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How index selection, compression, and recording schedule impact the description of ecological soundscapes
1. Acoustic indices derived from environmental soundscape recordings are being used to monitor ecosystem health and vocal animal biodiversity. Soundscape data can quickly become very expensive and difficult to manage, so data compression or temporal down‐sampling are sometimes employed to reduce dat...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8495811/ https://www.ncbi.nlm.nih.gov/pubmed/34646463 http://dx.doi.org/10.1002/ece3.8042 |
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author | Heath, Becky E. Sethi, Sarab S. Orme, C. David L. Ewers, Robert M. Picinali, Lorenzo |
author_facet | Heath, Becky E. Sethi, Sarab S. Orme, C. David L. Ewers, Robert M. Picinali, Lorenzo |
author_sort | Heath, Becky E. |
collection | PubMed |
description | 1. Acoustic indices derived from environmental soundscape recordings are being used to monitor ecosystem health and vocal animal biodiversity. Soundscape data can quickly become very expensive and difficult to manage, so data compression or temporal down‐sampling are sometimes employed to reduce data storage and transmission costs. These parameters vary widely between experiments, with the consequences of this variation remaining mostly unknown. 2. We analyse field recordings from North‐Eastern Borneo across a gradient of historical land use. We quantify the impact of experimental parameters (MP3 compression, recording length and temporal subsetting) on soundscape descriptors (Analytical Indices and a convolutional neural net derived AudioSet Fingerprint). Both descriptor types were tested for their robustness to parameter alteration and their usability in a soundscape classification task. 3. We find that compression and recording length both drive considerable variation in calculated index values. However, we find that the effects of this variation and temporal subsetting on the performance of classification models is minor: performance is much more strongly determined by acoustic index choice, with Audioset fingerprinting offering substantially greater (12%–16%) levels of classifier accuracy, precision and recall. 4. We advise using the AudioSet Fingerprint in soundscape analysis, finding superior and consistent performance even on small pools of data. If data storage is a bottleneck to a study, we recommend Variable Bit Rate encoded compression (quality = 0) to reduce file size to 23% file size without affecting most Analytical Index values. The AudioSet Fingerprint can be compressed further to a Constant Bit Rate encoding of 64 kb/s (8% file size) without any detectable effect. These recommendations allow the efficient use of restricted data storage whilst permitting comparability of results between different studies. |
format | Online Article Text |
id | pubmed-8495811 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-84958112021-10-12 How index selection, compression, and recording schedule impact the description of ecological soundscapes Heath, Becky E. Sethi, Sarab S. Orme, C. David L. Ewers, Robert M. Picinali, Lorenzo Ecol Evol Original Research 1. Acoustic indices derived from environmental soundscape recordings are being used to monitor ecosystem health and vocal animal biodiversity. Soundscape data can quickly become very expensive and difficult to manage, so data compression or temporal down‐sampling are sometimes employed to reduce data storage and transmission costs. These parameters vary widely between experiments, with the consequences of this variation remaining mostly unknown. 2. We analyse field recordings from North‐Eastern Borneo across a gradient of historical land use. We quantify the impact of experimental parameters (MP3 compression, recording length and temporal subsetting) on soundscape descriptors (Analytical Indices and a convolutional neural net derived AudioSet Fingerprint). Both descriptor types were tested for their robustness to parameter alteration and their usability in a soundscape classification task. 3. We find that compression and recording length both drive considerable variation in calculated index values. However, we find that the effects of this variation and temporal subsetting on the performance of classification models is minor: performance is much more strongly determined by acoustic index choice, with Audioset fingerprinting offering substantially greater (12%–16%) levels of classifier accuracy, precision and recall. 4. We advise using the AudioSet Fingerprint in soundscape analysis, finding superior and consistent performance even on small pools of data. If data storage is a bottleneck to a study, we recommend Variable Bit Rate encoded compression (quality = 0) to reduce file size to 23% file size without affecting most Analytical Index values. The AudioSet Fingerprint can be compressed further to a Constant Bit Rate encoding of 64 kb/s (8% file size) without any detectable effect. These recommendations allow the efficient use of restricted data storage whilst permitting comparability of results between different studies. John Wiley and Sons Inc. 2021-08-26 /pmc/articles/PMC8495811/ /pubmed/34646463 http://dx.doi.org/10.1002/ece3.8042 Text en © 2021 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Research Heath, Becky E. Sethi, Sarab S. Orme, C. David L. Ewers, Robert M. Picinali, Lorenzo How index selection, compression, and recording schedule impact the description of ecological soundscapes |
title | How index selection, compression, and recording schedule impact the description of ecological soundscapes |
title_full | How index selection, compression, and recording schedule impact the description of ecological soundscapes |
title_fullStr | How index selection, compression, and recording schedule impact the description of ecological soundscapes |
title_full_unstemmed | How index selection, compression, and recording schedule impact the description of ecological soundscapes |
title_short | How index selection, compression, and recording schedule impact the description of ecological soundscapes |
title_sort | how index selection, compression, and recording schedule impact the description of ecological soundscapes |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8495811/ https://www.ncbi.nlm.nih.gov/pubmed/34646463 http://dx.doi.org/10.1002/ece3.8042 |
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