Species-independent analysis and identification of emotional animal vocalizations
Abstract Animal vocalizations can differ depending on the context in which they are produced and serve as an instant indicator of an animal’s emotional state. Interestingly, from an evolutional perspective, it should be possible to directly compare different species using the same set of acoustic ma...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-08-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-14323-2 |
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| author | Stavros Ntalampiras |
| author_facet | Stavros Ntalampiras |
| author_sort | Stavros Ntalampiras |
| collection | DOAJ |
| description | Abstract Animal vocalizations can differ depending on the context in which they are produced and serve as an instant indicator of an animal’s emotional state. Interestingly, from an evolutional perspective, it should be possible to directly compare different species using the same set of acoustic markers. This paper proposes a deep neural network architecture for analysing and recognizing vocalizations representing positive and negative emotional states. Understanding these vocalizations is critical for advancing animal health and welfare, a subject of growing importance due to its ethical, environmental, economic, and public health implications. To this end, a framework assessing the relationships between vocalizations was developed. Towards keeping all potentially relevant audio content, the constructed framework operates on log-Mel spectrograms. Similarities/dissimilarities are learned by a suitably designed Siamese Neural Network composed of convolutional layers. The formed latent space is appropriately clustered to identify the support set facilitating the emotion classification task. We employed a publicly available dataset and followed a thorough experimental protocol. The efficacy of such a scheme is shown after extensive experiments considering both classification and support set selection. Last but not least, by elaborating collectively the network’s activations when processing positive and negative vocalizations, important differences in the time-frequency plane are evidenced across emotions and species, assisting their understanding from animal scientists. |
| format | Article |
| id | doaj-art-c468abd87b7b417bbfca0784cfc727a3 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-c468abd87b7b417bbfca0784cfc727a32025-08-20T03:45:53ZengNature PortfolioScientific Reports2045-23222025-08-0115111010.1038/s41598-025-14323-2Species-independent analysis and identification of emotional animal vocalizationsStavros Ntalampiras0Department of Computer Science, University of MilanAbstract Animal vocalizations can differ depending on the context in which they are produced and serve as an instant indicator of an animal’s emotional state. Interestingly, from an evolutional perspective, it should be possible to directly compare different species using the same set of acoustic markers. This paper proposes a deep neural network architecture for analysing and recognizing vocalizations representing positive and negative emotional states. Understanding these vocalizations is critical for advancing animal health and welfare, a subject of growing importance due to its ethical, environmental, economic, and public health implications. To this end, a framework assessing the relationships between vocalizations was developed. Towards keeping all potentially relevant audio content, the constructed framework operates on log-Mel spectrograms. Similarities/dissimilarities are learned by a suitably designed Siamese Neural Network composed of convolutional layers. The formed latent space is appropriately clustered to identify the support set facilitating the emotion classification task. We employed a publicly available dataset and followed a thorough experimental protocol. The efficacy of such a scheme is shown after extensive experiments considering both classification and support set selection. Last but not least, by elaborating collectively the network’s activations when processing positive and negative vocalizations, important differences in the time-frequency plane are evidenced across emotions and species, assisting their understanding from animal scientists.https://doi.org/10.1038/s41598-025-14323-2Animal health and welfareAudio pattern recognitionSpectral clusteringLatent representation |
| spellingShingle | Stavros Ntalampiras Species-independent analysis and identification of emotional animal vocalizations Scientific Reports Animal health and welfare Audio pattern recognition Spectral clustering Latent representation |
| title | Species-independent analysis and identification of emotional animal vocalizations |
| title_full | Species-independent analysis and identification of emotional animal vocalizations |
| title_fullStr | Species-independent analysis and identification of emotional animal vocalizations |
| title_full_unstemmed | Species-independent analysis and identification of emotional animal vocalizations |
| title_short | Species-independent analysis and identification of emotional animal vocalizations |
| title_sort | species independent analysis and identification of emotional animal vocalizations |
| topic | Animal health and welfare Audio pattern recognition Spectral clustering Latent representation |
| url | https://doi.org/10.1038/s41598-025-14323-2 |
| work_keys_str_mv | AT stavrosntalampiras speciesindependentanalysisandidentificationofemotionalanimalvocalizations |