Multiclass CNN Approach for Automatic Classification of Dolphin Vocalizations
Monitoring dolphins in the open sea is essential for understanding their behavior and the impact of human activities on the marine ecosystems. Passive Acoustic Monitoring (PAM) is a non-invasive technique for tracking dolphins, providing continuous data. This study presents a novel approach for clas...
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MDPI AG
2025-04-01
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| author | Francesco Di Nardo Rocco De Marco Daniel Li Veli Laura Screpanti Benedetta Castagna Alessandro Lucchetti David Scaradozzi |
| author_facet | Francesco Di Nardo Rocco De Marco Daniel Li Veli Laura Screpanti Benedetta Castagna Alessandro Lucchetti David Scaradozzi |
| author_sort | Francesco Di Nardo |
| collection | DOAJ |
| description | Monitoring dolphins in the open sea is essential for understanding their behavior and the impact of human activities on the marine ecosystems. Passive Acoustic Monitoring (PAM) is a non-invasive technique for tracking dolphins, providing continuous data. This study presents a novel approach for classifying dolphin vocalizations from a PAM acoustic recording using a convolutional neural network (CNN). Four types of common bottlenose dolphin (<i>Tursiops truncatus</i>) vocalizations were identified from underwater recordings: whistles, echolocation clicks, burst pulse sounds, and feeding buzzes. To enhance classification performances, edge-detection filters were applied to spectrograms, with the aim of removing unwanted noise components. A dataset of nearly 10,000 spectrograms was used to train and test the CNN through a 10-fold cross-validation procedure. The results showed that the CNN achieved an average accuracy of 95.2% and an F1-score of 87.8%. The class-specific results showed a high accuracy for whistles (97.9%), followed by echolocation clicks (94.5%), feeding buzzes (94.0%), and burst pulse sounds (92.3%). The highest F1-score was obtained for whistles, exceeding 95%, while the other three vocalization typologies maintained an F1-score above 80%. This method provides a promising step toward improving the passive acoustic monitoring of dolphins, contributing to both species conservation and the mitigation of conflicts with fisheries. |
| format | Article |
| id | doaj-art-e32b03881315406eb4bd23cf7d6f0178 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-e32b03881315406eb4bd23cf7d6f01782025-08-20T03:13:57ZengMDPI AGSensors1424-82202025-04-01258249910.3390/s25082499Multiclass CNN Approach for Automatic Classification of Dolphin VocalizationsFrancesco Di Nardo0Rocco De Marco1Daniel Li Veli2Laura Screpanti3Benedetta Castagna4Alessandro Lucchetti5David Scaradozzi6Dipartimento di Ingegneria dell’informazione, Università Politecnica delle Marche, 60131 Ancona, ItalyInstitute of Biological Resources and Marine Biotechnology (IRBIM), National Research Council (CNR), 60125 Ancona, ItalyInstitute of Biological Resources and Marine Biotechnology (IRBIM), National Research Council (CNR), 60125 Ancona, ItalyDipartimento di Ingegneria dell’informazione, Università Politecnica delle Marche, 60131 Ancona, ItalyDipartimento di Ingegneria dell’informazione, Università Politecnica delle Marche, 60131 Ancona, ItalyInstitute of Biological Resources and Marine Biotechnology (IRBIM), National Research Council (CNR), 60125 Ancona, ItalyDipartimento di Ingegneria dell’informazione, Università Politecnica delle Marche, 60131 Ancona, ItalyMonitoring dolphins in the open sea is essential for understanding their behavior and the impact of human activities on the marine ecosystems. Passive Acoustic Monitoring (PAM) is a non-invasive technique for tracking dolphins, providing continuous data. This study presents a novel approach for classifying dolphin vocalizations from a PAM acoustic recording using a convolutional neural network (CNN). Four types of common bottlenose dolphin (<i>Tursiops truncatus</i>) vocalizations were identified from underwater recordings: whistles, echolocation clicks, burst pulse sounds, and feeding buzzes. To enhance classification performances, edge-detection filters were applied to spectrograms, with the aim of removing unwanted noise components. A dataset of nearly 10,000 spectrograms was used to train and test the CNN through a 10-fold cross-validation procedure. The results showed that the CNN achieved an average accuracy of 95.2% and an F1-score of 87.8%. The class-specific results showed a high accuracy for whistles (97.9%), followed by echolocation clicks (94.5%), feeding buzzes (94.0%), and burst pulse sounds (92.3%). The highest F1-score was obtained for whistles, exceeding 95%, while the other three vocalization typologies maintained an F1-score above 80%. This method provides a promising step toward improving the passive acoustic monitoring of dolphins, contributing to both species conservation and the mitigation of conflicts with fisheries.https://www.mdpi.com/1424-8220/25/8/2499convolutional neural networksdeep learningdolphinspassive acoustic monitoring |
| spellingShingle | Francesco Di Nardo Rocco De Marco Daniel Li Veli Laura Screpanti Benedetta Castagna Alessandro Lucchetti David Scaradozzi Multiclass CNN Approach for Automatic Classification of Dolphin Vocalizations Sensors convolutional neural networks deep learning dolphins passive acoustic monitoring |
| title | Multiclass CNN Approach for Automatic Classification of Dolphin Vocalizations |
| title_full | Multiclass CNN Approach for Automatic Classification of Dolphin Vocalizations |
| title_fullStr | Multiclass CNN Approach for Automatic Classification of Dolphin Vocalizations |
| title_full_unstemmed | Multiclass CNN Approach for Automatic Classification of Dolphin Vocalizations |
| title_short | Multiclass CNN Approach for Automatic Classification of Dolphin Vocalizations |
| title_sort | multiclass cnn approach for automatic classification of dolphin vocalizations |
| topic | convolutional neural networks deep learning dolphins passive acoustic monitoring |
| url | https://www.mdpi.com/1424-8220/25/8/2499 |
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