A Deep Learning System for Emotional Cat Sound Classification and Generation 


Vol. 13,  No. 10, pp. 492-496, Oct.  2024
https://doi.org/10.3745/TKIPS.2024.13.10.492


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  Abstract

Cats are known to express their emotions through a variety of vocalizations during interactions. These sounds reflect their emotional states, making the understanding and interpretation of these sounds crucial for more effective communication. Recent advancements in artificial intelligence has introduced research related to emotion recognition, particularly focusing on the analysis of voice data using deep learning models. Building on this background, the study aims to develop a deep learning system that classifies and generates cat sounds based on their emotional content. The classification model is trained to accurately categorize cat vocalizations by emotion. The sound generation model, which uses deep learning based models such as SampleRNN, is designed to produce cat sounds that reflect specific emotional states. The study finally proposes an integrated system that takes recorded cat vocalizations, classify them by emotion, and generate cat sounds based on user requirements.

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[IEEE Style]

J. Y. Shim, S. Lim, J. Kim, "A Deep Learning System for Emotional Cat Sound Classification and Generation," The Transactions of the Korea Information Processing Society, vol. 13, no. 10, pp. 492-496, 2024. DOI: https://doi.org/10.3745/TKIPS.2024.13.10.492.

[ACM Style]

Joo Yong Shim, SungKi Lim, and Jong-Kook Kim. 2024. A Deep Learning System for Emotional Cat Sound Classification and Generation. The Transactions of the Korea Information Processing Society, 13, 10, (2024), 492-496. DOI: https://doi.org/10.3745/TKIPS.2024.13.10.492.

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