Animal Cognition

, Volume 18, Issue 2, pp 405–421 | Cite as

Comparing supervised learning methods for classifying sex, age, context and individual Mudi dogs from barking

  • Ana Larrañaga
  • Concha Bielza
  • Péter Pongrácz
  • Tamás Faragó
  • Anna Bálint
  • Pedro LarrañagaEmail author
Original Paper


Barking is perhaps the most characteristic form of vocalization in dogs; however, very little is known about its role in the intraspecific communication of this species. Besides the obvious need for ethological research, both in the field and in the laboratory, the possible information content of barks can also be explored by computerized acoustic analyses. This study compares four different supervised learning methods (naive Bayes, classification trees, \(k\)-nearest neighbors and logistic regression) combined with three strategies for selecting variables (all variables, filter and wrapper feature subset selections) to classify Mudi dogs by sex, age, context and individual from their barks. The classification accuracy of the models obtained was estimated by means of \(K\)-fold cross-validation. Percentages of correct classifications were 85.13 % for determining sex, 80.25 % for predicting age (recodified as young, adult and old), 55.50 % for classifying contexts (seven situations) and 67.63 % for recognizing individuals (8 dogs), so the results are encouraging. The best-performing method was \(k\)-nearest neighbors following a wrapper feature selection approach. The results for classifying contexts and recognizing individual dogs were better with this method than they were for other approaches reported in the specialized literature. This is the first time that the sex and age of domestic dogs have been predicted with the help of sound analysis. This study shows that dog barks carry ample information regarding the caller’s indexical features. Our computerized analysis provides indirect proof that barks may serve as an important source of information for dogs as well.


Mudi dog barks Acoustic communication Feature subset selection Machine learning Supervised classification \(K\)-fold cross-validation 



This work has been partially supported by the Spanish Ministry of Economy and Competitiveness, projects Cajal Blue Brain (C080020-09; the Spanish partner of the Blue Brain Project initiative from EPFL) and TIN2013-41592-P, by the János Bolyai Research Scholarship from the Hungarian Academy of Sciences, and co-financed by a grant from OTKA K82020. The authors are thankful to Celeste R. Pongrácz for the English proofreading of this manuscript and to Nikolett Czinege for help in preparing the bark databases.

Supplementary material

10071_2014_811_MOESM1_ESM.pdf (43 kb)
Supplementary material 1 (pdf 42 KB)


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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Ana Larrañaga
    • 1
  • Concha Bielza
    • 3
  • Péter Pongrácz
    • 2
  • Tamás Faragó
    • 2
  • Anna Bálint
    • 2
  • Pedro Larrañaga
    • 3
    Email author
  1. 1.Student at the Universidad Alfonso X El SabioVillanueva de la CañadaSpain
  2. 2.Department of Ethology, Biological InstituteEötvös Loránd UniversityBudapestHungary
  3. 3.Computational Intelligence GroupUniversidad Politecnica de MadridBoadilla del MonteSpain

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