Find research datasets worth reusing

opencc-zeroAug 2022View details →

Acoustic data of calls of Manx shearwater on Lundy Island

Vocalizations are widely used to signal behavioural intention in animal communication, but may also carry additional information encoded in the signal, in particular, vocalisations may carry acoustic signatures unique to the calling individual. Manx shearwater (Puffinus puffinus) are nocturnal seabirds that breed in dense colonies, where they must recognize and locate mates among thousands of conspecifics calling in the dark. There is evidence for individual vocal signatures in two shearwater species, but quantitative data on the vocalisations of Manx shearwater are lacking. We recorded calls of 13 Manx shearwaters on Lundy Island, UK, by eliciting vocal responses to playback of conspecific calls. We measured several spectral and temporal parameters of the calls, applied linear discriminate analysis with leave-one-out cross-validation, and have confirmed the individual vocal signatures. We then calculated among-individual repeatability of 34 features describing the vocalization to determine the extent to which these features may contribute to individual signature coding. We found that calls cluster by individual in both temporal and spectral characteristics, suggesting these are contributing to Manx shearwaters' unique call signatures.

opencc-by-4.0May 2024View details →

Unlabeled AnuraSet: A dataset for leveraging unlabeled data in machine learning models for passive acoustic monitoring

The Unlabeled AnuraSet (U-AnuraSet) is an extension of the original AnuraSet dataset. It consists of soundscape recordings from passive acoustic monitoring conducted in Brazil. The recording sites are identical to those in the original AnuraSet. Each site comprises 2,666 one-minute raw audio files of unlabeled data. The U-AnuraSet is publicly available to encourage machine learning researchers to explore innovative methods for leveraging unlabeled data in the training of models aimed at solving problems such as anuran call identification. If you find the Unlabeled AnuraSet useful for your research, please consider citing it as follows: Cañas, J.S., Toro-Gómez, M.P., Sugai, L.S.M., et al. A dataset for benchmarking Neotropical anuran calls identification in passive acoustic monitoring. Sci Data 10, 771 (2023). https://doi.org/10.1038/s41597-023-02666-2

opencc-zeroJul 2024View details →

Data from: Performance of unmarked abundance models with data from machine-learning classification of passive acoustic recordings

The ability to conduct cost-effective wildlife monitoring at scale is rapidly increasing due to availability of inexpensive autonomous recording units (ARUs) and automated species recognition, presenting a variety of advantages over human-based surveys. However, estimating abundance with such data collection techniques remains challenging because most abundance models require data that are difficult for low-cost monoaural ARUs to gather (e.g., counts of individuals, distance to individuals), especially when using the output of automated species recognition. Statistical models that do not require counting or measuring distances to target individuals in combination with low-cost ARUs provide a promising way of obtaining abundance estimates for large-scale wildlife monitoring projects but remain untested. We present a case study using avian field data collected in forests of Pennsylvania during the Spring of 2020 and 2021 using both traditional point counts and passive acoustic monitoring at the same locations. We tested the ability of the Royle-Nichols and time-to-detection models to estimate abundance of two species from detection histories generated by applying a machine-learning classifier to ARU-gathered data. We compared abundance estimates from these models to estimates from the same models fit using point-count data and to two additional models appropriate for point counts, the N-mixture model and distance models. We found that the Royle-Nichols and time-to-detection models can be used with ARU data to produce abundance estimates similar to those generated by a point-count based study but with greater precision. ARU-based models produced confidence or credible intervals that were on average 31.9% ( 11.9 SE) smaller than their point-count counterpart. Our findings were consistent across two species with differing relative abundance and habitat use patterns. The higher precision of models fit using ARU data is likely due to higher cumulative detection probability, which itself may be the result of greater survey effort using ARUs and machine-learning classifiers to sample significantly more time for focal species at any given point. Our results provide preliminary support the use of ARUs in abundance-based study applications, and thus may afford researchers a better understanding of habitat quality and population trends, while allowing them to make more informed conservation actions and recommendations.

