Dataset from Danum Valley Conservation Area, Sabah, Malaysia for BirdNET transfer learning exercise

Dataset summary This dataset contains two folders. The first folder 'Trainingdata' contains short audio clips (.wav) of sounds from four categories: 'gibbon.female', 'hornbill.helmeted', 'hornbill.rhino', and 'noise'. Each folder contains 10 individual clips. The second folder 'Testdata' contains one 2-hour sound file and a corresponding Raven selection containing manual annotations of 'gibbon.female', 'hornbill.helmeted' and 'hornbill.rhino'. Passive acoustic monitoring data collection We collected data using first generation Swift autonomous recording units (ARUs) with a microphone sensitivity of −44 (+/−3) dB re 1 V/Pa. We collected acoustic data from one primary conservation area in Sabah, Malaysia: Danum Valley Conservation Area (using 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 generally considered 'aseasonal' due to its lack of clearly differentiated wet and dry seasons. The ARUs recorded at a sampling rate of 16 kHz, and 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. Acoustic data preparation 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 converted 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. We considered cases where all time windows between 6 - 20 seconds exceeded the threshold as 'sound events', and 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: helmeted hornbills (Rhinoplax vigil), rhinoceros hornbills (Buceros rhinoceros), female gibbons (Hylobates funereus) and a catch-all “noise” category. Data Usage If you use this dataset, please also cite this paper: Clink, D.J., Kier, I., Ahmad, A.H. and Klinck, H., 2023. A workflow for the automated detection and classification of female gibbon calls from long-term acoustic recordings. Frontiers in Ecology and Evolution, 11, p.1071640. https://www.frontiersin.org/articles/10.3389/fevo.2023.1071640/full