Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
10
datasets available to search
ShareScore release 0.9.0
Dataset results
10 results for “acoustic technology”
Dataset for "Integrated acoustic resonators in commercial fin field-effect transistor technology"
<p>Companion dataset and code for "Integrated acoustic resonators in commercial fin field-effect transistor technology" by Anderson, He, Bahr, and Weinstein.</p> <p>https://www.nature.com/articles/s41928-022-00827-6</p> <p>https://arxiv.org/abs/2107.00608</p> <p> </p>
CAVEMOVE: An acoustic database for the study of voice-enabled technologies inside moving vehicles
<p>We provide a collection of multichannel audio recordings obtained inside four different cars. The recording process involves (i) recordings of acoustic impulse responses, which are acquired at static conditions and provide the means for modeling the speech and car-audio components (ii) recordings of acoustic noise at a wide range of both static and in-motion conditions. Data is recorded with two different microphone configurations and particularly (i) a compact microphone array or (ii) a distributed microphone setup. A <a href="https://github.com/SPL-FORTH-ICS/CAVEMOVE">python API and a Matlab API</a>, that can be freely downloaded from CAVEMOVE github, is provided as the means to easily exploit the open access audio recordings to synthesize mixtures of speech and acoustic noise at different driving conditions. This way, the user can easily synthesize the microphone signals required for research on voice enabled technologies inside moving vehicles.</p> <p>All audio recordings and impulse responses are provided at 16 kHz sampling rate and are in the form of 8-channel .wav files.</p> <p>Some basic principles followed in CAVEMOVE APIs are the following. <br>- We provide functions for retrieving speech and noise components as separate entities (e.g. numpy arrays). Users must then add the speech and noise components to derive a mixture.<br>- Noise recordings are derived as a function of driving conditions, specifically the speed (in km/hour) and the window aperture (3 or 4 different windows conditions are considered in each vehicle)<br>-Apart from the basic noise components, we also provide means for adding ventilation/air-condition noise and also, interference from the built-in car audio system (e.g. radio, cd player etc)<br>-To produce the speech components, users must provide their own dry speech recordings <br>-To produce the car-audio components, users must provide their own audio signals.</p> <p>The Documentation .pdf file that we provide along with the audio recordings lists all the conditions that were recorded or measured inside the three cars (the same documantation can also be found in CAVEMOVE github). This information is important for correct use of the python API, since, asking for a condition that was not recorded can potentially produce an error. Note that a list of recorded driving conditions given the car name and the microphone configuration can also be retrieved from auxiliary functions included in the APIs.</p> <p>For any questions with respect to CAVEMOVE dataset or API, feel free to send an email to <br>Andreas Symiakakis at andrysmi@ics.forth.gr <br>or<br>Nikos Stefanakis at nstefana@ics.forth.gr</p> <p>CAVEMOVE project is funded by the Institute of Computer Science of the Foundation for Research and Technology-Hellas (FORTH).</p>
Implantation of the HiRes90K™ Advantage Cochlear Implant With HiFocus™ Mid-Scala and Development of a Combined Electric and Acoustic Stimulation Technology in Adults With Partial Deafness
ClinicalTrials.gov study NCT02189798. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Data from: Performance assessment of two whole-lake acoustic positional telemetry systems - is reality mining of free-ranging aquatic animals technologically possible?
Acoustic positional telemetry systems (APTs) represent a novel approach to study the behaviour of free ranging aquatic animals in the wild at unprecedented detail. System manufactures promise remarkably high temporal and spatial resolution. However, the performance of APTs has rarely been rigorously tested at the level of entire ecosystems. Moreover, the effect of habitat structure on system performance has only been poorly documented. Two APTs were deployed to cover two small lakes and a series of standardized stationary tests were conducted to assess system performance. Furthermore, a number of tow tests were conducted to simulate moving fish. Based on these data, we quantified system performance in terms of data yield, accuracy and precision as a function of structural complexity in relation to vegetation. Mean data yield of the two systems was 40 % (Lake1) and 60 % (Lake2). Average system accuracy (acc) and precision (prec) were Lake1: acc = 3.1 m, prec = 1.1 m; Lake2: acc = 1.0 m, prec = 0.2 m. System performance was negatively affected by structural complexity, i.e., open water habitats yielded far better performance than structurally complex vegetated habitats. Post-processing greatly improved data quality, and sub-meter accuracy and precision were, on average, regularly achieved in Lake2 but remained the exception in the larger and structurally more complex Lake1. Moving transmitters were tracked well by both systems. Whereas overestimation of moved distance is inevitable for stationary transmitters due to accumulation of small tracking errors, moving transmitters can result in both over- and underestimation of distances depending on circumstances. Both deployed APTs were capable of providing high resolution positional data at the scale of entire lakes and are suitable systems to mine the reality of free ranging fish in their natural environment. This opens important opportunities to advance several fields of study such as movement ecology and animal social networks in the wild. It is recommended that thorough performance tests are conducted in any study utilizing APTs. The APTs tested here appear best suited for studies in structurally simple ecosystems or for studying pelagic species. In such situations, the data quality provided by the APTs is exceptionally high.
