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17 results for “acoustic source”
Acoustical measurements of a rabab reconstructed after a pictorial source from the 13th century (Cantigas de Santa María)
<p>Instrument: rabab<strong> </strong><br>Pictorial source: <i>Cantigas de Santa Maria</i>, <i>E</i>-Codex (<i>Códice de los músicos</i>), ca. 1284, fol. 118r, <i>Cantiga</i> 110, Madrid, San Lorenzo de El Escorial, Real Biblioteca del Monasterio del Escorial, Ms. b-I-2 <br>Instrument maker: Thilo Hirsch <br>Year of manufacture: 2021 <br>Location: Basel, property of the ensemble arcimboldo</p><p>Dimensions: <br>Total length: 468 mm <br>Max. Body width: 104 mm <br>Body depth: approx. 80 mm</p><p>Vibrating string lengths: <br>a-string: 403 mm <br>d-string: 401 mm</p><p>Materials: <br>Body: cherry <br>Pegbox: cherry <br>Fingerboard: maple <br>Bars: spruce <br>Nut/String attachment button: bone <br>Bridge: maple <br>Top: goatskin</p><p>The main aim of this research was to measure the acoustic effects of the different sound holes. To do this, the instrument was first measured with the two open rosettes on the fingerboard and then the upper one was sealed with a piece of wood (see photos of the setup).</p><p>Acoustical measurements: Alexander Mayer, mdw - University of Music and Performing Arts Vienna, Department of Music Acoustics – Wiener Klangstil (IWK), 25.9.2023</p><p>Force: Impact hammer exciting at the bass side of the bridge <br>ACC: Acceleration measured on the same side, close to the impact point. <br>Average is the average of all measurements (to be used in the analysis).</p><p>Photos of the setup: Thilo Hirsch</p><p>Folder cantigas_rosette_o_offen: <br>Files: cantigas_roo_1 to 6 (Description: upper rosette open) <br>File: cantigas_roo_do (Description: upper rosette open / damper moved between 2 rosettes) </p><p>Folder cantigas_rosette_o_zu: <br>Files: cantigas_roz_1 to 6 (Description: upper rosette closed with wooden sheet)</p><p>________________________</p><p>How to read VIA-Files: <br>Line 1 to 9: Header, Line 8 holds the number of values</p><p>Data is organized as followed: 1st col: Frequency [Hz], 2nd col: Magnitude [as Factor not dB!], 3rd col: Phase [rad] 4th col: Real part [as Factor not dB!] 5th col: Imaginary part [as Factor not dB!]. So only first 3 columns are needed</p><p>To get dB Value: Amplitude[dB] = 20 log [Magnitude]</p><p>Usually the Magnitude was calculated as response/reference (input) in the frequency domain. As for measuring the mechanical admittance the sensor is most likely an accelerometer (capturing the response in m/s2 of the object of interest) and the reference an impact hammer capturing the input force in Newton. As the mechanical admittance is defined as v/F (speed over force) the acceleration signal has to be integrated. The here captured signals are integrated in the frequency domain, what means the magnitude is divided by the corresponding frequency value in s-1. </p><p>Values coded like: 3.30750000000000E+1 -> 3.3075 * 10 -> 33.075</p>
Acoustical measurements of a rabab reconstructed after a pictorial source from the 14th century
<p>Instrument: rabab<strong> </strong><br>Pictorial source: Francesc Comes, <i>Madonna and Child with angel musicians</i>, around 1394, Gold and tempera on wood, Pollença (Mallorca), Museu de Pollença <br>Instrument maker: Thilo Hirsch <br>Year of manufacture: 2022 <br>Location: Basel, property of the ensemble arcimboldo</p><p>Dimensions: <br>Total length: 579 mm <br>Max. Body width: 112 mm <br>Body depth: approx. 95 mm </p><p>Vibrating string lengths: <br>d-string: 500 mm <br>G-string: 497 mm</p><p>Materials: <br>Body: cherry <br>Pegbox: cherry <br>Fingerboard: serviceberry <br>Bars: spruce <br>Nut/String attachment button: bone <br>Bridge: boxwood <br>Top: goatskin</p><p>The main aim of this research was to measure the acoustic effects of the different sound holes. To do this, the instrument was first measured with the upper rosette and the two holes in the body closed, then with both rosettes and the body holes open, and finally only with the body holes closed.</p><p>Acoustical measurements: Alexander Mayer, mdw - University of Music and Performing Arts Vienna, Department of Music Acoustics – Wiener Klangstil (IWK), 25.9.2023</p><p>Force: Impact hammer exciting at the bass side of the bridge <br>ACC: Acceleration measured on the same side, close to the impact point. <br>Average is the average of all measurements (to be used in the analysis).</p><p>Photos of the setup: Thilo Hirsch</p><p>Folder comes_rosette_o_loecher_zu <br>Files: comes_rolz_1 to 6 (Description: upper rosette closed with wooden sheet, body-holes closed)</p><p>Folder comes_rosette_oO_loecher_offen <br>Files: comes_roo_lo_1 to 6 (Description: rosette open , body-holes open)</p><p>Folder comes_rosette_oO_loecher_zu <br>Files: comes_roo_lz_1 to 6 (Description: rosette open , body-holes closed) </p><p>________________________</p><p>How to read VIA-Files:</p><p>Line 1 to 9: Header, Line 8 holds the number of values</p><p>Data is organized as followed: 1st col: Frequency [Hz], 2nd col: Magnitude [as Factor not dB!], 3rd col: Phase [rad], 4th col: Real part [as Factor not dB!], 5th col: Imaginary part [as Factor not dB!]