SM4在被動聲學監測中新熱帶無尾目鳥類識別基準數據集中的應用
Abstract
Global change is predicted to induce shifts in anuran acoustic behavior, which can be studied through passive acoustic monitoring (PAM). Understanding changes in calling behavior requires automatic identification of anuran species, which is challenging due to the particular characteristics of neotropical soundscapes. In this paper, we introduce a large-scale multi-species dataset of anuran amphibians calls recorded by PAM, that comprises 27hours of expert annotations for 42 different species from two Brazilian biomes. We provide open access to the dataset, including the raw recordings, experimental setup code, and a benchmark with a baseline model of the fine-grained categorization problem. Additionally, we highlight the challenges of the dataset to encourage machine learning researchers to solve the problem of anuran call identification towards conservation policy. All our experiments and resources have been made available at https://soundclim.github.io/anuraweb/.
摘要：
全球變化預計會引起無尾聲波行為的變化，這可以通過被動聲學監測（PAM）來研究。了解呼喚行為的變化需要自動識別無尾目動物物種，由于新熱帶音景的特殊特征，這是一個挑戰。本文介紹了PAM記錄的無尾兩棲動物叫聲的大規模多物種數據集，其中包括來自巴西兩個生物群落的42個不同物種的27小時專家注釋。我們提供對數據集的開放訪問，包括原始記錄、實驗設置代碼和具有細粒度分類問題基線模型的基準。此外，我們強調了數據集的挑戰，以鼓勵機器學習研究人員解決針對保護政策的anuran呼叫識別問題。我們所有的實驗和資源都在https://soundclim.github.io/anuraweb/上面.
關鍵詞：SM4聲音記錄器，鳥鳴叫監測，Wildlife Acoustics，野外動物聲音監測，鳥類監測
