Dataset of the paper "Soundscape dynamics of a cold protected forest: dominance of aircraft noise"
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| Format: | Recurso digital |
| Langue: | anglais |
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Zenodo
2024
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| _version_ | 1866902322403082240 |
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| author | Haupert, Sylvain Grinfeder, Elie Sueur, Jérôme Sèbe, Frédéric Ducrettet, Manon Barlet, Julien Reynet, Marie-Pierre |
| author_facet | Haupert, Sylvain Grinfeder, Elie Sueur, Jérôme Sèbe, Frédéric Ducrettet, Manon Barlet, Julien Reynet, Marie-Pierre |
| contents | <p>Annotated training dataset used to train an artificial neural network (ANN) with mel frequency cepstral coefficients (MFCC) to systematically detect the occurrence of silence and sounds coming from birds, mammals, insects (biophony), rain (geophony), wind (geophony), and aircraft (anthropophony).</p> <p>The training dataset contains <strong>1314 audio recordings</strong> (i.e. soundscapes) collected in a temperate cold climax forest locally named ‘Risoux’. The forest is located in the East of France in the Jura Mountains at the Swiss border and the closest city is Morez (46° 31′ 22″ N, 6° 01′ 23″ E). The forest soundscape was recorded using four automatic SongMeter 4 recorders (Wildlife Acoustics Inc, Concord, MA, USA). These recorders were installed across the Risoux Forest central zone, along a West–East axis. The distance between the recorders was 1.00 ± 0.10 km. The position of the recorders was chosen to (1) cover the forest area, (2) sample a single habitat defined by the dominance of the European spruce, (3) avoid pseudo-replication between neighboring recorders, and (4) avoid anthropogenic noise due to car traffic and local activities. The subsample contained 1% of all recordings and was built to ensure a balanced dataset that is representative of (1) the four recording sites, (2) the 12 months of the year, (3) the seven week days and (4) the 24 h of a day-and-night cycle. Each recording was then listened to and manually classified as one of the five sound types by one of us (EG) using Audacity v2.3.2 (Audacity team 2019).</p> <p>The script to train the ANN model is available on a Github repository : <a href="https://github.com/ear-team/Grinfeder_Landscape_Ecology_2022">https://github.com/ear-team/Grinfeder_Landscape_Ecology_2022</a> </p> <p>This work was supported by the Parc naturel du Haut-Jura who received funding from the Région Bourgogne-Franche-Comté </p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_10701274 |
| institution | Zenodo |
| language | eng |
| publishDate | 2024 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Dataset of the paper "Soundscape dynamics of a cold protected forest: dominance of aircraft noise" Haupert, Sylvain Grinfeder, Elie Sueur, Jérôme Sèbe, Frédéric Ducrettet, Manon Barlet, Julien Reynet, Marie-Pierre ecoacoustics soundscape sounds passive acoustic monitoring ecology biophony geophony anthropophony climate automatic classification machine learning ann cold forest protected area annotations audio recordings <p>Annotated training dataset used to train an artificial neural network (ANN) with mel frequency cepstral coefficients (MFCC) to systematically detect the occurrence of silence and sounds coming from birds, mammals, insects (biophony), rain (geophony), wind (geophony), and aircraft (anthropophony).</p> <p>The training dataset contains <strong>1314 audio recordings</strong> (i.e. soundscapes) collected in a temperate cold climax forest locally named ‘Risoux’. The forest is located in the East of France in the Jura Mountains at the Swiss border and the closest city is Morez (46° 31′ 22″ N, 6° 01′ 23″ E). The forest soundscape was recorded using four automatic SongMeter 4 recorders (Wildlife Acoustics Inc, Concord, MA, USA). These recorders were installed across the Risoux Forest central zone, along a West–East axis. The distance between the recorders was 1.00 ± 0.10 km. The position of the recorders was chosen to (1) cover the forest area, (2) sample a single habitat defined by the dominance of the European spruce, (3) avoid pseudo-replication between neighboring recorders, and (4) avoid anthropogenic noise due to car traffic and local activities. The subsample contained 1% of all recordings and was built to ensure a balanced dataset that is representative of (1) the four recording sites, (2) the 12 months of the year, (3) the seven week days and (4) the 24 h of a day-and-night cycle. Each recording was then listened to and manually classified as one of the five sound types by one of us (EG) using Audacity v2.3.2 (Audacity team 2019).</p> <p>The script to train the ANN model is available on a Github repository : <a href="https://github.com/ear-team/Grinfeder_Landscape_Ecology_2022">https://github.com/ear-team/Grinfeder_Landscape_Ecology_2022</a> </p> <p>This work was supported by the Parc naturel du Haut-Jura who received funding from the Région Bourgogne-Franche-Comté </p> |
| title | Dataset of the paper "Soundscape dynamics of a cold protected forest: dominance of aircraft noise" |
| topic | ecoacoustics soundscape sounds passive acoustic monitoring ecology biophony geophony anthropophony climate automatic classification machine learning ann cold forest protected area annotations audio recordings |
| url | https://doi.org/10.5281/zenodo.10701274 |