SM2+：自然環境超長時間錄音的分析與可視化
Abstract
Advances in technology and reduction in data storage costs enable the autonomous collection of large quantities of continuous audio recordings. While the collection of very long environmental recordings has become easier, the analysis of these recordings remains challenging. A very-long-duration audio recording is defined as one with a minimum length of one day, but may have durations of weeks, months, or years. This thesis provides methods for data reduction and visualisation that enable the ecological interpretation and navigation of very-long-duration audio recordings.
The major theme of data reduction commenced after the establishment of protocols and the collection of two thirteen-month continuous audio recordings from two separate Southeast Queensland forest ecosystems. The acoustic indices calculated on one-minute audio segments were used to develop two new techniques to visualise the contents of very long-duration recordings. An acoustic index is a mathematical expression used to measure a particular aspect of the energy distribution in audio recordings. Microphone failure in one channel was noticed shortly after the recording commenced. A method was established to detect microphone problems in long recordings.
A novel error measure was developed to detect seasonal and site differences and enable optimisation of the clustering based on seasonal and site differences in the data. Cluster interpretation on very-long-duration audio recordings is problematic because listening to large amounts of audio is time-consuming and therefore impractical. To overcome this, a series of five methods were developed to build on the interpretations made through listening. These methods enabled the allocation of an acoustic label to each cluster, resulting in a labelled acoustic sequence. This acoustic sequence was used to develop three additional visualisation techniques.
The culmination of the methods developed in this thesis was the six case studies. These extended the ecological interpretation of the acoustic sequence beyond those that were made through the visualisations. The case studies demonstrated that clustering can facilitate ecological interpretation of very-long-duration audio recordings.
摘要：
技術的進步和數據存儲成本的降低使得能夠自主收集大量連續的音頻記錄。雖然收集非常長的環境記錄變得更加容易，但對這些記錄的分析仍然具有挑戰性。非常長持續時間的錄音被定義為最小長度為一天的錄音，但可能持續數周、數月或數年。本文提供了數據簡化和可視化的方法，使長時間音頻記錄的生態解釋和導航成為可能。
在制定協議并從昆士蘭州東南部兩個獨立的森林生態系統收集了兩份為期13個月的連續錄音后，數據減少的主要主題開始了。基于一分鐘音頻片段計算的聲學指數被用于開發兩種新技術，以可視化長時間錄音的內容。聲學指數是一種數學表達式，用于測量錄音中能量分布的特定方面。錄音開始后不久，發現一個通道的麥克風出現故障。建立了一種檢測長錄音中麥克風問題的方法。
開發了一種新的誤差度量來檢測季節和地點差異，并能夠根據數據中的季節和地點的差異優化聚類。對持續時間很長的錄音進行集群解釋是有問題的，因為聽大量的音頻很耗時，因此不切實際。為了克服這一點，開發了一系列五種方法，以通過聽力進行的解釋為基礎。這些方法能夠為每個簇分配一個聲學標簽，從而產生一個標記的聲學序列。該聲學序列用于開發三種額外的可視化技術。
本文開發的方法的高潮是六個案例研究。這些將聲學序列的生態學解釋擴展到了通過可視化所做的解釋之外。案例研究表明，聚類可以促進對長時間音頻記錄的生態解釋。
關鍵詞：聲學指標、人類聲學、生物聲學、生物生物學、Diel圖；點陣圖、生態聲學、生態監測、檢波器、長時間假彩色光譜圖、主成分分析、聲景生態學、超長時間錄音、可視化。
