SM2+：自然环境超长时间录音的分析与可视化
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：
技术的进步和数据存储成本的降低使得能够自主收集大量连续的音频记录。虽然收集非常长的环境记录变得更加容易，但对这些记录的分析仍然具有挑战性。非常长持续时间的录音被定义为最小长度为一天的录音，但可能持续数周、数月或数年。本文提供了数据简化和可视化的方法，使长时间音频记录的生态解释和导航成为可能。
在制定协议并从昆士兰州东南部两个独立的森林生态系统收集了两份为期13个月的连续录音后，数据减少的主要主题开始了。基于一分钟音频片段计算的声学指数被用于开发两种新技术，以可视化长时间录音的内容。声学指数是一种数学表达式，用于测量录音中能量分布的特定方面。录音开始后不久，发现一个通道的麦克风出现故障。建立了一种检测长录音中麦克风问题的方法。
开发了一种新的误差度量来检测季节和地点差异，并能够根据数据中的季节和地点的差异优化聚类。对持续时间很长的录音进行集群解释是有问题的，因为听大量的音频很耗时，因此不切实际。为了克服这一点，开发了一系列五种方法，以通过听力进行的解释为基础。这些方法能够为每个簇分配一个声学标签，从而产生一个标记的声学序列。该声学序列用于开发三种额外的可视化技术。
本文开发的方法的高潮是六个案例研究。这些将声学序列的生态学解释扩展到了通过可视化所做的解释之外。案例研究表明，聚类可以促进对长时间音频记录的生态解释。
关键词：声学指标、人类声学、生物声学、生物生物学、Diel图；点阵图、生态声学、生态监测、检波器、长时间假彩色光谱图、主成分分析、声景生态学、超长时间录音、可视化。
