Csaba Molnár

Barking in family dogs: an ethological approach.

Although it is one of the most conspicuous features of dog behaviour, barking has received little attention from ethologists or from an applied perspective. In this review, an ethological look is taken at the communicative aspect of dog barking. Emerging new research has indicated that in the repertoire of dog vocalisations barking has unique features in showing wide ranges of acoustic parameters, such as frequency, tonality and rhythmicity. Barking has been shown to be context dependent, and provides information for humans about the inner state of the dog although there are few indications that barking is used for intra-species communication. It is assumed that dog barking emerged through selective processes in which human preferences for certain acoustic aspects of the vocalisation may have been paramount. A more experiment-oriented approach is required for the study of dog vocalisation that could shed light on the possible communicative function of these acoustic signals.

Dogs discriminate between barks: The effect of context and identity of the caller

In the present study we explored whether dogs (Canis familiaris) are able to discriminate between conspecific barks emitted in different contexts recorded either from the same or different individuals. Playback experiments were conducted with dogs using barks as stimuli in a habituation-dishabituation paradigm. Barks were recorded in two contexts (stranger at the fence and when the dog was left alone) from different individuals. We found that dogs distinguished between barks emitted in these two contexts and were also able to discriminate between different individuals which were barking in the same context. These findings suggest that dog bark may carry context- and individual-specific information for the conspecifics.

Classification of dog barks: A machine learning approach

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

Seeing with ears: Sightless humans' perception of dog bark provides a test for structural rules in vocal communication

Prerecorded family dog (Canis familiaris) barks were played back to groups of congenitally sightless, sightless with prior visual experience, and sighted people (none of whom had ever owned a dog). We found that blind people without any previous canine visual experiences can categorize accurately various dog barks recorded in different contexts, and their results are very close to those of sighted people in characterizing the emotional content of barks. These findings suggest that humans can recognize some of the most important motivational states reflecting, for example, fear or aggression in a dogs bark without any visual experience. It is very likely that this result can be generalized to other mammalian species—that is, no visual experience of another individual is needed for recognizing some of the most important motivational states of the caller.