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, \(k\)-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.
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Acevedo M, Corrada-Bravo C, Corrada-Bravo H, Villanueva-Rivera L, Aide T (2009) Automated classification of bird and amphibian calls using machine learning: a comparison of methods. Ecol Inform 4(4):206–214
Adachi I, Kuwahata H, Fujita K (2007) Dogs recall their owner’s face upon hearing the owner’s voice. Anim Cogn 10:17–21
Adams M, Law B, Gibson M (2010) Reliable automation of bat call identification for Eastern New South Wales, Australia, using classification trees and AnaScheme software. Acta Chiropterol 12(1):231–245
Armitage D, Ober H (2010) A comparison of supervised learning techniques in the classification of bat echolocation calls. Ecol Inform 5(6):465–473
Au W, Andersen L, Roitblat ARH, Nachtigall P (1995) Neural network modeling of a dolphin’s sonar discrimination capabilities. J Acoust Soc Am 98:43–50
Bálint A, Faragó T, Dóka A, Miklósi A, Pongrácz P (2013) “Beware, I am big and non-dangerous!”—playfully growling dogs are perceived larger than their actual size by their canine audience. Appl Anim Behav Sci 148:128–137
Bielza C, Li G, Larrañaga P (2011) Multi-dimensional classification with Bayesian networks. Int J Approx Reason 52:705–727
Blumstein D, Munos O (2005) Individual, age and sex-specific information is contained in yellow-bellied marmot alarm calls. Anim Behav 69(2):353–361
Borchani H, Bielza C, Martínez-Martín P, Larrañaga P (2012) Markov blanket-based approach for learning multi-dimensional Bayesian network classifiers: an application to predict the European Quality of Life-5 Dimensions (EQ-5D) from the 39-item Parkinson’s Disease Questionnaire (PDQ-39). J Biomed Inform 45:1175–1184
Britzke E, Duchamp J, Murray K, Swihart R, Robbins L (2011) Acoustic identification of bats in the Eastern United States: a comparison of parametric and nonparametric methods. J Wildl Manage 75(3):660–667
Charrier I, Aubin T, Mathevon N (2010) Mother-calf vocal communication in Atlantic walrus: a first field experimental study. Anim Cogn 13(3):471–482
Cheng J, Sun Y, Ji L (2010) A call-independent and automatic acoustic system for the individual recognition of animals: a novel model using four passerines. Pattern Recogn 43(11):3846–3852
Chesmore E (2001) Application of time domain signal coding and artificial neural networks to passive acoustical identification of animals. Appl Acoust 62(12):1359–1374
Clemins P (2005) Automatic Classification of Animal Vocalizations. PhD thesis, Marquete University
Cohen J, Fox M (1976) Vocalizations in wild canids and possible effects of domestication. Behav Process 1:77–92
Coppinger R, Feinstein M (1991) “Hark! Hark! the dogs bark\(ldots\)” and bark and hark. Smithonian 21:119–128
Druzhkova A, Thalmann O, Trifonov V, Leonard J, Vorobieva N, Ovodov N, ASGraphodatsky, Wayne R (2013) Ancient DNA analysis affirms the canid from Altai as a primitive dog. PLoS ONE 8(e57):754
Duda R, Hart P, Stork D (2001) Pattern classification. Wiley, New York
Fant G (1976) Acoustic theory of speech production. Mouton De Gruyter.
