Windy events detection in big bioacoustics datasets using a pre-trained Convolutional Neural Network Article Swipe
Francesca Terranova
,
Lorenzo Betti
,
Valeria Ferrario
,
Olivier Friard
,
Katrin Ludynia
,
Gavin Sean Petersen
,
Nicolas Mathevon
,
David Reby
,
Livio Favaro
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.scitotenv.2024.174868
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.scitotenv.2024.174868
Passive Acoustic Monitoring (PAM), which involves using autonomous record units for studying wildlife behaviour and distribution, often requires handling big acoustic datasets collected over extended periods. While these data offer invaluable insights about wildlife, their analysis can present challenges in dealing with geophonic sources. A major issue in the process of detection of target sounds is represented by wind-induced noise. This can lead to false positive detections, i.e., energy peaks due to wind gusts misclassified as biological sounds, or false negative, i.e., the wind noise masks the presence of biological sounds. Acoustic data dominated by wind noise makes the analysis of vocal activity unreliable, thus compromising the detection of target sounds and, subsequently, the interpretation of the results. Our work introduces a straightforward approach for detecting recordings affected by windy events using a pre-trained convolutional neural network. This process facilitates identifying wind-compromised data. We consider this dataset pre-processing crucial for ensuring the reliable use of PAM data. We implemented this preprocessing by leveraging YAMNet, a deep learning model for sound classification tasks. We evaluated YAMNet as-is ability to detect wind-induced noise and tested its performance in a Transfer Learning scenario by using our annotated data from the Stony Point Penguin Colony in South Africa. While the classification of YAMNet as-is achieved a precision of 0.71, and recall of 0.66, those metrics strongly improved after the training on our annotated dataset, reaching a precision of 0.91, and recall of 0.92, corresponding to a relative increment of >28 %. Our study demonstrates the promising application of YAMNet in the bioacoustics and ecoacoustics fields, addressing the need for wind-noise-free acoustic data. We released an open-access code that, combined with the efficiency and peak performance of YAMNet, can be used on standard laptops for a broad user base.
Related Topics
Concepts
Computer science
Bioacoustics
Convolutional neural network
Noise (video)
Transfer of learning
Preprocessor
Artificial intelligence
Process (computing)
Data pre-processing
Deep learning
Machine learning
Precision and recall
Artificial neural network
Speech recognition
Pattern recognition (psychology)
Telecommunications
Operating system
Image (mathematics)
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.scitotenv.2024.174868
- OA Status
- hybrid
- Cited By
- 5
- References
- 74
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4400850005
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4400850005Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1016/j.scitotenv.2024.174868Digital Object Identifier
- Title
-
Windy events detection in big bioacoustics datasets using a pre-trained Convolutional Neural NetworkWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-07-20Full publication date if available
- Authors
-
Francesca Terranova, Lorenzo Betti, Valeria Ferrario, Olivier Friard, Katrin Ludynia, Gavin Sean Petersen, Nicolas Mathevon, David Reby, Livio FavaroList of authors in order
- Landing page
-
https://doi.org/10.1016/j.scitotenv.2024.174868Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
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https://doi.org/10.1016/j.scitotenv.2024.174868Direct OA link when available
- Concepts
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Computer science, Bioacoustics, Convolutional neural network, Noise (video), Transfer of learning, Preprocessor, Artificial intelligence, Process (computing), Data pre-processing, Deep learning, Machine learning, Precision and recall, Artificial neural network, Speech recognition, Pattern recognition (psychology), Telecommunications, Operating system, Image (mathematics)Top concepts (fields/topics) attached by OpenAlex
- Cited by
-
5Total citation count in OpenAlex
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2025: 3, 2024: 2Per-year citation counts (last 5 years)
- References (count)
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74Number of works referenced by this work
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-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.using | 6, 131, 191 |
| abstract_inverted_index.vocal | 101 |
| abstract_inverted_index.which | 4 |
| abstract_inverted_index.windy | 129 |
| abstract_inverted_index.(PAM), | 3 |
| abstract_inverted_index.Colony | 200 |
| abstract_inverted_index.YAMNet | 174, 208, 254 |
| abstract_inverted_index.detect | 178 |
| abstract_inverted_index.energy | 68 |
| abstract_inverted_index.events | 130 |
| abstract_inverted_index.neural | 135 |
| abstract_inverted_index.noise. | 59 |
| abstract_inverted_index.recall | 216, 236 |
| abstract_inverted_index.record | 8 |