. So only first 3 columns are needed!</p><p>To get dB Value: Amplitude[dB] = 20 log [Magnitude]</p><p>Usually the Magnitude was calculated as response/reference (input) in the frequency domain. As for measuring the mechanical admittance the sensor is most likely an accelerometer (capturing the response in m/s2 of the object of interest) and the reference an impact hammer capturing the input force in Newton. As the mechanical admittance is defined as v/F (speed over force) the acceleration signal has to be integrated. The here captured signals are integrated in the frequency domain, what means the magnitude is divided by the corresponding frequency value in s-1. Values coded like: 3.30750000000000E+1 -> 3.3075 * 10 -> 33.075</p>
Acoustical measurements of a rabab reconstructed after a pictorial source from the 16th century
<p><strong>Acoustical measurements of a rabab reconstructed after a pictorial source from the 16th century</strong></p><p>Instrument: rabab<strong> </strong><br>Pictorial source: Jorge Affonso (attr.), <i>The Adoration of the Shepherds</i>, 1515, oil on wood, Lissabon, Museu Nacional de Arte Antiga <br>Instrument maker: Thilo Hirsch <br>Year of manufacture: 2022 <br>Location: Basel, property of the ensemble arcimboldo</p><p>Dimensions: <br>Total length: 510 mm <br>Max. Body width: 110 mm <br>Body depth: approx. 93 mm</p><p>Vibrating string lengths: <br>d'-string: 351 mm <br>a-string: 350 mm <br>e-string: 348 mm</p><p>Materials: <br>Body: cherry <br>Pegbox: cherry <br>Fingerboard: cerry <br>Bars: spruce <br>Nut/String attachment button: bone <br>Bridge: boxwood <br>Top: goatskin</p><p>The main aim of this research was to measure the acoustic effects of the different sound holes. To do this, the instrument was first measured with the rosette and the two holes in the body open, then with the body holes closed.</p><p>Acoustical measurements: Alexander Mayer, mdw - University of Music and Performing Arts Vienna, Department of Music Acoustics – Wiener Klangstil (IWK), 25.9.2023</p><p>Force: Impact hammer exciting at the bass side of the bridge <br>ACC: Acceleration measured on the same side, close to the impact point. <br>Average is the average of all measurements (to be used in the analysis).</p><p>Photos of the setup: Thilo Hirsch</p><p>Folder modell_affonso <br>Files: affonso_1 to 6 (Description: holes open)</p><p>Folder modell_affonso_loecher_zu <br>Files: affonso_hc_1 to 6 (Description: both holes closed)</p><p>________________________</p><p>How to read VIA-Files: Line 1 to 9: Header, Line 8 holds the number of values</p><p>Data is organized as followed: 1st col: Frequency [Hz], 2nd col: Magnitude [as Factor not dB!], 3rd col: Phase [rad], 4th col: Real part [as Factor not dB!], 5th col: Imaginary part [as Factor not dB!]. So only first 3 columns are needed!</p><p>To get dB Value: Amplitude[dB] = 20 log [Magnitude]</p><p> Usually the Magnitude was calculated as response/reference (input) in the frequency domain. As for measuring the mechanical admittance the sensor is most likely an accelerometer (capturing the response in m/s2 of the object of interest) and the reference an impact hammer capturing the input force in Newton. As the mechanical admittance is defined as v/F (speed over force) the acceleration signal has to be integrated. The here captured signals are integrated in the frequency domain, what means the magnitude is divided by the corresponding frequency value in s-1. Values coded like: 3.30750000000000E+1 -> 3.3075 * 10 -> 33.075</p>
Data set associated to the publication "An active source seismo-acoustic experiment using tethered balloons to validate instrument concepts and modelling tools for atmospheric seismology"
<p>Data set of the scientific publication entitled "An active source seismo-acoustic experiment using tethered balloons to validate instrument concepts and modelling tools for atmospheric seismology":</p> <p>Seismological sensors</p> <p>Microphones</p> <p>Barometers</p> <p>Accelerometers</p> <p>Detailed test report.</p>
ASN Database - v3.2 - Database of Simulated Room Impulse Responses for Acoustic Sensor Networks Deployed in Complex Multi-Source Acoustic Environments
