کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
4374751 1617199 2016 6 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
First automatic passive acoustic tool for monitoring two species of procellarides (Pterodroma baraui and Puffinus bailloni) on Reunion Island, Indian Ocean
ترجمه فارسی عنوان
اولین ابزار صوتی غیرفعال اتوماتیک برای نظارت بر دو گونه procellarides (Pterodroma baraui و bailloni Puffinus) در جزیره ریونیون، اقیانوس هند
کلمات کلیدی
پرندگان دریایی؛ آشکارساز خودکار؛ نظارت صوتی؛ Procellarides؛ واحد ضبط مستقل؛ مرغ باران باراو
موضوعات مرتبط
علوم زیستی و بیوفناوری علوم کشاورزی و بیولوژیک بوم شناسی، تکامل، رفتار و سامانه شناسی
چکیده انگلیسی


• Cheap acoustic autonomous recording units were placed in Barau's petrel (Pterodroma baraui) and tropical shearwater (Puffinus bailloni) breeding colonies.
• Two acoustic automatic detectors of Barau's petrel and tropical shearwater vocalisations were modelled.
• Best models were able to discriminate each target species calls from other sounds of its colony with F1 scores of 88% (Barau's petrel, 1015 samples) and 85% (tropical shearwater, 1217 samples).
• Automatic acoustic monitoring of these two species of seabirds based on acoustic data collected by autonomous recording devices in harsh, windy and wet environments is feasible.

Here are proposed two automatic detectors of Barau's petrel (Pterodroma baraui) and tropical shearwater (Puffinus bailloni) vocalisations in noisy audio recordings (1) trained with a low number of positive training instances, and (2) whose performances would be the highest possible. To do so, acoustic recordings were performed in one Barau's petrel colony between February and May 2014 (85 h) and in two tropical shearwater colonies in March and April (21 h). Manual and automatic methods of segmentation were combined. Manual segmentation allowed (1) to miss a very few number of positive segments and (2) to avoid introducing false positive instances. Automatic segmentation provided quickly a diversified set of negative instances. Manual labelling must be regarded as an investment, for current and future works. A random forest classifier and classical methods of acoustic signal characterisation (cepstral coefficients, spectral moments, etc.) were tested. Best models were able to discriminate each target species calls from other sounds of its colony with F1 scores of 88% (Barau's petrel, 1015 samples) and 85% (tropical shearwater, 1217 samples). The acoustic monitoring of nocturnal burrow-nesting seabirds based on (1) data collected by autonomous recording units in harsh, windy and wet environments and (2) automatic analysis tools is feasible. The size of our database was limited. Consequently further works will be necessary to study robustness of models on long time-series data.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Ecological Informatics - Volume 35, September 2016, Pages 55–60
نویسندگان
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