A labelled dataset of the loud calls of four vertebrates collected using passive acoustic monitoring in Malaysian Borneo

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.