SM2BAT+在聲學(xué)調(diào)查中的自動化識別誤差中的應(yīng)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(tài)和趨勢需要大規(guī)模和長期的監(jiān)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(jī)存儲容量的大幅增加，被動聲學(xué)監(jiān)測（PAM）在各種動物群體中的應(yīng)用呈指數(shù)級增長。PAM導(dǎo)致聲學(xué)數(shù)據(jù)量快速增長，使得人工識別越來越耗時。因此，開發(fā)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(chǎn)生識別錯誤，這可能會影響對物種生態(tài)反應(yīng)的分析。以PAM作為有效工具的蝙蝠為例，我們提出了一種謹(jǐn)慎的方法來解釋任何分類群的聲學(xué)識別錯誤，而無需對記錄進(jìn)行過多的人工檢查。
關(guān)鍵詞：SM2BAT+，生物聲學(xué)、被動聲學(xué)監(jiān)測、半自動識別、調(diào)查方法