Faragó T, Pongrácz P, Miklósi A, Huber L, Virányi Z, Range F (2010a) Dogs’ expectation about signalers’ body size by virtue of their growls. PLoS ONE 5(12):e15,175
Faragó T, Pongrácz P, Range F, Virányi Z, Miklósi A (2010b) The bone is mine’: affective and referential aspects of dog growls. Anim Behav 79(4):917–925
Feddersen-Petersen DU (2000) Vocalization of European wolves (Canis lupus lupus l.) and various dog breeds (Canis lupus f. fam.). Arch Tierz Dummerstorf 43(4):387–397
Fix E, Hodges JL (1951) Discriminatory analysis. Nonparametric discrimination: consistency properties. USAF Sch Aviat Med 4:261–279
Frommolt KH, Goltsman M, MacDonald D (2003) Barking foxes, Alopex lagopus: field experiments in individual recognition in a territorial mammal. Anim Behav 65:509–518
Goodwin M, Gooding KM, Regnier F (1979) Sex pheromone in the dog. Science 203:559–561
Gunasekaran S, Revathy K (2011) Automatic recognition and retrieval of wild animal vocalizations. Int J Comput Theor Eng 3(1):136–140
Hall M (1999) Correlation-based feature selection for machine learning. PhD thesis, Department of Computer Science, University of Waikato, UK
Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten I (2009) The WEKA data mining software: an update. SIGKDD Explor 11(1):10–18
Hartwig S (2005) Individual acoustic identification as a non-invasive conservation tool: an approach to the conservation of the African wild dog Lycaon pictus (Temminck, 1820). Bioacoustics 15:35–50
Hecht J, Miklósi A, Gácsi M (2012) Behavioral assessment and owner perceptions of behaviors associated with guilt in dogs. Appl Anim Behav Sci 139:134–142
Hunag C, Yang Y, Yang D, Chen Y (2009) Frog classification using machine learning techniques. Expert Syst Appl 36(2):3737–3743
Jain A, Murty MN, Flynn P (1999) Data clustering: a review. ACM Comput Surv 31(3):264–323
Le Cessie S, van Houwelingen JC (1992) Ridge estimators in logistic regression. Appl Stat 41(1):191–201
Liu H, Motoda H (1998) Feature selection for knowledge discovery and data mining. Kluwer Academic, Dordrecht
Lord K, Feinstein M, Coppinger R (2000) Barking and mobbing. Behav Process 81:358–368
Manser M, Seyfarth R, Cheney D (2002) Suricate alarm calls signal predator class and urgency. Trends Cogn Sci 6(2):55–57
Maros K, Pongrácz P, Bárdos G, Molnár C, Faragó T, Miklósi A (2008) Dogs can discriminate barks from different situations. Appl Anim Behav Sci 114:159–167
Mazzini F, Townsend SW, Virányi Z, Range F (2013) Wolf howling is mediated by relationship quality rather than underlying emotional stress. Curr Biol 23:1677–1680
McConnell PB (1990) Acoustic structure and receiver response in domestic dogs, Canis familiaris. Anim Behav 39:897–904
McConnell PB, Baylis JR (1985) Interspecific communication in cooperative herding: acoustic and visual signals from human shepherds and herding dogs. Z Tierpsychol 67:302–382
Mech LD (1999) Alpha status, dominance and division of labor in wolf packs. Can J Zool 77:1196–1203
Meints K, Racca A, Hickey N (2010) Child-dog misunderstandings: children misinterpret dogs’ facial expressions. In: Proceedings of the 2nd Canine Science Forum, p 99
Miklósi A, Polgárdi R, Topál J, Csányi V (2000) Intentional behaviour in dog-human communication: an experimental analysis of “showing” behaviour in the dog. Anim Cogn 3:159–166
Minsky M (1961) Steps toward artificial intelligence. T Ins Radio Eng 49:8–30
Molnár C, Pongrácz P, Dóka A, Miklósi A (2006) Can humans discriminate between dogs on the base of the acoustic parameters of barks? Behav Process 73:76–83
Molnár C, Kaplan F, Roy P, Pachet F, Pongrácz P, Dóka A, Moklósi A (2008) Classification of dog barks: a machine learning approach. Anim Cogn 11:389–400
Molnár C, Pongrácz P, Faragó T, Dóka A, Miklósi A (2009) Dogs discriminate between barks: the effect of context and identity of the caller. Behav Process 82(2):198–201
Morton E (1977) On the occurrence and significance of motivation—structural rules in some bird and mammal sounds. Am Nat 111:855–869
Netto W, Planta D (1997) Behavioural testing for aggression in the domestic dog. Appl Anim Behav Sci 52:243–263
Overall K, Dunham A, Frank D (2001) Frequency of nonspecific clinical signs in dogs with separation anxiety, thunderstorm phobia, and noise phobia, alone or in combination. J Am Vet Med Assoc 219:467–473
