SM2BAT+在聲學調查中的自動化識別誤差中的應用
Abstract
Assessing the state and trend of biodiversity in the face of anthropogenic threats requires large‐scale and long‐time monitoring, for which new recording methods offer interesting possibilities. Reduced costs and a huge increase in storage capac ity of acoustic recorders have resulted in an exponential use of passive acoustic monitoring (PAM) on a wide range of animal groups in recent years. PAM has led to a rapid growth in the quantity of acoustic data, making manual identification increasingly time‐consuming. Therefore, software detecting sound events, ex tracting numerous features and automatically identifying species have been de veloped. However, automated identification generates identification errors, which could influence analyses which look at the ecological response of species. Taking the case of bats for which PAM constitutes an efficient tool, we propose a cau tious method to account for errors in acoustic identifications of any taxa without excessive manual checking of recordings.
摘要：
面對人為威脅，評估生物多樣性的狀態和趨勢需要大規模和長期的監測，新的記錄方法為此提供了有趣的可能性。近年來，隨著成本的降低和錄音機存儲容量的大幅增加，被動聲學監測（PAM）在各種動物群體中的應用呈指數級增長。PAM導致聲學數據量快速增長，使得人工識別越來越耗時。因此，開發了檢測聲音事件、提取大量特征和自動識別物種的軟件。然而，自動識別會產生識別錯誤，這可能會影響對物種生態反應的分析。以PAM作為有效工具的蝙蝠為例，我們提出了一種謹慎的方法來解釋任何分類群的聲學識別錯誤，而無需對記錄進行過多的人工檢查。
關鍵詞：SM2BAT+，生物聲學、被動聲學監測、半自動識別、調查方法