Feasibility of Remote Evaluation and Monitoring of Acoustic Pathophysiological Signals With External Sensor Technology in Covid-19
ClinicalTrials.gov study NCT04695821. IPD Sharing: NO. Countries: 1. Publications: 0.
Acoustic Radiation Force Impulse (ARFI)Technology in Prediction of Liver Fibrosis
ClinicalTrials.gov study NCT01268865. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Feasibility of Remote Evaluation and Monitoring of Acoustic Pathophysiological Signals With External Sensor Technology
ClinicalTrials.gov study NCT04693091. IPD Sharing: NO. Countries: 1. Publications: 0.
Comparison Study Between Nellcor Respiration Rate Technology (RRoxi) and Masimo Acoustic Respiration Rate (RRa)
ClinicalTrials.gov study NCT02657733. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Data from: Performance assessment of two whole-lake acoustic positional telemetry systems - is reality mining of free-ranging aquatic animals technologically possible?
Open the record for dataset details and reuse information.
Towards Using Virtual Acoustics for Evaluating Spatial Ecoacoustic Monitoring Technologies - Data
<h1>About</h1> <p>This database contains the raw data and certain outputs used for the project 'Towards Using Virtual Acoustics for Evaluating Spatial Ecoacoustic Monitoring Technologies'. In this work, we developed an ambisonic Virtual Sound Environment (VSE) for simulating real natural soundscapes to evaluate spatial PAM technologies in a more controlled and repeatable manner.</p> <p>We set three objectives to validate this approach:</p> <ul> <li>(O1) to determine whether the VSE could replicate natural soundscapes well enough to be a test environment;</li> <li>(O2) to pilot the VSE as a test environment for Passive Acoustic Monitoring (PAM) hardware; and, </li> <li>(O3) to pilot the VSE as a test platform for PAM software.</li> </ul> <p>To meet these objectives, we used a recently-developed, open source six-microphone field recorder to capture recordings of six field sites and their VSE-based simulations. Sites were based at the Imperial College Silwood Park Campus (Ascot, UK). For O1, we compared field and VSE recordings using a typical suite of ecoacoustic metrics. For O2, we used the VSE to explore how orientation impacts the performance of the six-microphone array. We extended the suite of metrics from O1 to compare VSE recordings from this array at various pitch angles: vertical (as in the field), 45° pitch, and horizontal. For O3, we investigate how BirdNET and HARKBird, software for classifying and localising avian calls, respectively, perform on bird calls added to the VSE-replicated soundscapes. We compare adding calls by encoding to the ambisonics domain and by playback from individual loudspeakers. </p> <p> </p> <h3><strong>The data is organised as follows: </strong></h3> <p><strong>'6mic Audio O1O2': </strong>contains the six-channel field and VSE-based recordings of all six sites. Recordings are approximately 10 minutes long. Note that there are three VSE-based recording per site, for each recording orientation used in the VSE (vertical, 45°, and horizontal). Several of our analyses used low-passed versions of these recordings (4 kHz cutoff and 12 dB roll-off); note that the recordings provided here are raw and therefore not low-passed.</p> <ul> <li>File names indicate whether the recording was performed in the field or VSE ('Field' vs 'ReRec') and the orientation of the 6mic array for the latter ('V', '45', or 'H').</li> <li>The final number in each filename indicates the Site the recording corresponds to (1-6).</li> </ul> <p><strong>'6mic Audio O3':</strong> contains the six-channel VSE recordings of five of the original sites with additional avian calls located at certain moments in space and time in each recording. 10 bird calls were added to the soundscapes, each one in its own soundscape recording (each of the five sites' soundscapes was therefore used twice). Four methods of adding the bird calls were trialled, hence there are 40 files in this folder – 10 for each of: ambisonic encoding, playback from individual loudspeakers, playback from individual loudspeakers with no reverb, and playback from individual loudspeakers with no reverb or background soundscape (i.e., 'soloed'). Our analyses focussed on comparing the first two of these methods. The filenames are structured as follows:</p> <ul> <li> "sXsYbirdNameEmbeddingMethod', where X corresponds to the site the background soundscape was recorded at,</li> <li>Y indicates the position of the sinusoidal sweep used to create simulated reverb of the bird call (1-4 for 0°, 90°, 180°, or 270° around the device used to capture the ambisonic soundscape recordings),</li> <li>'birdName' is the common name of the added species, and</li> <li>'EmbeddingMethod' is either: <ul> <li>'Ambi' (ambisonic encoding),</li> <li>'LSPK' (playback from an individual loudspeaker),</li> <li>'LSPK-NR' (playback from an individual loudspeaker with no reverb), or</li> <li>'Solo' (soloed playback from an individual loudspeaker).