| abstract_inverted_index.sounds | 54, 110 |
| abstract_inverted_index.target | 53, 109 |
| abstract_inverted_index.tasks. | 171 |
| abstract_inverted_index.tested | 182 |
| abstract_inverted_index.Africa. | 203 |
| abstract_inverted_index.Passive | 0 |
| abstract_inverted_index.Penguin | 199 |
| abstract_inverted_index.YAMNet, | 163, 282 |
| abstract_inverted_index.ability | 176 |
| abstract_inverted_index.crucial | 148 |
| abstract_inverted_index.dataset | 146 |
| abstract_inverted_index.dealing | 40 |
| abstract_inverted_index.fields, | 260 |
| abstract_inverted_index.laptops | 288 |
| abstract_inverted_index.metrics | 220 |
| abstract_inverted_index.present | 37 |
| abstract_inverted_index.process | 49, 138 |
| abstract_inverted_index.sounds, | 77 |
| abstract_inverted_index.sounds. | 90 |
| abstract_inverted_index.Acoustic | 1, 91 |
| abstract_inverted_index.Learning | 188 |
| abstract_inverted_index.Transfer | 187 |
| abstract_inverted_index.achieved | 210 |
| abstract_inverted_index.acoustic | 20, 266 |
| abstract_inverted_index.activity | 102 |
| abstract_inverted_index.affected | 127 |
| abstract_inverted_index.analysis | 35, 99 |
| abstract_inverted_index.approach | 123 |
| abstract_inverted_index.combined | 274 |
| abstract_inverted_index.consider | 144 |
| abstract_inverted_index.dataset, | 229 |
| abstract_inverted_index.datasets | 21 |
| abstract_inverted_index.ensuring | 150 |
| abstract_inverted_index.extended | 24 |
| abstract_inverted_index.handling | 18 |
| abstract_inverted_index.improved | 222 |
| abstract_inverted_index.insights | 31 |
| abstract_inverted_index.involves | 5 |
| abstract_inverted_index.learning | 166 |
| abstract_inverted_index.network. | 136 |
| abstract_inverted_index.periods. | 25 |
| abstract_inverted_index.positive | 65 |
| abstract_inverted_index.presence | 87 |
| abstract_inverted_index.reaching | 230 |
| abstract_inverted_index.relative | 242 |
| abstract_inverted_index.released | 269 |
| abstract_inverted_index.reliable | 152 |
| abstract_inverted_index.requires | 17 |
| abstract_inverted_index.results. | 117 |
| abstract_inverted_index.scenario | 189 |
| abstract_inverted_index.sources. | 43 |
| abstract_inverted_index.standard | 287 |
| abstract_inverted_index.strongly | 221 |
| abstract_inverted_index.studying | 11 |
| abstract_inverted_index.training | 225 |
| abstract_inverted_index.wildlife | 12 |
| abstract_inverted_index.annotated | 193, 228 |
| abstract_inverted_index.behaviour | 13 |
| abstract_inverted_index.collected | 22 |
| abstract_inverted_index.detecting | 125 |
| abstract_inverted_index.detection | 51, 107 |
| abstract_inverted_index.dominated | 93 |
| abstract_inverted_index.evaluated | 173 |
| abstract_inverted_index.geophonic | 42 |
| abstract_inverted_index.increment | 243 |
| abstract_inverted_index.negative, | 80 |
| abstract_inverted_index.precision | 212, 232 |
| abstract_inverted_index.promising | 251 |
| abstract_inverted_index.wildlife, | 33 |
| abstract_inverted_index.Monitoring | 2 |
| abstract_inverted_index.addressing | 261 |
| abstract_inverted_index.autonomous | 7 |
| abstract_inverted_index.biological | 76, 89 |
| abstract_inverted_index.challenges | 38 |
| abstract_inverted_index.efficiency | 277 |
| abstract_inverted_index.introduces | 120 |
| abstract_inverted_index.invaluable | 30 |
| abstract_inverted_index.leveraging | 162 |
| abstract_inverted_index.recordings | 126 |
| abstract_inverted_index.application | 252 |
| abstract_inverted_index.detections, | 66 |
| abstract_inverted_index.facilitates | 139 |
| abstract_inverted_index.identifying | 140 |
| abstract_inverted_index.implemented | 158 |
| abstract_inverted_index.open-access | 271 |
| abstract_inverted_index.performance | 184, 280 |
| abstract_inverted_index.pre-trained | 133 |
| abstract_inverted_index.represented | 56 |
| abstract_inverted_index.unreliable, | 103 |
| abstract_inverted_index.bioacoustics | 257 |
| abstract_inverted_index.compromising | 105 |
| abstract_inverted_index.demonstrates | 249 |
| abstract_inverted_index.ecoacoustics | 259 |
| abstract_inverted_index.wind-induced | 58, 179 |
| abstract_inverted_index.convolutional | 134 |
| abstract_inverted_index.corresponding | 239 |
| abstract_inverted_index.distribution, | 15 |
| abstract_inverted_index.misclassified | 74 |
| abstract_inverted_index.preprocessing | 160 |
| abstract_inverted_index.subsequently, | 112 |
| abstract_inverted_index.classification | 170, 206 |
| abstract_inverted_index.interpretation | 114 |
| abstract_inverted_index.pre-processing | 147 |
| abstract_inverted_index.straightforward | 122 |
| abstract_inverted_index.wind-noise-free | 265 |
| abstract_inverted_index.wind-compromised | 141 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 94 |
| corresponding_author_ids | https://openalex.org/A5077244767 |
| countries_distinct_count | 5 |
| institutions_distinct_count | 9 |
| corresponding_institution_ids | https://openalex.org/I55143463 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/7 |
| sustainable_development_goals[0].score | 0.7300000190734863 |
| sustainable_development_goals[0].display_name | Affordable and clean energy |
| citation_normalized_percentile.value | 0.91720017 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |