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openother-openOct 2020View details →

Utility of acoustic indices for ecological monitoring in complex sonic environments

<p>Abstract</p> <p>With the continued adoption of passive acoustic monitoring as a tool for rapid and high-resolution ecosystem monitoring, ecologists are increasingly making use of a suite of acoustic indices to summarise the sonic environment. Though these indices are often reported to well represent some aspect of the biology of an ecosystem, the degree to which they are confounded by various extraneous sonic conditions is largely unknown. We conducted an aural inventory across 23 field sites in Okinawa to identify the number of unique animal sounds present in recordings. Using these values of ‘measured richness’, we then examined how the performance of 11 commonly-used acoustic indices varied across a range of sonic conditions (including in the presence and absence of insect stridulations, audible wind or rain, and human-related sounds). Our analysis identified both well- and poor-performing acoustic indices, as well as those that were particularly sensitive to sonic conditions. Only two indices reflected measured richness across the full range of sonic conditions examined. A few indices were relatively insensitive to extraneous sonic conditions, but no index correlated with measured richness when masked by sound from broadband stridulating insects. Our results demonstrate considerable sensitivity of most commonly used acoustic indices to confounding sonic conditions, highlighting the challenges of working with large acoustic datasets collected in the field. We make practical recommendations for acoustic index use based on study design, with the aim of identifying the suite of acoustic indices with greatest utility as indicators for rapid biodiversity monitoring and management of the world’s natural soundscapes.</p> <p>Methods</p> <p>The dataset contains the names of audio files collected across 23 field sites between April 2017 and January 2018 as part of the OKEON-Churamori project on the island of Okinawa, Japan. We conducted an aural inventory, manually counting and recording the number of unique biotic sounds (approximately corresponding to species richness) and noting the presence or absence of three potentially confounding sonic conditions: audible geophony (wind, rain etc.), anthropophony (human-related sounds), and broadband sounds produced by stridulating insect (e.g. cicadas, orthopterans). We then calculated 11 commonly used acoustic indices from the literature and compared their performance (correlation with richness) in the presence vs absence of each sonic condition. Our dataset also contains time and date information for each recording, and the mean site-level richness (i.e. across multiple recordings) for each site and for each unique site-by-season combination. See Table A2 and Methods section in the associated manuscript for details on data processing and the calculation of acoustic indices.</p> <p>Usage notes</p> <p>See readme file for descriptions of data table structure.</p>

opencc-zeroOct 2020View details →

Unsupervised acoustic classification of individual gibbon females and the implications for passive acoustic monitoring

<p>1. Passive acoustic monitoring (PAM) has the potential to greatly improve our ability to monitor cryptic yet vocal animals. Advances in automated signal detection have increased the scope of PAM, but distinguishing between individuals— which is necessary for density estimation— remains a major challenge. When individual identity is known, supervised classification techniques can be used to distinguish between individuals. Supervised methods require labeled training data, whereas unsupervised techniques do not. If the acoustic signals of individuals are sufficiently different, the number of clusters might represent the number of individuals sampled. The majority of applications of unsupervised techniques in animal vocalizations have focused on quantifying species-specific call repertoires. However, with increased interest in PAM applications, unsupervised methods that can distinguish between individuals are needed. <br> 2. Here, we use an existing dataset of Bornean gibbon female calls with known identity from five sites on Malaysian Borneo to test the ability of three different unsupervised clustering algorithms (affinity propagation, K-medoids, and Gaussian mixture model-based clustering) to distinguish between individuals. Calls from different gibbon females are readily distinguishable using supervised techniques. For internal validation of unsupervised cluster solutions, we calculated silhouette coefficients. For external validation, we compared clustering results with female identity labels using a standard metric: normalized mutual information. We also calculated classification accuracy by assigning unsupervised cluster solutions to females based on which cluster had the highest number of calls from a particular female.<br> 3. We found that affinity propagation clustering consistently outperformed the other algorithms for all metrics used. In particular, classification accuracy of affinity propagation clustering was more consistent as the number of females increased, and when we randomly sampled females across sites. <br> 4. We conclude that unsupervised techniques may be useful for providing additional information regarding individual identity for PAM applications. We stress that although we use gibbons as a case study, these methods will be applicable for any individually-distinct vocal animal. <br> </p>

opencc-zeroDec 2016View details →

Data from: Acoustic monitoring of coastal dolphins and their response to naval mine neutralization exercises