</li> </ul> </li> <li>Note that due to issues of spatial aliasing, we low-passed the 'Ambi' and 'LSPK' recordings in our analyses (as the two main embedding methods compared) using a 4 kHz cutoff and 12 dB roll-off. However, again the raw (not low-passed) audio is shared here.</li> </ul> <p><strong>'Acoustic Indices':</strong> contains the CSV files with matrices of the Acoustic Indices extracted from the first channel of the low-passed '6mic Audio O1O2' recordings.</p> <ul> <li>Each column is for a different index, rows are the values over time (indices were extracted on 30 s windows).</li> <li>We extracted the following 7 common acoustic indices: <ul> <li>Acoustic Complexity Index (ACI; column 1),</li> <li>Acoustic Diversity Index (ADI; column 2),</li> <li>Acoustic Evenness (AEve; column 3),</li> <li>Bioacoustic Index (Bio; column 4),</li> <li>Normalised Difference Soundscape Index (NDSI; column 5)</li> <li>Acoustic Entropy (H; column 6), and</li> <li>Median of the Amplitude Envelope (M; column 7).</li> <li>Filenames indicate: <ul> <li>the field site,</li> <li>whether the indices are for a field or VSE ('Lab') recording, and</li> <li>the orientation of the recording for the latter ('Vert', '45', or 'H').</li> <li>The 'LP' suffix denotes that these indices were extracted from a low-passed version of the 6mic O1O2 recordings (4 kHz cutoff and 12 dB roll-off).</li> </ul> </li> </ul> </li> </ul> <p><strong>'BirdNET O1O2 Outputs':</strong> CSV files generated from avian call classifier BirdNET (using the Winows GUI version) using the first channel ('Mic 1') of the '6mic Audio O1O2' recordings.</p> <p><strong>'BirdNET O3 Outputs':</strong> outputs of BirdNET on the '6mic Audio O3' recordings.</p> <ul> <li>Here, rather than the raw CSV files generated by BirdNET, the BirdNET results have been filtered to just those during the added bird calls' start and end times, and have been compiled into two CSV files: <ul> <li>one for recordings of VSE-based soundscapes with bird calls added by <em>ambisonic encoding</em> and</li> <li>another for calls added by <em>individual loudspeaker playback</em>.</li> </ul> </li> <li>These CSV files also contain columns to specify added birds' site, sweep (used to generate reverberation, see '6mic Audio O3' above), azimuth and elevation.</li> </ul> <p><strong>'HARKBird O1O2 Outputs':</strong> outputs of avian call localisation tool HARKBird on the '6mic Audio O1O2' recordings. Note that HARKBird outputs a folder with additional results for each file, however, only the CSV files presented here. These contain the times and estimated azimuth angles of bird calls and were the only HARKBird ouput used for subsequent analyses.</p> <ul> <li>Filenames indicate whether recordings are from the field ('Field') or VSE ('ReRec'), and</li> <li>those for the latter also indicate the recording orientation ('Vert', 45' 'H').</li> <li>The final number in each filename indicates the site the recording corresponds to.</li> </ul> <p><strong>'HARKBird O3 Outputs':</strong> CSV files of HARKBird outputs (as described above) for the '6mic Audio O3' recordings.</p> <ul> <li>Filenames are based on those for this set of recordings (see '6mic Audio O3' above);</li> <li>names that contain 'LP' were low-passed (with a 4 kHz cutoff frequency and 12 dB roll-off) prior to passing through HARKBird.</li> </ul> <p><strong>'Manual Labels O2':</strong> manual labels of audible bird calls' species in the omnidirectional (first) channel of the Zylia ZM-1 ambisonic recordings (see 'Zylia Recordings' below) for all sites. This data was used to calculate the precision and recall of BirdNET's outputs on the 6mic O1O2 recordings for these sites.</p> <p><strong>'VGGish Features':</strong> contains the 128-dimension feature embedding of the pre-trained VGGish convolutional neural network extracted from the '6mic Audio O1O2' recordings.</p> <ul> <li>Filenames indicate the site,</li> <li>whether the recording was made in the field or VSE ('Lab'),</li> <li>and the recording's orientation.</li> <li>Note again that recordings were low-passed (4 kHz cutoff and 12 dB roll-off) prior to the feature extraction, hence the 'LP' suffix.</li> </ul> <p><strong>'Zylia Recordings':</strong> approximately 10 minute field recordings of the 6 study sites captured with the 19-microphone ZYLIA 'ZM-1' recorder. These recordings have been converted to third order ambisonic 'b-format' (16 channels) using Furse-Malham channel ordering and SN3D normalisation. This was achieved with the 'Zylia Ambisonics Converter' software. These third-order ambisonic recordings were used to replicate the six sites' soundscapes in the VSE.</p> <p>The accompanying code for this dataset has been submitted via ScholarOne with the manuscript for peer-review.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.