Automatic individual dog recognition based on the acoustic properties of its barks

Abstract

The bark is a very distinctive vocalization of the dog. It is very common and a mean for interaction with humans. However, the scope of their automated analysis by computational techniques, as well as the possible applications to which they can give rise have been little explored. In this study, we describe the process to develop an automatic classifier that can identify individual dogs based on their barks. We created a database with more than 6,000 barks applying positive and negative stimuli to dogs. We acoustically characterized the barking samples using a signal processing tool that extracts large sets of features. Based on these sets, we generated optimal subsets of features to feed machine learning algorithms which trained classification models. We evaluated such models and compared the classification performance of different algorithms. We analyzed the pertinence of training specific models per each breed. The classification obtained outperform the results previously reported in similar works. Our findings suggest that practical applications could be constructed on this kind of technology.