Fig. 6 in Evolution and systematics of Green Bush-crickets (Orthoptera: Tettigoniidae: Tettigonia) in the Western Palaearctic: testing concordance between molecular, acoustic, and morphological data

Fig. 6 Comparison of the duty cycle in the songs of the T. armeniaca complex and T. caudata (left panel) and the Tettigonia viridissima group (right panel)

Fig. 2 in Evolution and systematics of Green Bush-crickets (Orthoptera: Tettigoniidae: Tettigonia) in the Western Palaearctic: testing concordance between molecular, acoustic, and morphological data

Fig. 2 Oscillograms of the song of the Tettigonia viridissima group (1–9) and T. cantans (10) recorded at two speeds: 1 T. cf. longealata (MO: Ajabo, T = 20 °C), 2 T. cf. vaucheriana (MO: N Fes, T = 20 °C), 3 T. cf. vaucheriana (MO: Bouchfaa W of Taza, T = 21 °C), 4 T. cf. vaucheriana (MO: Tilougguite Pass, T = 23 °C), 5 T. cf. vaucheriana and cf. longealata (MO: El Kebab, T = 25 °C), 6 T. cf. vaucheriana (MO: El Kebab, T = 28–30 °C), 7 T. cf. viridissima (MO: S Aïn Zora, T = 22 °C), 8 T. cf. viridissima (MO: S Aïn Zora, T = 25 °C), 9 T. viridissima (BG: Sofia, T = 27 °C), and 10 T. cantans (IT: Val Malene; from Massa et al. 2012, T = 15 °C)). Scale bar for A is 10 s and for B 2 s

Fig. 5 in Evolution and systematics of Green Bush-crickets (Orthoptera: Tettigoniidae: Tettigonia) in the Western Palaearctic: testing concordance between molecular, acoustic, and morphological data

Fig. 5 Appearance of some taxa of Western Palaearctic Tettigonia (relative size proportions between photos not retained). a T. cantans, male, Germany, Gunzenhausen; b T. cantans, female, Germany, Gunzenhausen; c T. uvarovi Ebner, 1946—male, holotype, Siberia (NHMW), lateral view; d same, dorsal view; e T. caudata, male, Bulgaria, Russe district, Byala; f T. acutipennis Ebner, 1946—male, holotype, "Kleinasien 1914 | Marasch, Tölg. | coll. R. Ebner" (NHMW), dorsal view; g same, lateral view; h T. armeniaca, male, Armenia, Djermuk; i T. armeniaca, male, Turkey, Ispir; j T. viridissima morphotype of longealata, male, Morocco, El Kebab; k T. viridissima morphotype of longealata, female, Morocco, El Kebab; l T. viridissima morphotype of vaucheriana, male, Morocco, El Kebab; and m T. viridissima, male and female in copula, Bulgaria, Haskovo district, Kostilkovo village

Fig. 4 in Evolution and systematics of Green Bush-crickets (Orthoptera: Tettigoniidae: Tettigonia) in the Western Palaearctic: testing concordance between molecular, acoustic, and morphological data

Fig. 4 Phylogenetic tree of the genus Tettigonia based on BI analysis of concatenated COI-ITS1-ITS2 sequences. BI posterior probability (PP) values are shown near resolved branches (only support values above 0.50). Species groups, as defined by genetic and morpho-acoustic data, are distinctly shaded, and the respective branches are marked with an open circle and a capital letter as follows: "A"—T. viridissima group, "B"—T. caudata group, and "C"—T. cantans group. Haplotype codes correspond to Table 1 in the Supplement, followed by morphological identification. Squares on the right side of names correspond to relative wing length: filled squares short wings and open squares long wings;

Fig. 7 in Evolution and systematics of Green Bush-crickets (Orthoptera: Tettigoniidae: Tettigonia) in the Western Palaearctic: testing concordance between molecular, acoustic, and morphological data

