Dolphin Health Classifications from Whistle Features
Bottlenose dolphins often conceal behavioral signs of illness until they reach an advanced stage. Motivated by the efficacy of vocal biomarkers in human health diagnostics, we utilized supervised machine learning methods to assess various model architectures’ effectiveness in classifying dolphin hea...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/12/12/2158 |
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| author | Brittany Jones Jessica Sportelli Jeremy Karnowski Abby McClain David Cardoso Maximilian Du |
| author_facet | Brittany Jones Jessica Sportelli Jeremy Karnowski Abby McClain David Cardoso Maximilian Du |
| author_sort | Brittany Jones |
| collection | DOAJ |
| description | Bottlenose dolphins often conceal behavioral signs of illness until they reach an advanced stage. Motivated by the efficacy of vocal biomarkers in human health diagnostics, we utilized supervised machine learning methods to assess various model architectures’ effectiveness in classifying dolphin health status from the acoustic features of their whistles. A gradient boosting classifier achieved a 72.3% accuracy in distinguishing between normal and abnormal health states—a significant improvement over chance (permutation test; 1000 iterations, <i>p</i> < 0.001). The model was trained on 30,693 whistles from 15 dolphins and the test set (15%) totaled 3612 ‘normal’ and 1775 ‘abnormal’ whistles. The classifier identified the health status of the dolphin from the whistles features with 72.3% accuracy, 73.2% recall, 56.1% precision, and a 63.5% F1 score. These findings suggest the encoding of internal health information within dolphin whistle features, with indications that the severity of illness correlates with classification accuracy, notably in its success for identifying ‘critical’ cases (94.2%). The successful development of this diagnostic tool holds promise for furnishing a passive, non-invasive, and cost-effective means for early disease detection in bottlenose dolphins. |
| format | Article |
| id | doaj-art-de681ff34a32438e9c97e0f29cc5740d |
| institution | OA Journals |
| issn | 2077-1312 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-de681ff34a32438e9c97e0f29cc5740d2025-08-20T02:00:28ZengMDPI AGJournal of Marine Science and Engineering2077-13122024-11-011212215810.3390/jmse12122158Dolphin Health Classifications from Whistle FeaturesBrittany Jones0Jessica Sportelli1Jeremy Karnowski2Abby McClain3David Cardoso4Maximilian Du5Naval Information Warfare Center Pacific, 53560 Hull Street, San Diego, CA 92152, USANational Marine Mammal Foundation: 3131, 2240 Shelter Island Dr, San Diego, CA 92106, USAUniversity of California San Diego, 9500 Gilman Dr, La Jolla, CA 92093, USANaval Information Warfare Center Pacific, 53560 Hull Street, San Diego, CA 92152, USASan Diego State University, 5500 Campanile Drive, San Diego, CA 92182, USAStanford University, 450 Jane Stanford Way, Stanford, CA 94305, USABottlenose dolphins often conceal behavioral signs of illness until they reach an advanced stage. Motivated by the efficacy of vocal biomarkers in human health diagnostics, we utilized supervised machine learning methods to assess various model architectures’ effectiveness in classifying dolphin health status from the acoustic features of their whistles. A gradient boosting classifier achieved a 72.3% accuracy in distinguishing between normal and abnormal health states—a significant improvement over chance (permutation test; 1000 iterations, <i>p</i> < 0.001). The model was trained on 30,693 whistles from 15 dolphins and the test set (15%) totaled 3612 ‘normal’ and 1775 ‘abnormal’ whistles. The classifier identified the health status of the dolphin from the whistles features with 72.3% accuracy, 73.2% recall, 56.1% precision, and a 63.5% F1 score. These findings suggest the encoding of internal health information within dolphin whistle features, with indications that the severity of illness correlates with classification accuracy, notably in its success for identifying ‘critical’ cases (94.2%). The successful development of this diagnostic tool holds promise for furnishing a passive, non-invasive, and cost-effective means for early disease detection in bottlenose dolphins.https://www.mdpi.com/2077-1312/12/12/2158bioacousticsmachine learninghealth classifierdolphin whistles |
| spellingShingle | Brittany Jones Jessica Sportelli Jeremy Karnowski Abby McClain David Cardoso Maximilian Du Dolphin Health Classifications from Whistle Features Journal of Marine Science and Engineering bioacoustics machine learning health classifier dolphin whistles |
| title | Dolphin Health Classifications from Whistle Features |
| title_full | Dolphin Health Classifications from Whistle Features |
| title_fullStr | Dolphin Health Classifications from Whistle Features |
| title_full_unstemmed | Dolphin Health Classifications from Whistle Features |
| title_short | Dolphin Health Classifications from Whistle Features |
| title_sort | dolphin health classifications from whistle features |
| topic | bioacoustics machine learning health classifier dolphin whistles |
| url | https://www.mdpi.com/2077-1312/12/12/2158 |
| work_keys_str_mv | AT brittanyjones dolphinhealthclassificationsfromwhistlefeatures AT jessicasportelli dolphinhealthclassificationsfromwhistlefeatures AT jeremykarnowski dolphinhealthclassificationsfromwhistlefeatures AT abbymcclain dolphinhealthclassificationsfromwhistlefeatures AT davidcardoso dolphinhealthclassificationsfromwhistlefeatures AT maximiliandu dolphinhealthclassificationsfromwhistlefeatures |