Laboratoire LSIS, UMR CNRS 7296, Université Sud Toulon-Var, Avenue de l'Université, BP20132, 83957 La Garde Cedex, France.
authors.forename: Xanadu C
authors.initials: XC
authors.lastname: Halkias
authors.forename: Sébastien
authors.initials: S
authors.lastname: Paris
authors.forename: Hervé
authors.initials: H
authors.lastname: Glotin
mesh_terms: D000162:Acoustics; D000818:Animals; D001185:Artificial Intelligence; D002591:Cetacea / Q000145:classification* / Q000502:physiology*; D004784:Environmental Monitoring / Q000379:methods*; D009791:Oceanography / Q000379:methods*; D009792:Oceans and Seas; D010363:Pattern Recognition, Automated; D012815:Signal Processing, Computer-Assisted; D013018:Sound Spectrography; D013045:Species Specificity; D013550:Swimming; D013997:Time Factors; D014828:Vocalization, Animal / Q000145:classification*