To investigate the potential impacts of naval mine neutralization exercises (MINEX) on odontocete cetaceans, a long-term passive acoustic monitoring study was conducted at a US Navy training range near Virginia Beach, USA. Bottom-moored acoustic recorders were deployed in 2012–2016 near the epicentre of MINEX training activity and were refurbished every 2–4 months. Recordings were analysed for the daily presence/absence of dolphins, and dolphin acoustic activity was quantified in detail for the hours and days before and after 31 MINEX training events. Dolphins occurred in the area year-round, but there was clear seasonal variability, with lower presence during winter months. Dolphins exhibited a behavioural response to underwater detonations. Dolphin acoustic activity near the training location was lower during the hours and days following detonations, suggesting that animals left the area and/or reduced their signalling. Concurrent acoustic monitoring farther away from the training area suggested that the radius of response was between 3 and 6 km. A generalized additive model indicated that the predictors that explained the greatest amount of deviance in the data were the day relative to the training event, the hour of the day and circumstances specific to each training event.

opencc-zeroJun 2019View details →

Data from: Persistent near real-time passive acoustic monitoring for baleen whales from a moored buoy: system description and evaluation

1. Managing interactions between human activities and marine mammals often relies on an understanding of the real-time distribution or occurrence of animals. Visual surveys typically cannot provide persistent monitoring because of expense and weather limitations, and while passive acoustic recorders can monitor continuously, the data they collect are often not accessible until the recorder is recovered. 2. We have developed a moored passive acoustic monitoring system that provides near real-time occurrence estimates for humpback, sei, fin, and North Atlantic right whales from a single site for a year, and makes those occurrence estimates available via a publicly accessible website, email and text messages, a smartphone/tablet app, and the U.S. Coast Guard's maritime domain awareness software. We evaluated this system using a buoy deployed off the coast of Massachusetts during 2015-2016 and redeployed again during 2016-2017. Near real-time estimates of whale occurrence were compared to simultaneously collected archived audio as well as whale sightings collected near the buoy by aerial surveys. 3. False detection rates for right, humpback, and sei whales were 0% and nearly 0% for fin whales, while missed detection rates at daily time scales were modest (12-42%). Missed detections were significantly associated with low calling rates for all species. We observed strong associations between right whale visual sightings and near real-time acoustic detections over a monitoring range of 30-40 km and temporal scales of 24-48 hours, suggesting that silent animals were not especially problematic for estimating occurrence of right whales in the study area. There was no association between acoustic detections and visual sightings of humpback whales. 4. The moored buoy has been used to reduce the risk of ship strikes for right whales in a U.S. Coast Guard gunnery range, and can be applied to other mitigation applications.

opencc-zeroDec 2016View details →

Data from: AudioMoth: evaluation of a smart open acoustic device for monitoring biodiversity and the environment

1. The cost, usability and power efficiency of available wildlife monitoring equipment currently inhibits full ground-level coverage of many natural systems. Developments over the last decade in technology, open science, and the sharing economy promise to bring global access to more versatile and more affordable monitoring tools, to improve coverage for conservation researchers and managers. 2. Here we describe the development and proof-of-concept of a low-cost, small-sized and low-energy acoustic detector: 'AudioMoth'. The device is open-source and programmable, with diverse applications for recording animal calls or human activity at sample rates of up to 384kHz. We briefly outline two ongoing real-world case studies of large-scale, long-term monitoring for biodiversity and exploitation of natural resources. These studies demonstrate the potential for AudioMoth to enable a substantial shift away from passive continuous recording by individual devices, towards smart detection by networks of devices flooding large and inaccessible ecosystems. 3. The case studies demonstrate one of the smart capabilities of AudioMoth, to trigger event logging on the basis of classification algorithms that identify specific acoustic events. An algorithm to trigger recordings of the New Forest cicada (Cicadetta montana) demonstrates the potential for AudioMoth to vastly improve the spatial and temporal coverage of surveys for the presence of cryptic animals. An algorithm for logging gunshot events has potential to identify a shotgun blast in tropical rainforest at distances of up to 500 m, extending to 1km with continuous recording. 4. AudioMoth is more energy efficient than currently available passive acoustic monitoring (PAM) devices, giving it considerably greater portability and longevity in the field with smaller batteries. At a build cost of ~US$43 per unit, AudioMoth has potential for varied applications in large-scale, long-term acoustic surveys. With continuing developments in smart, energy-efficient algorithms and diminishing component costs, we are approaching the milestone of local communities being able to afford to remotely monitor their own natural resources.

opencc-zeroOct 2018View details →

Data from: Pollination on the dark side: acoustic monitoring reveals impacts of a total solar eclipse on flight behavior and activity schedule of foraging bees

