Automatic detection and classification of bearded seal vocalizations in the northeastern Chukchi Sea using convolutional neural networks
| Author(s) | Escobar-Amado, Christian D. | |
| Author(s) | Badiey, Mohsen | |
| Author(s) | Pecknold, Sean | |
| Date Accessioned | 2022-09-23T13:09:41Z | |
| Date Available | 2022-09-23T13:09:41Z | |
| Publication Date | 2022-01-19 | |
| Description | Copyright 2022 Acoustical Society of America. This article may be downloaded for personal use only. Any other use requires prior permission of the author and the Acoustical Society of America. The following article appeared in Christian. D. Escobar-Amado, Mohsen. Badiey, and Sean. Pecknold, "Automatic detection and classification of bearded seal vocalizations in the northeastern Chukchi Sea using convolutional neural networks", The Journal of the Acoustical Society of America 151, 299-309 (2022) https://doi.org/10.1121/10.0009256 and may be found at https://doi.org/10.1121/10.0009256. | en_US |
| Abstract | Bearded seals vocalizations are often analyzed manually or by using automatic detections that are manually validated. In this work, an automatic detection and classification system (DCS) based on convolutional neural networks (CNNs) is proposed. Bearded seal sounds were year-round recorded by four spatially separated receivers on the Chukchi Continental Slope in Alaska in 2016–2017. The DCS is divided in two sections. First, regions of interest (ROI) containing possible bearded seal vocalizations are found by using the two-dimensional normalized cross correlation of the measured spectrogram and a representative template of two main calls of interest. Second, CNNs are used to validate and classify the ROIs among several possible classes. The CNNs are trained on 80% of the ROIs manually labeled from one of the four spatially separated recorders. When validating on the remaining 20%, the CNNs show an accuracy above 95.5%. To assess the generalization performance of the networks, the CNNs are tested on the remaining recorders, located at different positions, with a precision above 89.2% for the main class of the two types of calls. The proposed technique reduces the laborious task of manual inspection prone to inconstant bias and possible errors in detections. | en_US |
| Sponsor | This research was supported by the Office of Naval Research Ocean Acoustics Program (ONR OA322) under Grant Nos. N00014-15-1-2110, N00014-18-1-2140, and N00014-21-1-2760. | en_US |
| Citation | Christian. D. Escobar-Amado, Mohsen. Badiey, and Sean. Pecknold , "Automatic detection and classification of bearded seal vocalizations in the northeastern Chukchi Sea using convolutional neural networks", The Journal of the Acoustical Society of America 151, 299-309 (2022) https://doi.org/10.1121/10.0009256 | en_US |
| ISSN | 1520-8524 | |
| URL | https://udspace.udel.edu/handle/19716/31410 | |
| Language | en_US | en_US |
| Publisher | The Journal of the Acoustical Society of America | en_US |
| Title | Automatic detection and classification of bearded seal vocalizations in the northeastern Chukchi Sea using convolutional neural networks | en_US |
| Type | Article | en_US |
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