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25 results for “sound processing”
[Dataset] In situ laser-ultrasonic monitoring of Poisson's ratio and bulk sound velocities of steel plates during thermal processes
<p>Data generated and analyzed in the work titled "In situ laser-ultrasonic monitoring of Poisson’s ratio and bulk sound velocities of steel plates during thermal processes". See the associated publication for more context.</p> <p>All files are stored in Matlab's binary MAT-file format.</p> <ul> <li>cutOffs_ZGVs_nu_S1S2_A2A3_S3S6_A4A7.mat <ul> <li>Dispersion relation data of plates obtained from numerical calculation with a range of Poisson's ratios and otherwise arbitrary but fixed material properties.</li> <li>S1S2-, A2A3-, S3S6- and A4A7-ZGV resonance frequencies and k-values</li> <li>L1 and T1 thickness resonance frequencies</li> </ul> </li> <li>lusResults_jmat_dilatometry_data.mat <ul> <li>LUS measurement data and resulting material properties (raw displacement data recorded on the oscilloscope is stored separately to keep the file size reasonable.)</li> <li>Dilatometer measurements</li> <li>JMatPro simulation</li> </ul> </li> <li>lusOscilloscope_data.mat <ul> <li>Normal surface displacement measurement data obtained in situ with LUS and recorded with an oscilloscope</li> </ul> </li> </ul>
Compositional discovery of architecture-aware and sound process models from event logs of multi-agent systems: experimental data.
<p>This repository contains the experimental data used for the evaluation of the compositional approach to the discovery of process models from event logs of multi-agent systems, where agents interact according to specific patterns of synchronous and asynchronous interactions.</p> <p>According to the experiment plan, there is the folder for each interface pattern containing:</p> <ol> <li>The reference model (Petri net encoded in PNML-file)</li> <li>The event log obtained by simulating the behavior of the reference model (XES-file)</li> <li>The model discovered directly from the generated event log (Petri net encoded in PNML-file)</li> <li>The model discovered by composing the agent model w.r.t. the interface pattern (Petri net encoded in PNML-file)</li> </ol>
A Universe of Sound: Processing NASA Data into Sonifications to Explore Participant Response
<p>Files containing additional data for the first and second open-ended response questions of the survey discussed in Section 3.3 of the paper "A Universe of Sound: Processing NASA Data into Sonifications to Explore Participant Response" and text descriptions of the associated sonifications of three astronomical objects (the Galactic Center, Cassiopeia A, and the Chandra Deep Field South).</p>
Compilation of existing underwater PAM repositories, libraries, and applications for sound processing
<p>Resources for passive acoustic monitoring (PAM) are continuously expanding and being developed, yet a major challenge for users is staying up-to-date and finding the best software or application for their acoustics investigation. We expand on previous efforts (Rhinehart & Nicholson, 2022; Felgate, 2023) with the aim of providing a current, comprehensive list of 1) underwater sound repositories of raw sound data without significant processing, 2) biological sound reference libraries, with species or taxa identification, and 3) sound processing tools for visualization, annotation, or analysis. This spreadsheet contains three pages, one dedicated to each of the aforementioned items, along with some descriptive information to help users identify the best resources for their needs.</p> <p>This work was done to support the Global Library of Underwater Biological Sounds (GLUBS) project and funded in part by the Richard Lounsbery Foundation and from funding to SCOR WG #169 (GLUBS) provided by national committees of the Scientific Committee on Oceanic Research (SCOR) and from a grant to SCOR from the US National Science Foundation (OCE--2140395), with support from the International Quiet Ocean Experiment.</p> <p> </p>
Fig. 4. A in Identification of Sound-Producing Hydrophilid Beetles (Coleoptera: Hydrophilidae) in Underwater Recordings Using Digital Signal Processing
Fig. 4. A half-second vocalization by Tropisternus blatchleyi from 0–5,000 Hz changed into the frequency domain using the Fast Fourier Transformation. Active call frequency regions are at 1,100 Hz and 4,400 Hz. The first feature for T. blatchleyi divides the sum of the points in the active frequency band by the sum of the points in the inactive band, yielding a large number in T. blatchleyi exemplar calls.
