title: Comparing supervised learning methods for classifying sex, age, context and individual Mudi dogs from barking.
abstract: 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, [Formula: see text]-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 [Formula: see text]-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 [Formula: see text]-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.
authors.affiliation: Student at the Universidad Alfonso X El Sabio, Av. Universidad, 1, 28691, Villanueva de la Cañada, Madrid, Spain.
authors.forename: Ana
authors.initials: A
authors.lastname: Larrañaga
authors.forename: Concha
authors.initials: C
authors.lastname: Bielza
authors.forename: Péter
authors.initials: P
authors.lastname: Pongrácz
authors.forename: Tamás
authors.initials: T
authors.lastname: Faragó
authors.forename: Anna
authors.initials: A
authors.lastname: Bálint
authors.forename: Pedro
authors.initials: P
authors.lastname: Larrañaga
mesh_terms: D000162:Acoustics; D000367:Age Factors; D000465:Algorithms; D000818:Animals; D001499:Bayes Theorem; D004285:Dogs / Q000502:physiology* / Q000523:psychology; D005260:Female; D016015:Logistic Models; D000069550:Machine Learning; D008297:Male; D012727:Sex Characteristics; D013018:Sound Spectrography; D014828:Vocalization, Animal