Molnár, Csaba, Kaplan, Frédéric, Roy, Pierre, Pachet, Francois, Pongrácz, Péter, Dóka, Antal and Miklósi, Ádám Classification of dog barks: a machine learning approach. Animal Cognition, 11(3):389-400, 2008.

Sony CSL authors: Frédéric Kaplan, François Pachet, Pierre Roy


In this study we analyzed the possible contextspeciWc and individual-speciWc features of dog barks using a new machine-learning algorithm. A pool containing more than 6,000 barks, which were recorded in six diVerent 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 diVerent contexts and from diVerent individuals, could be distinguished from another. The program conducted this task by analyzing barks emitted in previously identiWed contexts by identiWed dogs. After the best feature set had been obtained (with which the highest identiWcation rate was achieved), the eYciency of the algorithm was tested in a classiWcation 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 eYciency of 43% and with an eYciency of 52% of the barking individuals. These Wndings suggest that dog barks have context-speciWc and individual-speciWc acoustic features. In our opinion, this machine learning method may provide an eYcient tool for analyzing acoustic data in various behavioral studies.

Keywords: feature generation, ethology


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BibTeX entry

@ARTICLE { molnar:08a, AUTHOR="Molnár, Csaba and Kaplan, Frédéric and Roy, Pierre and Pachet, Francois and Pongrácz, Péter and Dóka, Antal and Miklósi, Ádám", JOURNAL="Animal Cognition", NUMBER="3", PAGES="389-400", TITLE="Classification of dog barks: a machine learning approach", VOLUME="11", YEAR="2008", }