Characterising soundscapes across diverse ecosystems using a universal acoustic feature set

Natural habitats are being impacted by human pressures at an alarming rate. Monitoring these ecosystem-level changes often requires labor-intensive surveys that are unable to detect rapid or unanticipated environmental changes. Here we have developed a generalizable, data-driven solution to this challenge using eco-acoustic data. We exploited a convolutional neural network to embed soundscapes from a variety of ecosystems into a common acoustic space. In both supervised and unsupervised modes, this allowed us to accurately quantify variation in habitat quality across space and in biodiversity through time. On the scale of seconds, we learned a typical soundscape model that allowed automatic identification of anomalous sounds in playback experiments, providing a potential route for real-time automated detection of irregular environmental behavior including illegal logging and hunting. Our highly generalizable approach, and the common set of features, will enable scientists to unlock previously hidden insights from acoustic data and offers promise as a backbone technology for global collaborative autonomous ecosystem monitoring efforts

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

Labeled passive acoustic monitoring dataset from Danum Valley Conservation Area, Sabah, Malaysia

This is the data archive for labeled training, validation and test data from a passive acoustic monitoring project in Danum Valley Conservation Area, Sabah, Malaysia. Please refer to Clink et al. (2023) and our GitHub Page for details and code. "AnnotatedFilesTest" "AnnotatedFilesValidation" "TestSoundFiles" "TrainingFilesValidated" "TrainingFilesValidatedAddFemales" "TrueFalsePositives" "UpdatedDanumDetectionsHQ99" "ValidationSoundFiles"This is the associated data for the following publications: Clink, D. J., Kier, I.A.*, Ahmad, A.H. & H. Klinck. (2023). A workflow for the automated detection and classification of female gibbon calls from long-term acoustic recordings. Frontiers in Ecology and Evolution. 11:1071640. doi: 10.3389/fevo.2023.107164

Dataset of gibbon individual calls broadcast and re-recorded at varying distances using autonomous recording units on Malaysian Borneo.

This is the data supporting the article: ‘Mel-frequency cepstral coefficients outperform embeddings from pre-trained convolutional neural networks under noisy conditions for discrimination tasks of individual gibbons’ (Lakdari et al, under review). Github repository here: https://github.com/DenaJGibbon/Gibbon-feature-comparison. Data collection: We recorded individual female gibbon calls from unhabituated gibbons at four locations in Sabah, Malaysia, spanning various periods from 2013 to 2016. We chose a subset of 60 calls from 12 females from this database for our playback experiments based on specific criteria, including the presence of five high signal-to-noise ratio calls within the gibbon's frequency range. Compiled and downsampled the selected calls into a single ~15 minute sound file using Audacity. Executed the playback experiment in the Maliau Basin Conservation Area, Sabah, Malaysia, across two days in August 2019. We positioned nine Swift autonomous acoustic recorders at 50-meter intervals (up to 400 meters) at a height of approximately 1.5 meters. Recorded at a sampling rate of 48 kHz and a gain of 40 dB, saving files as one-hour Waveform Audio Files (.wav). Conducted playbacks from a single location in the canopy walk at a height of approximately 20 meters, using a FoxPro Inferno Predator speaker calibrated to ~ 100 dB re 20 μPa at 10 meters, as estimated with a Larson-Davis (SLM LxT CLASS-1 377B02 FF-MIC) sound level meter. Data structure: There are seven folders, and each folder contains individual .wav files. The clips are labelled with the following structure, using "M1_20190823_060003_DK_02_021.01 _snr17.58.wav" as an example: Recorder Location: "M1" Date: "20190823" (August 23, 2019) Time: "060003" (06:00:03) Individual Gibbon: "DK_02" (Identifier for the specific gibbon) Call ID: "021.01" (Identifier for the individual call) SNR (Signal-to-Noise Ratio): "17.58" (Measurement of the signal's strength relative to background noise