The authors investigated whether human listeners could categorize played-back dog (Canis familiaris) barks recorded in various situations and associate them with emotional ratings. Prerecorded barks of a Hungarian herding dog breed (Mudi) provided the sample. Human listeners were asked to rate emotionality of the vocalization and to categorize the situations on the basis of alternative situations provided on a questionnaire. The authors found almost no effect of previous experience with the given dog breed or of owning a dog. Listeners were able to categorize bark situations high above chance level. Emotionality ratings for particular bark samples correlated with peak and fundamental frequency and interbark intervals. The authors did not find a significant effect of tonality (harmonic-to-noise ratio) on either the emotionality rating or situation categorization of the human listeners. Humans' ability to recognize meaning suggests that barks could serve as an effective means of communication between dog and human.

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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.

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