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In this study we describe the design and application of an automated classification system that utilizes artificial intelligence to corroborate the finding that Gunnison's prairie dogs have different alarm calls for different species of predators. This corroboration is strong because it utilizes an entirely different analysis technique than that used in the original research by Slobodchikoff et al. [Slobodchikoff, C.N., Fischer, C., Shapiro, J., 1986. Predator-specific alarm calls of prairie dogs. Am. Zool. 26, 557] or in subsequent study done by Slobodchikoff et al. [Slobodchikoff, C.N., Kiriazis, J., Fischer, C., Creef, E., 1991. Semantic information distinguishing individual predators in the alarm calls of Gunnison's...
In this study, we present a methodology that identifies acoustic units in Gunnison's prairie dog alarm calls and then uses those units to classify the alarm calls and bouts according to the species of predator that was present when the calls were vocalized. While traditional methods measure specific acoustic parameters in order to describe a vocalization, our method uses the variation in the internal structure of a vocalization to define possible information structures. Using a simple representation similar to that used in human speech to identify vowel sounds, a software system was developed that uses this representation to recognize acoustic units in prairie dog alarm calls. These acoustic units are then used...