<p>We present a large set of simulated room impulse responses for a multi-room apartment. The simulated apartment models a real vacation apartment for which a recorded set of audio data has already been made available in the context of the DCASE challenges. The impulse responses were rendered using a dense grid of sources and receivers by means of a hybrid auralization algorithm based on a low-order image-source method and deterministic cone tracing. The proposed data set can be used to generate a wide variety of acoustic scenes which, in turn, can benefit numerous data-demanding machine-learning algorithms.<br> <br> To obtain more information on the database, please visit <a href="https://github.com/Jearde/asn-database">the website</a>.<br> <strong>Please read the license file (available in the GitHub repository) before using the database.</strong></p>
Acoustic transfer function data for source and sensor placement
<p>Acoustic transfer function (ATF) data for the codes of source and sensor placement in sound field control. </p> <p>https://github.com/sh01k/SourceSensorPlacementSFC</p> <p>The ATF data in the 2D acoustic field was generated by the finite element method using FreeFem++ (<a href="https://freefem.org/">https://freefem.org/</a>).</p>
soundscape_IR: A source separation toolbox for exploring acoustic diversity in soundscapes
<p>1. Soundscapes contain rich acoustic information associated with animal behaviors, environmental characteristics, and human activities, providing opportunities for predicting biodiversity changes and associated drivers. However, assessing the diversity of animal vocalizations remains challenging due to the interference of environmental and anthropogenic noise. A tool for separating sound sources and delineating changes in acoustic signals is crucial for an effective assessment of acoustic diversity.</p> <p>2. We present soundscape_IR, an open-source Python toolbox dedicated to soundscape information retrieval in which non-negative matrix factorization is applied. This toolbox provides algorithms for supervised and unsupervised source separation (SS). It also enables the use of a snapshot recording for model training and subsequently applying adaptive and semi-supervised SS when target species produce sounds with varying features and when unseen sound sources are encountered.</p> <p>3. Our results demonstrated that SS could enhance the vocalizations of target species, characterize the complexity of vocal repertoires, and investigate the spatio-temporal divergence of soundscapes. In tropical forest soundscapes, the application of SS effectively detected the rutting vocalizations of sika deer and revealed a graded structure in their acoustic characteristics. In subtropical estuarine soundscapes, SS automated the process of identifying distinct biotic and abiotic sounds, and the result uncovered divergent sound compositions between inshore and offshore waters.</p> <p>4. Implementation of SS in soundscape analysis offers a promising method for streamlining the assessment of acoustic diversity in diverse environments. Future application of SS will open new directions to acoustically quantify ecological interactions across individual, species, and ecosystem levels.</p>
VORTEX 01: Analytic solution of the flow, aeroacoustic source and acoustic field from a co-rotating vortex pair
<div>This aeroacoustic reference example (often referred in literature as "Co-rotating vortex pair") provides the analytic solution of the flow, aeroacoustic source and acoustic field. Details on the example and derivation can be found in the article "Analysis of the co-rotating vortex pair as a test case for computational aeroacoustics" on which the analytic functions are based on can be found here: <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.jsv.2024.118496" target="_blank" rel="noopener">10.1016/j.jsv.2024.118496</a>. The following Matlab functions can be used for computing the aero-acoustic radiation of a co-rotating vortex pair:</div> <div> </div> <div><strong>Main script:</strong></div> <div>compute_pfar - Computes the radiated acoustic field. Use the "method" variable to change the aeroacoustic model</div> <div> </div> <div><strong>Computing the source terms:</strong></div> <div>vpair_pic - Incompressible pressure field p_ic and Dp_ic / Dt at a given time instance</div> <div>vpair_u - Velocity field u, and its first and second spatial derivatives at a given time instance</div> <div> </div> <div>plot_source_terms - Create a plot of the source terms of both Lighthill and PCWE</div> <div>plot_quadrupole_field - Plots the analytical quadrupole field solution</div> <div> </div> <div><strong>Utility functions used by the main script:</strong></div> <div>gaussquad - Gauss-Legendre quadrature in 1D (original version from Mathworks FTP)</div> <div>subdiv_graded - Graded subdivision of one or more sections in 1D</div> <div>vpair_quadrature - Quadrature base points and weights for numerical convolution</div> <div> </div> <div><strong>Resource:</strong></div> <div>colormap.png - Blue -> Gray -> Red color scale</div> <div> </div> <div>All functions and scripts, except gaussquad.m were created by the authors:</div> <div>P. Rucz, M. Á. Ulveczki, J. Heinz, S. Schoder</div>
