Document Type

Article

Language

eng

Format of Original

5 p.

Publication Date

7-2015

Publisher

Acoustical Society of America

Source Publication

Journal of the Acoustical Society of America

Source ISSN

0001-4966

Original Item ID

DOI: 10.1121/1.4922768, PubMed Central: PMID: 26233050

Abstract

Asian Small-Clawed Otters (Aonyx cinerea) are a small, protected but threatened species living in freshwater. They are gregarious and live in monogamous pairs for their lifetimes, communicating via scent and acoustic vocalizations. This study utilized a hidden Markov model (HMM) to classify stress versus non-stress calls from a sibling pair under professional care. Vocalizations were expertly annotated by keepers into seven contextual categories. Four of these—aggression, separation anxiety, pain, and prefeeding—were identified as stressful contexts, and three of them—feeding, training, and play—were identified as non-stressful contexts. The vocalizations were segmented, manually categorized into broad vocal type call types, and analyzed to determine signal to noise ratios. From this information, vocalizations from the most common contextual categories were used to implement HMM-based automatic classification experiments, which included individual identification, stress vs non-stress, and individual context classification. Results indicate that both individual identity and stress vs non-stress were distinguishable, with accuracies above 90%, but that individual contexts within the stress category were not easily separable.

Comments

Published version. Journal of the Acoustical Society of America, Vol. 138, No. 1 (July 2015): 105-109. DOI. © 2015 Acoustical Society of America. Used with permission.

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