Fig. 7 Relationship between the duration of chirps and inter-chirp intervals in T. caudata and the Tettigonia armeniaca complex. Green triangles mark recordings from Ispir, Turkey, where monosyllabic, disyllabic, and polysyllabic songs of T. armeniaca were recorded, as well as a song of T. caudata (Color figure online)

opencc-by-4.0Apr 2024View details →

Data set for "Bidirectional microwave-optical transduction based on integration of high-overtone bulk acoustic resonators and photonic circuits"

The repository contains raw data, processing scripts, simulation and GDS files for the manuscript "Bidirectional microwave-optical transduction based on integration of high-overtone bulk acoustic resonators and photonic circuits". For detailed usage instructions, please take a look at the README.txt file.

opencc-by-4.0May 2018View details →

Data for "Sounding out Ecoacoustic Metrics: Avian species richness is predicted by acoustic indices in temperate but not tropical habitats"

This deposit contains the data for the paper A Multi-habitat, Comparative Evaluation of Ecoacoustic Indices for Biodiversity Monitoring: Acoustic Indices Predict Avian Species Richness in Temperate but not Tropical Habitats. (Ecological Indicators) The dataset contains a series of 1 min wav files recorded across UK and Ecuadorian habitats. Each one has 26 acoustic indices calculated on it, and a full list of avian species and abundances and GPS data for each sample site. Abstract Affordable, autonomous recording devices facilitate large scale acoustic monitoring and Rapid Acoustic Survey is emerging as a cost-effective approach to ecological monitoring; the success of the approach rests on the development of computational methods by which biodiversity metrics can be automatically derived from remotely collected audio data. Dozens of indices have been proposed to date, but systematic validation against classical, in situ diversity measures. This study conducted the most comprehensive comparative evaluation to date of the relationship between avian species diversity and a suite of acoustic indices across a wide range of ecological conditions. Acoustic surveys were carried out across habitat gradients in temperate and tropical biomes. Baseline avian species richness and subjective multi-taxa biophonic density estimates were established through aural counting by expert ornithologists. 26 acoustic indices were calculated and compared to observed variations in species diversity. Five acoustic diversity indices (Bioacoustic Index, Acoustic Diversity Index, Acoustic Evenness Index, Acoustic Entropy, and the Normalised Difference Sound Index) were assessed as well as three simple acoustic descriptors (root-mean-square, spectral centroid and zero-crossing rate). Highly significant correlations, of up to 65%, between acoustic indices and avian species richness were observed across temperate habitats, supporting the use of automated acoustic indices in biodiversity monitoring where a single vocal taxon dominates. Significant, weaker correlations were observed in neotropical habitats which host multiple non-avian vocalizing species. Multivariate classification analyses suggest that AIs also track observed differences in habitat-dependent community composition and that each habitat has a distinct soundscape. Multivariate analyses of the relative predictive power of AIs show that compound indices are more powerful predictors of avian species richness than any single index and simple descriptors contribute to predicting avian diversity in multi-taxa tropical environments. Our results support the use of community level acoustic indices as a proxy for species richness and point to the potential for tracking of habitat-dependent changes in community composition. Recommendations for the design of compound indices for multi-taxa community composition appraisal are put forward, with consideration for the requirements of next generation, low power remote monitoring networks.   Sampling Methods (extract from paper) Acoustic surveys were carried out along a gradient of habitat degradation (1 forested, 2 regenerating forest and 3 agricultural land) in South East (SE) England and North Western (NW) Ecuador. The six sites (UK1, UK2, UK3, EC1, EC2, EC3) were sampled consecutively from May 6th - Aug 25th 2015. All UK sites were in the county of Sussex, in SE England, an area of weald clays (Fig. 2, left) and included ancient woodland (UK1), regenerating farmland with patches of woodland (UK2) and a downland barley farm (UK3).1 min mono audio recordings made every 15 minutes at three different habitats in the UK Ten day acoustic surveys were carried out consecutively at each study site using 15 Wildlife Acoustics Song Meter audio field recorders. Sampling points were arranged in a grid at a minimum distance of 200 m to minimise pseudo replication (the sound of most species being attenuated over this distance in all biomes). Altitudinal range of sample points across sites was minimised in order to prevent introduction of extraneous, confounding gradients (UK varied between 10 m – 50 m and Ecuador 130 m – 390 m). Recording schedules captured 1 min every 15 min around the clock for 10 days at each site, resulting in 960 recordings at each of 15 sample points for 3 habitat types in 2 different climates (86,400 1 minute recordings in total). Data across the 15 sample points was pooled; inter-site variation was not explored in the current analyses. In the UK 3½ hours of each dawn chorus was sampled starting at 1 hour before sunrise. This range was determined to capture the onset, progression and peak of the dawn chorus, creating a temporal gradient. The equatorial dawn chorus is more compact and was sampled for 2¼ hours starting 15 mins before sunrise, capturing a comparable chorus onset and peak.

