A labelled dataset of the loud calls of four vertebrates collected using passive acoustic monitoring in Malaysian Borneo
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Passive acoustic monitoring data collection We collected data using first generation Swift autonomous recording units (ARUs) (Koch et al. 2016) with a microphone sensitivity of −44 (+/−3) dB re 1 V/Pa. The microphone frequency response was not measured but is assumed to be flat (+/− 2 dB) in the frequency range 100 Hz to 7.5 kHz. The analog signal was amplified by 40 dB and digitized (16-bit resolution) using an analog-to-digital converter (ADC) with a clipping level of −/+ 0.9 V. We collected acoustic data from one primary conservation area in Sabah, Malaysia: Danum Valley Conservation Area (with 11 recording units from March to July 2018). Danum Valley covers an area of roughly 440 km², and is characterized by lowland dipterocarp forest. Unlike many tropical forest regions, this area is considered 'aseasonal' due to its lack of clearly differentiated wet and dry seasons (Walsh and Newbery 1999). In Danum Valley, the ARUs recorded at a sampling rate of 16 kHz. All recordings were saved in waveform audio (.wav) format, with files of 2-hr duration. We affixed each recording unit to trees approximately 2-m above the ground and recorded continuously over 24 hours. We set the units on a 750 m grid structure, and preliminary field tests indicate that with these recording settings the detection range of gibbon vocalizations is ~ 400 m. Acoustic data processing We randomly chose approximately 500 h of recordings from Danum Valley Conservation Area to use to create a training dataset. We used a band-limited energy detector (BLED) to identify potential sounds of interest in the gibbon frequency range. For the BLED detector, we convert the 2-hr recordings into a spectrogram using a 1,600-point (100 ms) Hamming window (3 dB bandwidth = 13 Hz) with 0% overlap and a 2,048-point DFT, with the "seewave" package (Sueur et al. 2008). We then filtered the spectrogram to focus on the desired frequency range, specifically 0.5–1.6 kHz for Northern grey gibbons. For each unique time window in the recording, we determined the total energy across frequency bins which gave a single value for every 100 ms interval. Utilizing the "quantile" function in base R, we established the threshold to delineate signal from noise. Preliminary tests with varied quantile values revealed that the 15th quantile led to optimized recall for our target signal. This approach resulted in 1,439 unique sound events. The sound events were then annotated by a single observer (DJC) using a custom-written function in R to visualize the spectrograms into the following categories: great argus pheasant (Argusianus argus) long and short calls (Clink et al. 2021), helmeted hornbills (Rhinoplax vigil), rhinoceros hornbills (Buceros rhinoceros), female gibbons (Hylobates funereus) and a catch-all “noise” category. References Clink, D. J., Groves, T., Ahmad, A. H., & Klinck, H. (2021). Not by the light of the moon: Investigating circadian rhythms and environmental predictors of calling in Bornean great argus. PloS one, 16(2), e0246564. Koch, R., Raymond, M., Wrege, P., & Klinck, H. (2016). SWIFT: A small, low-cost acoustic recorder for terrestrial wildlife monitoring applications. In North American Ornithological Conference (p. 619). Washington, D.C. Sueur, J., Aubin, T., & Simonis, C. (2008). Seewave: a free modular tool for sound analysis and synthesis. Bioacoustics, 18, 213–226. Walsh, R. P., & Newbery, D. M. (1999). The ecoclimatology of Danum, Sabah, in the context of the world’s rainforest regions, with particular reference to dry periods and their impact. Philosophical transactions of the Royal Society of London. Series B, Biological sciences, 354(1391), 1869–83. https://doi.org/10.1098/rstb.1999.0528 Webb, C. O., & Ali, S. (2002). Plants and vegetation of the Maliau Basin Conservation Area, Sabah, East Malaysia. Final Report to Maliau Basin Management Committee.