Fig. 3 in Identification of Sound-Producing Hydrophilid Beetles (Coleoptera: Hydrophilidae) in Underwater Recordings Using Digital Signal Processing
Fig. 3. The classifier algorithm with two features shown in a two-dimensional graph. Beetle call data and noise data are classified based on two beetle call features: difference in active frequency range shape (x-axis and in Matlab™ as a template) and a ratio of amplitudes in an active and non-active frequency range (y-axis). The algorithm is shown as a solid black line. Most beetle calls fall within the correct classification in the lower left-hand corner, however, some fall outside the equation and are classified as noise. Likewise, noises are occasionally classified as beetles. As more features are added, the algorithm becomes multidimensional.
Fig. 2 in Identification of Sound-Producing Hydrophilid Beetles (Coleoptera: Hydrophilidae) in Underwater Recordings Using Digital Signal Processing
Fig. 2. Five distress calls by Berosus pantherinus transformed into the frequency domain using the fast fourier transformation from 0–12,000 Hz (x-axis). Wide active frequency bands can be seen from 1,500–6,000 Hz and 7,000–9,500 Hz. The feature for distress calls is the sum of the data points between 1,000–6,000 Hz with an amplitude (y-axis) greater than 2.
A Treatment for a Form of Age-Related Central Auditory Processing Disorder Consisting of Clemastine Fumarate Plus Engineered Sound
ClinicalTrials.gov study NCT07304024. IPD Sharing: NO. Countries: 1. Publications: 9.
The Effect of Sound-Insulated Music Playing and Fıgured Mask Nebulızer Application on the Processing Anxiety of Children
ClinicalTrials.gov study NCT05881941. IPD Sharing: YES. Countries: 1. Publications: 1.
Characterization of Auditory Processing Involved in the Encoding of Speech Sounds
ClinicalTrials.gov study NCT02574299. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Subjective Evaluation of a Sound Processing Method for Hearing Aids on Auditory Distance Perception
ClinicalTrials.gov study NCT03512951. IPD Sharing: NO. Countries: 1. Publications: 1.
A Free Verbalization Method of Evaluating Sound Design: The Effectiveness of Artificially Intelligent Natural Language Processing Methods and Tools
<p>"Robot" voice sound files. Seventeen sound files were recorded in four formats; raw human voiceover (VO), and three types of robot voice: vocoded voice 1 (“robo”), vocoded voice 2 with music (“kbd”), and a “beep” voice. Each was recorded as 44.1kHz, 24-bit wav files in a professional recording studio. VO was recorded by professional voice actor DB Cooper, who has been the robot voice for several video games, as well as the voice of the DEE BMW internal car AI voice system. Cooper recorded three versions of the emotes on a Sennheiser MKH-416. Professional sound designer pdx Drescher, an expert in robot<br> and interface sound design, created three sets of robot voices from<br> the original voice files. With guidance from one of the authors,<br> pdx was tasked with trying different approaches to turning the VO<br> samples into three different types of robot voice while attempting<br> to maintain the meaning of the original sounds as described in the<br> list above through preserving the prosody/melodic contour of the<br> original. The first set, robo, used some clips from one of pdx’s prior<br> robot voice projects and integrated them to approximate the emo-<br> tional intention of the VO. Clips were re-pitched, manipulated, and<br> modulated using ProTools plugins. For the kbd takes, VO sounds<br> were played into a Shure SM58 microphone. Vocoder patches mod-<br> ified the signal by voice formants, and the pitch was determined<br> by MIDI notes and pitch-bend controllers. Output of the synthe-<br> sizer was then edited with additional synth patches and effects (EQ,<br> modulation, etc.). We made particular use of a plugin called Envy<br> by Cargo Cult, which takes the volume, pitch, and EQ envelopes of<br> one sound (the original VO) and apply them to another sound. This<br> helped make the synth resemble the prosody of the original sound<br> to some degree. The beep sounds underwent a similar development<br> process as the robo takes, but with interface “bleeps and bloops”<br> derived from various sound effects libraries, including the Star Trek<br> LCARS soundset.</p> <p>The following sounds were recorded: 1. Warning calm (“Uh-oh”) 2. Warning alarm (“ah!”) 3. Wrong/<br> error (“rrrrr”) 4. Correct/good (“yay”) 5. Surprise (neutral) (“Oh!”) 6.<br> Surprise (good) (“Oh!”) 7. Surprise (bad) “(ohhh”) 8. Love/adoration<br> (“awww”) 9. Disgust (“ew”) 10. Contempt (“ech”) 11. Guilt (“hmmm”)<br> 12. Confused (“huh?”) 13. Laugh (“ha ha”) 14. Calculating (“hmmm”)<br> 15. Sigh 16. Giggle 17. Pain (“ow ")</p>
CP1150 Sound Processor Speech Perception Compared With the Next Generation of Signal Processing Technology
ClinicalTrials.gov study NCT05286385. IPD Sharing: NO. Countries: 1. Publications: 0.