opencc-by-4.0Jul 2018View details →

Data for "Nonlinear Trapping Stiffness of Mid-Air Single-Axis Acoustic Levitators"

Data associated with the manuscript entitled "Nonlinear Trapping Stiffness of Mid-Air Single-Axis Acoustic Levitators".

opencc-by-4.0Sep 2018View details →

Research data supporting "Engineering anisotropic muscle tissue using acoustic cell patterning"

Raw research data supporting the publication: Armstron, JPK et al., "Engineering anisotropic muscle tissue using acoustic cell paterning", Advanced Materials, DOI: 10.1002/adma.201802649 (2018)

opencc-by-4.0Sep 2024View details →

Assessment of Simulations in Faust and Tascar for the Development of Audio Algorithms in Acoustic Environments - Code and Data

Developing and testing audio algorithms with hard real-time constraints can be a complex task, requiring certain programming skills and/or specialized equipment. However, many things can be tested in simulations on an ordinary computer, using <a href="https://tascar.org/" target="_blank" rel="noopener">TASCAR</a> for acoustic scene creation and <a href="https://faust.grame.fr/" target="_blank" rel="noopener">FAUST</a> for signal processing. Their capability are evaluated and compared to measurements using an FxLMS algorithm for active noise control as example. This repository contains code and measured data of the publication “Assessment of simulations in FAUST and TASCAR for the development of audio algorithms in acoustic environments”, presented at the International Faust Conference 2024 in Turin, Italy.

opencc-by-4.0Sep 2024View details →

Dataset for the manuscript "Self-Supervised Coherence-Based Denoising on Cryoseismological Distributed Acoustic Sensing Data"

The dataset contains cryoseismological data recorded in July 2020 on the Rhonegletscher, Switzerland, collected using both Distributed Acoustic Sensing and seismometers. This dataset provides the necessary data to reproduce the results presented in the paper “Self-Supervised Coherence-Based Denoising on Cryoseismological Distributed Acoustic Sensing Data.” The corresponding code is available on GitHub, and the paper can be accessed via Authorea.   Abstract:  One major challenge in cryoseismology is that signals of interest are often buried within the high noise level emitted by a multitude of environmental processes. Events of interest potentially stay unnoticed and remain unanalyzed, particularly because conventional sensors cannot monitor an entire glacier. However, with Distributed Acoustic Sensing (DAS), we can observe seismicity over multiple kilometers. DAS systems turn common fiber-optic cables into seismic arrays that measure strain rate data, enabling researchers to acquire seismic data in hard-to-access areas with high spatial and temporal resolution. We deployed a DAS system on Rhonegletscher, Switzerland, using a 9 km long fiberoptic cable that covered the entire glacier, from its accumulation to its ablation zone, recording seismicity for one month. The highly active and dynamic cryospheric environ ment, in combination with poor coupling, resulted in DAS data characterized by a low Signal-to-Noise Ratio (SNR) compared to classical point sensors. Our objective is to ef fectively denoise this dataset. We use a self-supervised J -invariant U-net autoencoder capable of separating incoherent environmental noise from temporally and spatially coherent signals of interest (e.g., stick-slip or crevasse signals). The method shows enhanced inter-channel coherence, increased SNR, and significantly improved visibility of the icequakes. Further, we compare different training data types varying in recording position, wavefield component, and waveform diversity. Our approach has the potential to enhance the detection capabilities of events of interest in cryoseismological DAS data, hence to improve the understanding of processes within Alpine glaciers.