Open‐source workflow approaches to passive acoustic monitoring of bats
<ol> <li>The affordability, storage, and power capacity of compact modern recording hardware has evolved passive acoustic monitoring (PAM) of animals and soundscapes into a non-invasive, cost-effective tool for research and ecological management and is particularly effective for bats and toothed whales that consistently echolocate. The use of PAM at large scales hinges on effective automated detectors and species classifiers which, combined with distance sampling approaches, have enabled species abundance estimation of toothed whales. But standardized, user-friendly, and open-access automated detection and classification workflows are in demand for this key conservation metric to be realized for bats.</li> <li>We used the PAMGuard toolbox including its new deep learning classification module to test the performance of four open-source workflows for automated analyses of acoustic datasets from bats. Each workflow used a different initial detection algorithm followed by the same deep learning classification algorithm and was evaluated against the performance of an expert manual analyst.</li> <li>Workflow performance depended strongly on the signal-to-noise ratio and detection algorithm used: the full deep learning workflow had the best classification accuracy (≤67%) but was computationally too slow for practical large-scale bat PAM. Workflows using PAMGuard's detection module or triggers onboard an SM4BAT or AudioMoth accurately classified up to 47%, 59% and 34%, respectively, of calls to species. Not all workflows included noise sampling critical to estimating changes in detection probability over time, a vital parameter for abundance estimation. The workflow using PAMGuard's detection module was 40 times faster than the full deep learning workflow and missed as few calls (recall for both ~0.6), thus balancing computational speed and performance. </li> <li>We show that complete acoustic detection and classification workflows for bat PAM data can be efficiently automated using open-source software such as PAMGuard and exemplify how detection choices, whether pre- or post-deployment, hardware or software-driven, affect the performance of deep learning classification and <span>the downstream ecological information that can be extracted from acoustic recordings. In particular, understanding, and quantifying detection/classification accuracy and the probability of detection are key to avoid introducing biases that may ultimately affect the quality of data for ecological management. </span> </li> </ol>
Open‐source workflow approaches to passive acoustic monitoring of bats
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soundscape_IR: A source separation toolbox for exploring acoustic diversity in soundscapes
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Source dataset of Figs.3 and S10 of "Higher-order topological semimetal in acoustic crystals"
<p><strong>Fig_3.xlsx</strong> is the source data from the third figure in the main text and <strong>Fig_S10.xlsx </strong>is the source data from the tenth figure in Supplementary Information of our submitted manuscript in Nature Materials (NM20062441C ) entitled <em>Higher-order topological semimetal in acoustic crystals. </em>by Qiang Wei<span>,</span> Xuewei Zhang, Weiyin Deng, Jiuyang Lu, Xueqin Huang, Mou Yan, Gang Chen, Zhengyou Liu, Suotang Jia<span>.</span></p> <p> </p> <p> </p>
Animal acoustic identification, denoising, and source separation using generative adversarial networks
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Binaural auralizations of listening experiment stimuli (acoustic source models / LEV)
<p>Binaural auralizations (two-channel wav files) of loudspeaker experiment stimuli. </p>
Source code and data files for the acoustic sensor-based wearable fetal movement monitor
<p>This repository contains data files and source code for the acoustic sensor-based wearable fetal movement monitor.</p>
Estimating labquake source parameters: spectral inversion from a calibrated acoustic system
<p>Laboratory acoustic emissions (AEs) serve as small-scale analogues to earthquakes, offering fundamental insights into seismic processes. To ensure accurate physical interpretations of AEs, rigorous calibration of the acoustic system is essential. In this paper, we present an empirical calibration technique that quantifies sensor responses, instrumentation effects, and path characteristics into a single entity termed instrument apparatus response.</p> <p>Using a controlled seismic source with different steel balls, we retrieve the instrument apparatus response in the frequency domain under typical experimental conditions for various piezoelectric sensors (PZTs) arranged to simulate a three-component seismic station. Removing these responses from the raw AEs spectra allows us to obtain calibrated AE source spectra which are then effectively used to constrain the AEs seismic source parameters.