ClearVoice Sound-processing Strategy for AB HiRes 120 Cochlear Implant Users
ClinicalTrials.gov study NCT01066780. IPD Sharing: Not stated. Countries: 2. Publications: 0.
Evaluation of a HiRes™ Optima Sound Processing Strategy for the HiResolution™ Bionic Ear
ClinicalTrials.gov study NCT01616576. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Sound Processing Innovations in Adult and Paediatric Cochlear Implant Recipients.
ClinicalTrials.gov study NCT05476328. IPD Sharing: NO. Countries: 2. Publications: 0.
OCO-3 Level 2 meteorological parameters interpolated from global assimilation model for each sounding, Retrospective Processing V11r (OCO3_L2_Met) at GES DISC
Version 11r is the current version of the data set. Older versions will no longer be available and are superseded by Version 11r.The Orbiting Carbon Observatory -3 (OCO-3) was deployed to the International Space Station in May, 2019. It is technically a single instrument, almost identical to OCO-2.The Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere.OCO-3 incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. Oxygen-A Band cloud screening algorithm is one of the primary cloud screening tools implemented in the operational OCO processing pipeline. The algorithm was introduced and applied to early GOSAT data with further analysis performed on OCO-2 simulations.The OCO ABO2 algorithm employs a fast Bayesian retrieval to estimate surface pressure and surface albedo from high resolution spectra of the molecular oxygen (O2) A-band, near 0.765 µm. The radiative transfer forward model (FM) assumes a clear-sky condition, i.e. Rayleigh scattering only, such that differences between the modeled and measured radiances are apparent when the measurement scene contains cloud or aerosol.
OCO-3 Level 2 meteorological parameters interpolated from global assimilation model for each sounding, Retrospective Processing V10r (OCO3_L2_Met) at GES DISC
Version 10r is the current version of the data set. Older versions will no longer be available and are superseded by Version 10r.The Orbiting Carbon Observatory -3 (OCO-3) was deployed to the International Space Station in May, 2019. It is technically a single instrument, almost identical to OCO-2.The Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere.OCO-3 incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. Oxygen-A Band cloud screening algorithm is one of the primary cloud screening tools implemented in the operational OCO processing pipeline. The algorithm was introduced and applied to early GOSAT data with further analysis performed on OCO-2 simulations.The OCO ABO2 algorithm employs a fast Bayesian retrieval to estimate surface pressure and surface albedo from high resolution spectra of the molecular oxygen (O2) A-band, near 0.765 µm. The radiative transfer forward model (FM) assumes a clear-sky condition, i.e. Rayleigh scattering only, such that differences between the modeled and measured radiances are apparent when the measurement scene contains cloud or aerosol.
OCO-2 Level 2 meteorological parameters interpolated from global assimilation model for each sounding, Retrospective Processing V11r (OCO2_L2_Met) at GES DISC
Version 11r is the current version of the data set. Older versions will no longer be available and are superseded by Version 11r.The Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. This collection encompass meteorological parameters interpolated from global assimilation model for each sounding.
OCO-2 Level 2 meteorological parameters interpolated from global assimilation model for each sounding, Retrospective Processing V10r (OCO2_L2_Met) at GES DISC
Version 10r is the current version of the data set. Older versions will no longer be available and are superseded by Version 10r.In early 2021, the OCO Team identified an issue with OCO-2 level 2 products processed since January 28, 2020. The Ancillary Geometric Product (AGAP) file, a static file used in OCO-2 Geolocation processing, was inadvertently replaced with an obsolete version. This AGAP file included a ~300 m pointing error. As a result, all OCO-2 Level 2, version 10r, data files for the period January 28 - December 31, 2020, were corrected and replaced. The replacement process was completed by the end of June, 2021. The significance of this error has been described in Kiel et al. (2019; doi:10.5194/amt-12-2241-2019).The Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. The OCO-2 project uses the LEOStar-2 spacecraft that carries a single instrument. It incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. This collection encompass meteorological parameters interpolated from global assimilation model for each sounding.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.