opencc-zeroFeb 2020View details →

Data from: Koe: Web-based software to classify acoustic units and analyse sequence structure in animal vocalisations

1. Classifying acoustic units is often a key step in studying repertoires and sequence structure in animal communication. Manual classification by eye and ear remains the primary method, but new tools and techniques are urgently needed to expedite the process for large, diverse datasets. 2. Here we introduce Koe, an application for classifying and analysing animal vocalisations. Koe offers bulk-labelling of units via interactive ordination plots and unit tables, as well as visualisation and playback, segmentation, measurement, data filtering/exporting and new tools for analysing repertoire and sequence structure—in an integrated environment. 3. We demonstrate Koe with a real-world case study of New Zealand bellbird Anthornis melanura songs from an archipelago metapopulation. Having classified 21,500 units in Koe, we compare repertoires and sequence structure between sites and sexes. 4. Koe is web-based (koe.io.ac.nz) and easy to use, making it ideal for collaboration, education and citizen science. By enabling large-scale, high-resolution classification and analysis of animal vocalisations, Koe expands the possibilities for bioacoustics research.

opengpl-3.0-or-laterJan 2023View details →

A river on fiber: high resolution fluvial monitoring with distributed acoustic sensing – Data, Matlab Scripts and App

Matlab software and data associated with Roth et al. (submitted to Seismica, 2025).

opencc-by-nc-sa-4.0Nov 2022View details →

Data Repository for MYRiAD: A Multi-Array Room Acoustic Database

In the development of acoustic signal processing algorithms, their evaluation in various acoustic environments is of utmost importance. In order to advance evaluation in realistic and reproducible scenarios, several high-quality acoustic databases have been developed over the years. In this paper, we present another complementary database of acoustic recordings, referred to as the Multi-arraY Room Acoustic Database (MYRiAD). The MYRiAD database is unique in its diversity of microphone configurations suiting a wide range of enhancement and reproduction applications (such as assistive hearing, teleconferencing, or sound zoning), the acoustics of the two recording spaces, and the variety of contained signals including 1214 room impulse responses (RIRs), reproduced speech, music, and stationary noise, as well as recordings of live cocktail parties held in both rooms. The microphone configurations comprise a dummy head (DH) with in-ear omnidirectional microphones, two behind-the-ear (BTE) pieces equipped with 2 omnidirectional microphones each, 5 external omnidirectional microphones (XMs), and two concentric circular microphone arrays (CMAs) consisting of 12 omnidirectional microphones in total. The two recording spaces, namely the SONORA Audio Laboratory (SAL) and the Alamire Interactive Laboratory (AIL), have reverberation times of 2.1s and 0.5s, respectively. Audio signals were reproduced using 10 movable loudspeakers in the SAL and a built-in array of 24 loudspeakers in the AIL. MATLAB and Python scripts are included for accessing the signals as well as microphone and loudspeaker coordinates. For a detailed description, please refer to the paper (<a href="https://arxiv.org/abs/2301.13057">preprint</a>, <a href="https://asmp-eurasipjournals.springeropen.com/articles/10.1186/s13636-023-00284-9">published</a>). Two files are provided, containing two different versions of the database: <table> <tbody> <tr> <td>MYRiAD_V2.zip</td> <td>The full version of the database (31.3 GB).</td> </tr> <tr> <td>MYRiAD_V2_econ.zip </td> <td>The economy-sized version, containing source signals and RIRs only (201.7 MB).</td> </tr> </tbody> </table> If you use the database, please cite the paper as follows: @article{dietzen2023myriad,   author = {Dietzen, T. and Ali, R. and Taseska, M. and van Waterschoot, T.},   title = {{MYRiAD}: A Multi-Array Room Acoustic Database},   journal = {EURASIP J. Audio Speech Music Process.},   volume = {2023, article no. 17},   number = {},   month = {Apr.},   year = {2023},   pages = {1--14} } ___________________________________________________________________________________________________________ Change log (as compared to Version 1.0): <ol> <li>Fixed erroneous file names in /audio/AIL/SU1/P2/.</li> <li>In the full version, applied a time shift to some of the speech, noise, and music recordings in the SAL (at most 2 samples, compensating for a slow phase drift, see manuscript for further details).</li> <li>Created an economy-sized version of the database containing source signals and RIRs only.</li> <li>Adjusted the following scripts for the economy-sized version:  - /tools/MATLAB/load_audio_data.m -  /tools/Python/load_audio_data.py</li> </ol>

