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416 results for “Acoustic data”

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dryad36/100

Data from: Surface Acoustic Wave-based Lab-On-a-Chip for the fast detection of Legionella pneumophila in water

Surface acoustic wave (SAW) -based immuno-biosensors are used for several applications, thanks to their versatility and faster response than conventional analytical methods. SAW immuno-biosensors can be usefully applied to promptly detect bacteria and prevent bacterial infections that can lead to severe diseases. Here, we present a SAW immuno-biosensor to detect Legionella pneumophila in water. Our device, working at ultra-high frequency (740 MHz), is functionalized with an anti-L. pneumophila antibody to maximize the specificity. We report the characteristic curve of the sensor, calculated measuring bacterial samples at known densities, and its related parameters. We also measure L. pneumophila samples contaminated with different Gram-positive and Gram-negative bacterial species (Escherichia coli and Enterococcus faecium) and samples diluted in mains waters. The proposed device is able to detect L. pneumophila in the range from 1×106 to 1×108 CFU/mL, with a limit of blank of 1.22×106 CFU/mL and a limit of detection of 2.01×106 CFU/mL. The nonspecific signal due to contaminant bacteria is very limited and measurements of L. pneumophila are not affected by contamination. We obtain a good detection also in mains water, representing a realistic matrix for L. pneumophila. Our results are encouraging and pave the way to the use of fast, easy-to-use, reliable and precise sensors to prevent bacterial infections in human activities.

opencc-zeroDec 2021View details →

zenodo36/100

Cross-spectra used in "Retrieval and precise phase-velocity estimation of Rayleigh waves by the spatial autocorrelation method between distributed acoustic sensing and seismometer data"

Cross-spectra used in "Retrieval and precise phase-velocity estimation of Rayleigh waves by the spatial autocorrelation method between distributed acoustic sensing and seismometer data ", by Shun Fukushima, Masanao Shinohara, Kiwamu Nishida, Akiko Takeo, Tomoaki Yamada, and Kiyoshi Yomogida  For more information, please contact Shun Fukushima (s-fuku@eri.u-tokyo.ac.jp)

opencc-by-4.0Mar 2024View details →

dryad36/100

Data from: Acoustic indices estimate breeding bird species richness with daily and seasonally variable effectiveness in lowland temperate Białowieża forest

Biodiversity monitoring is important to follow temporal changes of the environment. We examined whether acoustic indices can be used as a rapid and easy-to-apply tool for bird biodiversity estimation in one of the least changed European lowland forests – the Białowieża Forest. We collected soundscape recordings in early and late spring at 84 randomly chosen recording points. At each recording point, we analysed 72 1-min sound samples to evaluate how well acoustic indices predict bird species richness from the perspective of a single sound sample, single survey, and recording point, and how they follow the daily pattern of singing activity. For each 1-min sound sample, we prepared a list of vocalizing bird species and calculated three acoustic indices: Bioacoustic Index (BI), Acoustic Complexity Index (ACI), and Acoustic Diversity Index (ADI). We found that from the perspective of a single 1-min sound sample, BI best predicts the bird species richness, independently of time in the season but variably across the day, while ACI and ADI showed weaker and seasonally and daily variable dependency. The correlation between each index and the number of bird species was stronger in the early survey than in the late survey. All acoustic indices followed daily bird activity patterns, yet they provided greater values before the peak of the species richness estimated by manual spectrogram scanning and listening to recordings. We showed that acoustic indices correlate moderately to strongly with the bird species richness obtained by manual spectrogram scanning and listening to recordings by humans. Therefore, acoustic indices can be used as a tool for rapid estimation of bird biodiversity in temperate forests. However, daily and seasonal variation in effectiveness of acoustic indices should be taken into account in the analysis.

