標題：Wildlife Kaleidoscope Pro Analysis軟件論文：基于深度卷積神經網絡的區域珍稀鳥類聲學監測
Abstract
Bioacoustic monitoring with machine learning (ML) models can provide valuable insights for informed decisionmaking in conservation efforts. In this study, the team built deep convolutional neural networks to analyze field recordings and classify calls of Yellow-vented warbler (Phylloscopus cantator) and Rufous-throated wren-babbler (Spelaeornis caudatus), both of which are regionally rare in Nepal. Data augmentation techniques for calls of the two bird species were utilized to effectively increase the size of the training set and thus boost model performance. Nepali ornithologists were engaged in iterative data labeling from field recordings, leveraging ML technology in conjunction with expert manual labeling and verification. The model output provides insights of species activity and abundance throughout 2018–2019 in multiple ecosystems along an elevational transect in the Barun River Valley, Nepal. The results of this study may help conservationists better understand species distribution, behavior, diversity, and habitat preference. Additionally, the results provide baseline data to quantify future changes due to habitat disruption or climate change. This modeling methodology and its framework can be easily adopted by other acoustic classification problems.
摘要：
使用機器學習（ML）模型進行生物聲學監測可以為保護工作中的明智決策提供有價值的見解。在這項研究中，研究小組建立了深度卷積神經網絡來分析野外記錄，并對黃喉鶯（Phylloscopus cantator）和紅喉鷦鷯鶯（Spelaeornis caudatus）的叫聲進行分類，這兩種鶯在尼泊爾地區都很罕見。利用兩種鳥類叫聲的數據增強技術有效地增加了訓練集的大小，從而提高了模型的性能。尼泊爾鳥類學家利用機器學習技術結合專家手動標記和驗證，從現場記錄中進行迭代數據標記。該模型輸出提供了尼泊爾巴倫河谷海拔樣帶沿線多個生態系統2018-2019年物種活動和豐度的見解。這項研究的結果可能有助于保護主義者更好地了解物種分布、行為、多樣性和棲息地偏好。此外，這些結果提供了基線數據，以量化由于棲息地破壞或氣候變化而導致的未來變化。這種建模方法及其框架可以很容易地被其他聲學分類問題采用。
關鍵詞：Kaleidoscope Pro Analysis software，Wildlife Acoustics，聲學追蹤監測，野生動物聲學監測，聲學分析軟件，鳥鳴監測