opencc-zeroDec 2021View details →

Data for: Zebra finch song ecology: monitoring of breeding, observational transects, focal and year-round acoustic recordings, and a large-scale simultaneous playback experiment

Male songbirds sing to establish territories and to attract mates. However, increasing reports of singing in non-reproductive contexts and by females show that song use is more diverse than previously considered. Therefore, alternative functions of song, such as social cohesion and synchronisation of breeding, by and large were overlooked even in such well-studied species as the zebra finch (Taeniopygia guttata). In these social songbirds only the males sing and pairs breed synchronously in loose colonies following aseasonal rain events in their arid habitat. As males are not territorial, and pairs form long-term monogamous bonds early in life, conventional theory predicts that zebra finches should not sing much at all; yet they do and their song is the focus of hundreds of lab-based studies. We hypothesise that zebra finch song functions to maintain social cohesion and to synchronise breeding. Here we test this idea using data from five years of field studies, including observational transects, focal and year-round audio recordings, and a large-scale playback experiment. We show that zebra finches frequently sing while in groups, that breeding status influences song output at the nest and at aggregations, that they sing year-round, and that they predominantly sing when with their partner, suggesting that song remains important after pair formation. Our playback reveals that song actively features in social aggregations as it attracts conspecifics. Together, these results demonstrate that birdsong has important functions beyond territoriality and mate choice, illustrating its importance in coordination and cohesion of social units within larger societies.

opencc-zeroDec 2022View details →

Wideband acoustic transceiver data from probe attached to rosette on PolarFront 2022-05 cruise

PolarFront 2022-05 WBAT/Rosette Proprietary echosounder files and mission plan from Kongsberg Maritime WBT tranceiver EKA, serial number 253119. Time coverage: 2022-05-20T04:32:12Z/2022-05-25-T12:30:06Z Size: 2.8GiB <blockquote> Acoustic probe (WBAT) At some stations we deployed an acoustic probe composed of a Wideband Acoustic Transceiver (WBAT; Kongsberg Maritime AS) mounted on the LOPC rosette frame and connected to a sideward-looking 38 kHz split beam transducer (Model ES38-18DK; 36-45 kHz) and a sideward- looking 333 kHz single beam transducer (Model ES333-7CDK-single; 280-380 kHz), both operated in broadband mode split-beam wideband (Figure 1c). The ping rate was set to 1 second, the range to 50 m, the pulse length to 2,048 µs, and power to 225 W at 38kHz and 38 W at 333 kHz. The WBAT was calibrated in January 2022. The time of the WBAT was synchronized with the LOPC-CTD to extract the exact depth at each ping. </blockquote> For a list of probe stations, see M Daase (ed., 2022, table 4.1, p. 32). References Malin Daase (ed.) (2022). <a href="https://doi.org/10.5281/zenodo.7128746">PolarFront May 2022 Cruise Report</a>. Zenodo. https://doi.org/10.5281/zenodo.7128746