opencc-zeroFeb 2023View details →

zenodo36/100

GNSS-Acoustic Data Capturing the Mw 8.2 July 28, 2021 Chignik Earthquake

GNSS-Acoustic surveys collected at a site, SEM1, along the Alaska Subduction Zone southwest of Kodiak, AK from 2018-2021. These data contain positions close to the trench of the subduction zone both prior to and following the July 28, 2021 Mw 8.2 Chignik earthquake. Contains raw data files, data extractions in a text column format, and processed site positions.

opencc-by-4.0Mar 2023View details →

dryad36/100

Data from: An investigation into the effectiveness of using acoustic touch to assist people who are blind

In this work, we explored the potential of a technique known as "acoustic touch" to assist people who are blind in finding objects. This technique is an auditory sensory augmentation paradigm that uses smart glasses to sonify objects entering the device's field of view. We developed a wearable Foveated Audio Device (FAD) to study the efficacy and usability of using acoustic touch to search, memorise, and reach items. The study involved 14 participants, 7 blind/low vision and 7 sighted blindfolded participants. We compared the wearable device to two idealised conditions, a verbal clock face description and a sequential audio presentation through external speakers. We found that the wearable device can effectively aid the recognition and reaching of an object. We also observed that the device does not significantly increase the user's cognitive workload. These promising results suggest that acoustic touch can provide a wearable and effective method of sensory augmentation.

opencc-zeroAug 2023View details →

zenodo36/100

Dataset for "Benchmarking for the automated detection of southern yellow-cheeked crested gibbon calls from passive acoustic monitoring data"

"Benchmarking automated detection and classification approaches for long-term acoustic monitoring of endangered species: a case study on gibbons from Cambodia" <div> Recent advances in deep learning and transfer learning have revolutionized our ability for the automated detection of acoustic signals from long-term soundscape recordings. Here, we provide a benchmark for the automated detection of southern yellow-cheeked crested gibbon (Nomascus gabriellae) calls recorded in Jahoo, Cambodia. For the benchmarking, we compared the performance of support vector machines (SVMs), a quasi-DenseNet architecture (Koogu), transfer learning with ResNet50 models trained on the ‘ImageNet’ dataset (ResNet), and transfer learning with embeddings from a global birdsong model (BirdNET). We also investigated the impact of varying the number of training samples on the performance of these models. Transfer learning models based on BirdNET embeddings had superior performance with a smaller number of training samples, whereas Koogu and ResNet models only had acceptable performance with a larger number of training samples (>200 gibbon samples). We deployed the BirdNET-based model over > 130,000 hours of continuous soundscape data, which, after manual review, resulted in >12,000 verified true positive detections. We found that gibbon calling events occurred mostly in the early morning hours between 05:00 to 0:600 local time. We had fewer gibbon detections during the monsoon period and found substantial variation in spatial patterns of calling events across months and years. We show that automated detection can be used to investigate long-term spatial and temporal patterns of gibbon calling events. Reliable automated detection approaches are a critical first step for using passive acoustic monitoring to assess endangered gibbon populations at ecologically relevant temporal- and spatial-scales.  Detailed instructions regarding use are provided on GitHub. </div> Link to GitHub: https://github.com/DenaJGibbon/benchmark-gibbon-calls. Please cite both if you use these data:  Clink, D., Cross-Jaya, H., Kim, J., Ahmad, A. H., Hong, M., Sala, R., Birot, H., Agger, C., Vu, T. T., Thi, H. N., Chi, T. N., & Klinck, H. (2024). Dataset for "Benchmarking for the automated detection of southern yellow-cheeked crested gibbon calls from passive acoustic monitoring data" [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.12706803" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12706803</a> Clink DJ, Cross-Jaya H, Kim J, Ahmad AH, Hong M, Sala R, Birot H, Agger C, Vu TT, Thi HN, Chi TN. Benchmarking for the automated detection and classification of southern yellow-cheeked crested gibbon calls from passive acoustic monitoring data. bioRxiv. 2024:2024-08.

opencc-by-4.0Jul 2024View details →

dryad36/100