Parsons S (2001) Identification of New Zeeland bats (Chalinobus tuberculatus and Mystacina tuberculata) in flight from analysis of echolocation calls by artificial neural networks. J Zool 253(4):447–456
Parsons S, Jones G (2000) Acoustic identification of twelve species of echolocating bat by discriminant function analysis and artificial neural networks. J Exp Biol 203(17):2641–2656
Pongrácz P, Molnár C, Miklósi A, Csányi V (2005) Human listeners are able to classify dog (canis familiaris) barks recorded in different situations. J Comp Psychol 119:136–144
Pongrácz P, Molnár C, Miklósi A (2006) Acoustic parameters of dog barks carry emotional information for humans. Appl Anim Behav Sci 100:228–240
Pongrácz P, Molnár C, Miklósi A (2010) Barking in family dogs: an ethological approach. Vet J 183:141–147
Pongrácz P, Szabó E, Kis A, Péter A, Miklósi A (2014) More than noise? Field investigations of intraspecific acoustic communication in dogs (Canis familiaris). Appl Anim Behav Sci (in press)
Quinlan R (1993) C4.5: programs for machine learning. Morgan Kaufmann
Reid P (2009) Adapting to the human world: dogs’ responsiveness to our social cues. Behav Process 80:325–333
Roch M, Soldevilla M, Hoenigman R, Wiggins S, Hidebrand J (2008) Comparison of machine learning techniques for the classification of echolocation clicks from three species of odontocetes. Can Acoust 36(1):41–47
Root-Gutteridge H, Bencsik M, Chebli M, Gentle L, Terrell-Nield C, Bourit A, Yarnell RW (2013) Improving individual identification in captive Eastern grey wolves (Canis lupus lycaon) using the time course of howl amplitudes. Bioacoustics 23(1):39–53
Saeys Y, Inza I, Larrañaga P (2007) A review of feature selection techniques in bioinformatics. Bioinformatics 23(19):2507–2517
Serpell J, Hsu Y (2001) Development and validation of a novel method for evaluating behavior and temperament in guide dogs. Appl Anim Behav Sci 72:347–364
Smith A, Birnie A, Lane K, French J (2009) Production and perception of sex differences in vocalizations of wied’s black-tufted-ear marmosets (callithrix kuhlii). Am J Primatol 71:324–332
Stone M (1974) Cross-validatory choice and assessment of statistical predictions. J R Stat Soc B 36(2):111–147
Sucar E, Bielza C, Morales E, Hernandez-Leal P, Zaragoza J, Larrañaga P (2014) Multi-label classification with Bayesian network-based chain classifiers. Pattern Recogn Lett 41:14–22
Taylor A, Reby D, McComb K (2008) Human listeners attend to size information in domestic dog growls. J Acoust Soc Am 123(5):2903–2909
Taylor A, Reby D, McComb K (2009) Context-related variation in the vocal growling behaviour of the domestic dog (Canis familiaris). Ethology 115(10):905–915
Taylor A, Reby D, McComb K (2010) Size communication in domestic dog, Canis familiaris, growls. Anim Behav 79(1):205–210
Téglás E, Gergely A, Kupán K, Miklósi A, Topál J (2012) Dogs’ gaze following is tuned to human communicative signals. Curr Biol 22:1–4
Tembrock G (1976) Canid vocalizations. Behav Process 1:57–75
Volodin I, Volodina E, Klenova A, Filatova O (2005) Individual and sexual differences in the calls of the monomorphic white-faced whistling duck dendrocygna viduata. Acta Ornithol 40:43–52
Wan M, Bolger N, Champagne F (2012) Human perception of fear in dogs varies according to experience with dogs. PLoS ONE 7(e51):775
Yeon SC (2007) The vocal communication of canines. J Vet Behav 2:141–144
Yin S (2002) A new perspective on barking in dogs (Canis familiaris). J Comp Psychol 116:189–193
Yin S, McCowan B (2004) Barking in domestic dogs: context specificity and individual identification. Anim Behav 68:343–355
Yovel Y, Au WWL (2010) How can dolphins recognize fish according to their echoes? A statistical analysis of fish echoes. PLoS ONE 5(11):e14,054
Acknowledgments
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.
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Larrañaga, A., Bielza, C., Pongrácz, P. et al. Comparing supervised learning methods for classifying sex, age, context and individual Mudi dogs from barking. Anim Cogn 18, 405–421 (2015). https://doi.org/10.1007/s10071-014-0811-7
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DOI: https://doi.org/10.1007/s10071-014-0811-7