Song Meter Mini在基于未標記數據的南非鳥類自動生物聲學監測中的應用
Abstract
Analyses for biodiversity monitoring based on passive acoustic monitoring (PAM) recordings is time-consuming and chal lenged by the presence of background noise in recordings. Existing models for sound event detection (SED) worked only on certain avian species and the development of further models required labeled data. The developed framework automatically extracted labeled data from available platforms for selected avian species. The labeled data were embedded into recordings, including environmental sounds and noise, and were used to train convolutional recurrent neural network (CRNN) models. The models were evaluated on unprocessed real world data recorded in urban KwaZulu-Natal habitats. The Adapted SED-CRNN model reached a F1 score of 0.73, demonstrating its efficiency under noisy, real-world conditions. The proposed approach to automatically extract labeled data for chosen avian species enables an easy adaption of PAM to other species and habitats for future conservation projects.
摘要：
基于被動聲學監測（PAM）記錄的生物多樣性監測分析是耗時的，并且受到記錄中存在背景噪聲的挑戰。現有的聲音事件檢測（SED）模型僅適用于某些鳥類，進一步模型的開發需要標記數據。開發的框架自動從選定鳥類的可用平臺中提取標記數據。標記的數據被嵌入到錄音中，包括環境聲音和噪聲，并用于訓練卷積遞歸神經網絡（CRNN）模型。這些模型是在夸祖魯-納塔爾省城市棲息地記錄的未經處理的現實世界數據上進行評估的。Adapted SED-CRNN模型達到了0.73的F1分數，證明了它在嘈雜的現實世界條件下的效率。所提出的自動提取選定鳥類物種標記數據的方法使PAM能夠輕松適應其他物種和棲息地，以用于未來的保護項目。
關鍵詞：Song Meter，鳥鳴記錄，野生動物聲學監測，鳥類聲學記錄，鳥類被動式聲學監
