SM2+在手動和自動監測鬃毛狼聲音中的應用
Abstract
Although bioacoustics is increasingly used to study species and environments for their monitoring and conservation, detecting calls produced by species of interest is prohibitively time consuming when done manually. Here we compared four methods for detecting and identifying roar-barks of maned wolves (Chrysocyon brachyurus) within long sound recordings: (1) a manual method, (2) an automated detector method using Raven Pro 1.4, (3) an automated detector method using XBAT and (4) a mixed method using XBAT’sdetector followed by manual verification. Recordings were done using a song meter installed at the Serra da Canastra National Park (Minas Gerais, Brazil). For each method we evaluated the following variables in a 24-h recording: (1) total time required analysing files, (2) number of false positives identified and (3) number of true positives identified compared to total number of target sounds. Automated methods required less time to analyse the recordings (77–93min) when compared to manual method (189min), but consistently presented more false positives and were less efficient in identifying true positives (manual ? 91.89%, Raven ? 32.43% and XBAT ? 84.86%). Adding a manual verification after XBAT detection dramatically increased efficiency in identifying target sounds (XBAT t manual ? 100% true positives). Manual verification of XBAT detections seems to be the best way out of the proposed methods to collect target sound data for studies where large amounts of audio data need to be analysed in a reasonable time (111min, 58.73% of the time required to find calls manually).
摘要：
盡管生物聲學越來越多地用于研究物種和環境以進行監測和保護，但手動檢測感興趣物種發出的叫聲非常耗時。在這里，我們比較了四種在長錄音中檢測和識別鬃毛狼（Chrysocyon brachyurus）吼叫的方法：（1）手動方法，（2）使用Raven Pro 1.4的自動檢測器方法，（3）使用XBAT的自動檢測器法，以及（4）使用XBAT's檢測器然后手動驗證的混合方法。錄音是使用安裝在塞拉達卡納斯特拉國家公園（巴西米納斯吉拉斯州）的歌曲計完成的。對于每種方法，我們在24小時的記錄中評估了以下變量：（1）分析文件所需的總時間，（2）識別的假陽性數量，以及（3）與目標聲音總數相比識別的真陽性數量。與手動方法（189分鐘）相比，自動方法需要更少的時間來分析記錄（77-93分鐘），但始終出現更多的假陽性，在識別真陽性方面效率較低（手動?91.89%，Raven?32.43%和XBAT?84.86%）。在XBAT檢測后添加手動驗證大大提高了識別目標聲音的效率（XBAT手動?100%真陽性）。對于需要在合理時間內分析大量音頻數據的研究，手動驗證XBAT檢測似乎是收集目標聲音數據的最佳方法（111分鐘，占手動查找呼叫所需時間的58.73%）。
關鍵詞：SM2+，Wildlife Acoustics，野生動物聲學監測，動物聲學記錄，自動聲學監測
