Affiliations: [a]
Mexican National Research Council (CONACyT), Mexico
| [b] CICESE-UT 3, Andador 10 #109, Ciudad del Conocimiento, Tepic, Nayarit, México | [c] Computer Science Department. Instituto Nacional de Astrofísica Óptica y Electrónica (INAOE). Puebla, México
Abstract: The barking and other vocalizations of the domestic dog are an exciting source of information. Studies in the area of ethology have analyzed their function and the way humans and conspecifics perceive them. Without a doubt, better understanding the nature of barking can bring benefits both, to improve the welfare of dogs, and for humans who can build systems that take advantage of the information extracted from vocalizations for applications, such as, security, assistance, and entertainment. To develop automatic systems for the analysis of domestic dog vocalizations, we need to have acoustic characterization methods that allow capturing the most relevant properties of barking and thereby improving the performance of automatic classifiers. In this paper, a comparison between several acoustic characterization techniques is made to determine their relevance in the classification of two aspects of the barking, which are the context in which they were generated and the identity of the dog that emitted the bark. We classified the tested acoustic features as qualitative and quantitative. The quantitative are derived from the processing of low-level acoustic descriptors and have been used most widely in audio analysis. The qualitative ones are a type of acoustic that capture aspects related to the perception of the melody of the vocalizations and had not been previously tested in this field of application.
