Classification of dog barks: a machine learning approach

Animal Cognition
Csaba MolnárAdám Miklósi

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.

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Citations

Nov 6, 2010·Animal Cognition·Alexandre LerchLaurent Nagle
May 1, 2012·Animal Cognition·Marcello SiniscalchiAngelo Quaranta
Oct 10, 2008·PloS One·Marcello SiniscalchiLesley J Rogers
Mar 3, 2011·The Journal of the Acoustical Society of America·Nicolas GiretDalila Bovet
Jul 15, 2009·Behavioural Processes·Csaba MolnárAdám Miklósi
Feb 3, 2009·The Veterinary Journal·Péter PongráczAdám Miklósi
Dec 1, 2015·Laterality·Marcello SiniscalchiAngelo Quaranta
Jun 28, 2016·Behavioural Processes·Éloïse C DéauxJennifer A Clarke
Sep 23, 2016·PloS One·Hjalmar K TuressonVictor Hugo C de Albuquerque
Aug 2, 2018·Animals : an Open Access Journal From MDPI·Marcello SiniscalchiAngelo Quaranta
Apr 16, 2021·Current Zoology·Olga V SibiryakovaElena V Volodina
Sep 25, 2021·Scientific Reports·Richard PolichtVlastimil Hart

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