The total solar eclipse of 21 August 2017 traversed ~5000 km from coast to coast of North America. In its 90-min span, sunlight dropped by three orders of magnitude and temperature by 10–15°C. To investigate impacts of these changes on bee (Hymenoptera: Apoidea) pollinators, we monitored their flights acoustically in natural habitats of Pacific Coast, Rocky Mountain, and Midwest regions. Temperature changes during the eclipse had little impact on bee activity. Most of the explained variation (R2) in buzzing rate was attributable to changes in light intensity. Bees ceased flying during complete darkness at totality, but flight activity was unaffected by dim light in partial phases before and after totality. Flights of bees during partial phases of the eclipse lasted longer than flights made under full sun, showing that behavioral plasticity matched bee flight properties to changes in light intensity during the eclipse. Efforts of citizen scientists, including hundreds of school children, contributed to the scope and educational impact of this study.

opencc-zeroDec 2014View details →

Data from: Repertoire-based individual acoustic monitoring of a migratory passerine bird with complex song as an efficient tool for tracking territorial dynamics and annual return rates

In field ecological and behavioural studies, it is often necessary to identify specific individuals. In birds, colour rings are frequently used to mark individuals; however, rings are often difficult to observe, especially in small species and dense habitats. Acoustic-based monitoring detecting individuals by their characteristic vocalization is a potentially suitable alternative, but this approach is challenging in species with complex songs. On the example of the Tree Pipit (Anthus trivialis), a small migratory passerine often singing in flight or from perches obscured by foliage, we demonstrate that acoustic monitoring based on the syllable repertoire can be very efficient tool for individual recognition. During a 3-year study, we obtained over 500 recordings from males from one study population (a number of them returning after winter). Males banded with colour rings were repeatedly recorded throughout the seasons, and syllable repertoires were determined from spectrograms for each recording. The repertoire of each unambiguously identified male was distinct and stable within as well as between seasons; and males with similar syllable repertoires differed in syntax. Based on the congruence between identification based solely on spectrogram assessment, and that based on observation of colour rings, we inferred that reliable identification of singing males (including non-ringed ones) was possible in the studied population from assessing a repertoire and song syntax of <5-min recording (containing 20–30 songs). The acoustic-based data: (i) increased the overall estimated number of territorial males at the study locality (from 49 ringed to 61), and improved the estimates of the period of their presence; (ii) revealed dynamic within-season changes in territory occupancy that would otherwise be missed; and (iii) allowed identification of returning birds (including non-ringed ones and those actively avoiding approaching humans). Our results suggest that some commonly used methods may substantially underestimate return rates of migratory bird species. Individual acoustic monitoring should be applicable on various bird species with complex song and stable repertoires, and may be particularly useful for those living in dense habitat or sensitive to handling.

opencc-by-4.0Dec 2023View details →

Raw data for Evaluating community-wide temporal sampling in passive acoustic monitoring: A comprehensive study of avian vocal patterns in subtropical montane forests

<p>This dataset, utilized in the research paper "<a href="https://doi.org/10.12688/f1000research.141951.1">Evaluating community-wide temporal sampling in passive acoustic monitoring: A comprehensive study of avian vocal patterns in subtropical montane forests</a>", comprises columns such as site_name, longitude (WGS84), latitude (WGS84), altitude (meters above sea level), vegetation types, date, hour, minute, julian_day, scientific_name, and Vocal Activity Rate per minute (VAR_m). It encompasses data gathered from twelve Passive Acoustic Monitoring (PAM) stations positioned within Yushan National Park (YSNP), Taiwan. The collection period spanned from March 1 to June 30, 2021. The dataset documents 8,202,731 vocalizations from twelve bird species, detected using an automated sound identification tool named SILIC (Sound Identification and Labeling Intelligence for Creatures). The vocalization data is aggregated by site, species, and time (down to the minute).</p>

opencc-by-4.0Nov 2023View details →

[Data] Self-Supervised Bayesian Representation Learning of Acoustic Emissions from Laser Powder Bed Fusion Process for In-situ Monitoring

