Using Self-Organizing Maps to Recognize Acoustic Units Associated with Information Content in Animal Vocalizations

Kohonen self-organizing neural networks, also called self-organizing maps (SOMs), have been used successfully to recognize human phonemes and in this way to aid in human speech recognition. This paper describes how SOMS also can be used to associate specific information content with animal vocalizations. A SOM was used to identify acoustic units in Gunnison’s prairie dog alarm calls that were vocalized in the presence of three different predator species. Some of these acoustic units and their combinations were found exclusively in the alarm calls associated with a particular predator species and were used to associate predator species information with individual alarm calls. This methodology allowed individual alarm calls to be classified by predator species with an average of 91% accuracy. Furthermore, the topological structure of the SOM used in these experiments provided additional insights about the acoustic units and their combinations that were used to classify the target alarm calls. An important benefit of the methodology developed in this paper is that it could be used to search for groups of sounds associated with information content for any animal whose vocalizations are composed of multiple simultaneous frequency components

Acoustic Structures in the Alarm Calls of Gunnison’s Prairie Dogs

Acoustic structures of sound in Gunnison’s prairie dog alarm calls are described, showing how these acoustic structures may encode information about three different predator species (red-tailed hawk—Buteo jamaicensis; domestic dog—Canis familaris; and coyote—Canis latrans). By dividing each alarm call into 25 equal-sized partitions and using resonant frequencies within each partition, commonly occurring acoustic structures were identified as components of alarm calls for the three predators. Although most of the acoustic structures appeared in alarm calls elicited by all three predator species, the frequency of occurrence of these acoustic structures varied among the alarm calls for the different predators, suggesting that these structures encode identifying information for each of the predators. A classification analysis of alarm calls elicited by each of the three predators showed that acoustic structures could correctly classify 67% of the calls elicited by domestic dogs, 73% of the calls elicited by coyotes, and 99% of the calls elicited by red-tailed hawks. The different distributions of acoustic structures associated with alarm calls for the three predator species suggest a duality of function, one of the design elements of language listed by Hockett [in Animal Sounds and Communication, edited by W. E. Lanyon and W. N. Tavolga (American Institute of Biological Sciences, Washington, DC, 1960), pp. 392–430]

Using Self-Organizing Maps to Recognize Acoustic Units Associated with Information Content in Animal Vocalizations

Kohonen self-organizing neural networks, also called self-organizing maps (SOMs), have been used successfully to recognize human phonemes and in this way to aid in human speech recognition. This paper describes how SOMS also can be used to associate specific information content with animal vocalizations. A SOM was used to identify acoustic units in Gunnison’s prairie dog alarm calls that were vocalized in the presence of three different predator species. Some of these acoustic units and their combinations were found exclusively in the alarm calls associated with a particular predator species and were used to associate predator species information with individual alarm calls. This methodology allowed individual alarm calls to be classified by predator species with an average of 91% accuracy. Furthermore, the topological structure of the SOM used in these experiments provided additional insights about the acoustic units and their combinations that were used to classify the target alarm calls. An important benefit of the methodology developed in this paper is that it could be used to search for groups of sounds associated with information content for any animal whose vocalizations are composed of multiple simultaneous frequency components

Acoustic Structures in the Alarm Calls of Gunnison’s Prairie Dogs

Acoustic structures of sound in Gunnison’s prairie dog alarm calls are described, showing how these acoustic structures may encode information about three different predator species (red-tailed hawk—Buteo jamaicensis; domestic dog—Canis familaris; and coyote—Canis latrans). By dividing each alarm call into 25 equal-sized partitions and using resonant frequencies within each partition, commonly occurring acoustic structures were identified as components of alarm calls for the three predators. Although most of the acoustic structures appeared in alarm calls elicited by all three predator species, the frequency of occurrence of these acoustic structures varied among the alarm calls for the different predators, suggesting that these structures encode identifying information for each of the predators. A classification analysis of alarm calls elicited by each of the three predators showed that acoustic structures could correctly classify 67% of the calls elicited by domestic dogs, 73% of the calls elicited by coyotes, and 99% of the calls elicited by red-tailed hawks. The different distributions of acoustic structures associated with alarm calls for the three predator species suggest a duality of function, one of the design elements of language listed by Hockett [in Animal Sounds and Communication, edited by W. E. Lanyon and W. N. Tavolga (American Institute of Biological Sciences, Washington, DC, 1960), pp. 392–430]