被动声学监测数据集采集 本研究采用第一代Swift自主记录单元（autonomous recording units, ARUs）开展数据采集（Koch等，2016），所用麦克风的灵敏度为−44（±3）dB（参照1 V/Pa）。麦克风的频率响应未进行实测，但假设其在100 Hz至7.5 kHz的频段内具有平坦响应（±2 dB）。模拟信号经40 dB放大后，通过模数转换器（analog-to-digital converter, ADC）完成数字化，采样分辨率为16位，该转换器的削波电平为±0.9 V。 本次声学数据采集于马来西亚沙巴州的核心保护区——丹浓谷保护区（Danum Valley Conservation Area），2018年3月至7月间共部署11台记录单元。丹浓谷总面积约440 km²，以低地龙脑香科森林为典型植被特征。与多数热带林区不同，该区域因缺乏明确的干湿季划分，被归类为"无季节型"生态系统（Walsh与Newbery，1999）。 丹浓谷内的ARUs以16 kHz的采样率进行录音，所有录音均保存为波形音频（waveform audio, .wav）格式，单文件时长为2小时。每台记录单元被固定于距地面约2米的树干上，实现24小时连续录音。记录单元按750米的网格布局布设，初步野外测试表明，在本次录音参数设置下，长臂猿鸣唱的探测范围约为400米。 声学数据处理 本研究从丹浓谷保护区的录音数据中随机选取约500小时的音频，用于构建训练数据集。采用带宽限能量检测器（band-limited energy detector, BLED）识别北灰长臂猿鸣唱频段内的潜在目标声音。 针对BLED检测器，本研究使用R语言的seewave包（Sueur等，2008），将2小时时长的录音转换为语谱图：采用1600点（对应100 ms）的汉明窗（3 dB带宽=13 Hz），无重叠，并采用2048点离散傅里叶变换（DFT）。随后对语谱图进行滤波，聚焦于目标物种的频段——北灰长臂猿（Northern grey gibbons）的0.5–1.6 kHz频段。 对于录音中的每个独立时间窗口，本研究计算其跨频率bin的总能量，得到每100 ms间隔的单一能量值。利用R基础函数中的quantile函数，设定阈值以区分信号与噪声。通过对不同分位数阈值的预实验，发现选取15分位数时可实现目标信号的最优召回率。该流程共识别出1439个独立声音事件。 随后由单一观察者（DJC）使用R语言自定义函数，通过语谱图可视化将这些声音事件标注为以下类别：大眼斑雉（great argus pheasant, *Argusianus argus*）的长短鸣叫声（Clink等，2021）、盔犀鸟（helmeted hornbills, *Rhinoplax vigil*）、双角犀鸟（rhinoceros hornbills, *Buceros rhinoceros*）、雌性北灰长臂猿（female gibbons, *Hylobates funereus*），以及通用的"噪声"类别。 参考文献 1. Clink, D. J., Groves, T., Ahmad, A. H., & Klinck, H. (2021). 并非月光之下：婆罗洲大眼斑雉鸣唱的昼夜节律与环境预测因子研究. *PloS one*, 16(2), e0246564. 2. Koch, R., Raymond, M., Wrege, P., & Klinck, H. (2016). SWIFT：一款用于陆生野生动物监测的小型低成本声学记录仪. 见：北美鸟类学会议（第619页）. 华盛顿特区. 3. Sueur, J., Aubin, T., & Simonis, C. (2008). Seewave：一款用于声音分析与合成的免费模块化工具. *Bioacoustics*, 18, 213–226. 4. Walsh, R. P., & Newbery, D. M. (1999). 沙巴州丹浓谷的生态气候学：以全球热带雨林区域为背景，特别关注干旱期及其影响. *Philosophical transactions of the Royal Society of London. Series B, Biological sciences*, 354(1391), 1869–83. https://doi.org/10.1098/rstb.1999.0528 5. Webb, C. O., & Ali, S. (2002). 东马来西亚沙巴州马里乌盆地保护区的植物与植被. 提交给马里乌盆地管理委员会的最终报告.
