title: Classification of dog barks: a machine learning approach.
abstract: In this study we analyzed the possible context-specific and individual-specific features of dog barks using a new machine-learning algorithm. A pool containing more than 6,000 barks, which were recorded in six different communicative situations was used as the sound sample. The algorithm's task was to learn which acoustic features of the barks, which were recorded in different contexts and from different individuals, could be distinguished from another. The program conducted this task by analyzing barks emitted in previously identified contexts by identified dogs. After the best feature set had been obtained (with which the highest identification rate was achieved), the efficiency of the algorithm was tested in a classification task in which unknown barks were analyzed. The recognition rates we found were highly above chance level: the algorithm could categorize the barks according to their recorded situation with an efficiency of 43% and with an efficiency of 52% of the barking individuals. These findings suggest that dog barks have context-specific and individual-specific acoustic features. In our opinion, this machine learning method may provide an efficient tool for analyzing acoustic data in various behavioral studies.
authors.affiliation: Department of Ethology, Eötvös Loránd University, Pázmány Péter sétány 1/C, 1117, Budapest, Hungary. molcsa@gmail.com
authors.forename: Csaba
authors.initials: C
authors.lastname: Molnár
authors.forename: Frédéric
authors.initials: F
authors.lastname: Kaplan
authors.forename: Pierre
authors.initials: P
authors.lastname: Roy
authors.forename: François
authors.initials: F
authors.lastname: Pachet
authors.forename: Péter
authors.initials: P
authors.lastname: Pongrácz
authors.forename: Antal
authors.initials: A
authors.lastname: Dóka
authors.forename: Adám
authors.initials: A
authors.lastname: Miklósi
mesh_terms: D000162:Acoustics / Q000295:instrumentation; D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D004193:Discrimination Learning; D004285:Dogs; D005260:Female; D008297:Male; D013018:Sound Spectrography / Q000379:methods* / Q000662:veterinary; D014828:Vocalization, Animal / Q000145:classification*