Abstract

Animal communication takes place via vocalizations, body posture, movement, facial expressions, and context. Although there are some animal communications known to humans, decoding the internal state of an animal correctly may be challenging, especially if there are different species of animals. An idea of an AI Animal Translator is introduced in this paper. It is supposed that the system would perform audio analysis, computer vision, machine learning, and other context-related operations to classify the animals' sounds and behaviour into understandable human messages like hunger, fear, pain, stress, comfort, alertness, or any social activity. The proposed system would include data collection, preprocessing, features extraction, multimodal classification, confidence assessment, and alerting for users. This device should be used as an auxiliary system to help people to better comprehend animals' communication, but not as a direct translation from one language to another. The review of related scientific trends, the problem statement, the architecture of the proposed system, evaluation methodology, applications, limitations, ethical considerations, and further research are covered in the paper.

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