<div> <div> <div> <p>Different Laser Powder Bed Fusion (LPBF) process spaces were deliberately introduced by employing two distinct 316L stainless steel powder distributions (with particle sizes >45 μm and < 45 μm) and processing them with two sets of laser parameters, resulting in the creation of four datasets [D1, D2, D3, and D4]. These datasets encompass LoF pores, conduction mode, and keyhole formations, each associated with three LPBF regimes denoted as D1, D2, D3, and D4. The experiments utilized a Sisma MYSINT 100 commercial LPBF printer and an airborne AE sensor system with a flat frequency response ranging from 0 to 150 kHz. Validation of the ground truths for the three laser regimes across the four datasets, representing distinct process spaces, was accomplished through the confirmation of cross-sectional images. In the course of fabricating a cube using a powder bed and laser, data acquisition from an AE sensor was triggered when the optical intensity reached a threshold of 0.5 V for each scan length. The photodiode trigger gain was adjusted to saturate at 5 V, and the ensuing continuous-time window, where the optical signal remained at 5 V for 12.5 ms, was calculated and segmented to generate the dataset. Irrespective of the specific regime (Lack of Fusion, Conduction, and Keyhole) or the cube being fabricated (with two powder distributions), the signals obtained during this process were then segmented into a 12.5 ms window comprising 5000 data points. To eliminate any noise, an offline application of a low-pass Butterworth filter with a 150 kHz cut-off frequency was employed, aligned with the frequency response specification of the AE sensor. Each dataset has two files against it [raw/groundtruth label].</p> </div> </div> </div>

restrictedcc-by-4.0Dec 2024View details →

Acoustic monitoring data Roaringwater Bay, SW Ireland.

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opencc-by-4.0Aug 2014View details →

Supplementary material 1 from: Rosa G, Penny S, Andreone F, Crottini A, Holderied M, Rakotozafy L, Schwitzer C (2014) A new species of the Boophis rappiodes group (Anura, Mantellidae) from the Sahamalaza Peninsula, northwest Madagascar, with acoustic monitoring of its nocturnal calling activity. ZooKeys 435: 111-132. https://doi.org/10.3897/zookeys.435.7383

Calls of Boophis ankarafensis sp. n. emitted from streamside vegetation in Ankarafa Forest.: Explanation note: Calls of Boophis ankarafensis sp. n. emitted from streamside vegetation in Ankarafa Forest (Sahamalaza – Iles Radama National Park). Calls consist of multi-pulsed trill notes (type 1) and 1-3 pulsed click notes (type 2). Cut 1: chorus recorded on 13 October 2011, 18:34 h, 26.7 °C; Cut 2: chorus with background calls of Cophyla berara recorded on 30 December 2011 at 18:44 h, 28.4 °C.

Fig. 3 in Temporal occurrence of three blue whale populations in New Zealand waters from passive acoustic monitoring

Fig. 3.—Temporal occurrence pattern of New Zealand (dark blue), Antarctic (red), and Australian (yellow) blue whale song detections at each of the five hydrophones. The y-axis represents the number of hours per day that blue whale song was detected, and the x-axis represents the recording period. Grayed out sections represent gaps in recording due to hydrophone refurbishment.

Fig. 2 in Temporal occurrence of three blue whale populations in New Zealand waters from passive acoustic monitoring

Fig. 2.—Example spectrograms of the three song types recorded in New Zealand waters. (A) New Zealand song on 31 May 2016, (B) Antarctic song on 26 August 2017, and (C) Australian song on 22 January 2017. Spectrograms are configured with a 2048-point fast Fourier transform, Hann window, 50% overlap.

Fig. 4 in Temporal occurrence of three blue whale populations in New Zealand waters from passive acoustic monitoring

Fig. 4.—Conceptual map illustrating the current understanding of the approximate typical range of each blue whale population. Colors indicate the song type, and patterns represent inferred ecological use of each region. Distribution and occurrence patterns are synthesized from acoustic research published in the literature (e.g., Stafford et al. 2004; Balcazar et al. 2015, 2017; Tripovich et al. 2015; McCauley et al. 2018; Warren et al. 2021), and findings presented in this study.

Fig. 1 in Temporal occurrence of three blue whale populations in New Zealand waters from passive acoustic monitoring

Fig. 1.—Map of the study area in the South Taranaki Bight region, with hydrophone locations denoted by the stars. Gray lines show bathymetry contours at 50-m depth increments, from 0 to 500 m. Location of the study area within New Zealand is indicated by the inset map.

controlledIPD-YESFeb 2026View details →

ClinicalTrials.gov32/100

Acoustic Cough Monitoring for the Management of Patients With Known Respiratory Disease

ClinicalTrials.gov study NCT05042063. IPD Sharing: YES. Countries: 1. Publications: 14.

ClinicalTrials.gov32/100

Acoustic Emission Biomarkers for the Detection and Monitoring of Early Knee Osteoarthritis

ClinicalTrials.gov study NCT06351059. IPD Sharing: NO. Countries: 1. Publications: 7.

closedIPD-NOFeb 2026View details →

publicNov 2017View details →

Data from: Acoustic monitoring of coastal dolphins and their response to naval mine neutralization exercises

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