</p> <p>We apply this calibration method to acoustic emissions (AEs) generated during unstable stick-slip behavior of quartz gouge in double-direct shear experiments. The calibrated AEs range in magnitude from -7.1 to -6.4 and exhibit stress drops between 0.075 MPa and 4.29 MPa, consistent with earthquake scaling relation. </p> <p>This result highlights the strong similarities between AEs generated from frictional gouge experiments and natural earthquakes. Through this acoustic emission calibration we gain physical insights into the seismic sources of laboratory AEs, enhancing our understanding of seismic rupture processes in fault gouge experiments.</p>
Data Corpus for the IEEE-AASP Challenge on Acoustic Source Localization and Tracking (LOCATA)
<p>This repository contains the final release of the development and evaluation datasets for the LOCATA Challenge.</p> <p>The challenge of sound source localization in realistic environments has attracted widespread attention in the Audio and Acoustic Signal Processing (AASP) community in recent years. Source localization approaches in the literature address the estimation of positional information about acoustic sources using a pair of microphones, microphone arrays, or networks with distributed acoustic sensors. The IEEE AASP Challenge on <strong>acoustic source LOCalization And TrAcking (LOCATA)</strong> aimed at providing researchers in source localization and tracking with a framework to objectively benchmark results against competing algorithms using a common, publicly released data corpus that encompasses a range of realistic scenarios in an enclosed acoustic environment.</p> <p>Four different microphone arrays were used for the recordings, namely:</p> <ul> <li>Planar array with 15 channels (DICIT array) containing uniform linear sub-arrays</li> <li>Spherical array with 32 channels (Eigenmike)</li> <li>Pseudo-spherical array with 12-channels (robot head)</li> <li>Hearing aid dummies on a dummy head (2-channel per hearing aid).</li> </ul> <p>An optical tracking system (OptiTrack) was used to record the positions and orientations of talker, loudspeakers and microphone arrays. Moreover, the emitted source signals were recorded to determine voice activity periods in the recorded signals for each source separately. The ground truth values are compared to the estimated values submitted by the participants using several criteria to evaluate the accuracy of the estimated directions of arrival and track-to-source association. </p> <p>The datasets encompass the following six, increasingly challenging, scenarios:</p> <ul> <li><strong>Task 1:</strong> Localization of a single, static loudspeaker using static microphones arrays</li> <li><strong>Task 2:</strong> Multi-source localization of static loudspeakers using static microphone arrays</li> <li><strong>Task 3:</strong> Localization of a single, moving talker using static microphone arrays</li> <li><strong>Task 4:</strong> Localization of multiple, moving talkers using static microphone arrays</li> <li><strong>Task 5:</strong> Localization of a single, moving talker using moving microphone arrays</li> <li><strong>Task 6:</strong> Multi-source localization of moving talkers using moving microphone arrays.</li> </ul> <p>The development and evaluation datasets in this repository contain the following data:</p> <ul> <li>Close-talking speech signals for human talkers, recorded use DPA microphones</li> <li>Distant-talking recordings using four microphone arrays: <ul> <li>Spherical Eigenmike (32 channels)</li> <li>Pseudo-spherical prototype NAO robot (12 channels)</li> <li>Planar DICIT array (15 channels)</li> <li>Hearing aids installed in a head-torso simulator (4 channels)</li> </ul> </li> <li>Ground-truth annotations of all source and microphone positions, obtained using an OptiTrack system of infrared cameras. The ground-truth positions are provided at the frame rate of the optical tracking system</li> </ul> <p>The following software is provided with the data:</p> <ul> <li>Matlab code to read the datasets: <a href="https://github.com/cevers/sap_locata_io">github.com/cevers/sap_locata_io</a></li> <li>Matlab code for performance evaluation of localization and tracking algorithms: <a href="https://github.com/cevers/sap_locata_eval">github.com/cevers/sap_locata_eval</a></li> </ul> <p>For further information, see:</p> <ul> <li>C. Evers, H. W. Löllmann, H. Mellmann, A. Schmidt, H. Barfuss, P. A. Naylor, W. Kellermann<br> <em>"</em>The LOCATA Challenge: Acoustic Source Localization and Tracking<em>," </em>in <em>IEEE/ACM Transactions on Audio, Speech, and Language Processing</em>, vol. 28, pp. 1620-1643, 2020, doi: <a href="https://doi.org/10.1109/TASLP.2020.2990485">10.1109/TASLP.2020.2990485</a></li> <li>Documentation: <a href="https://www.locata.lms.tf.fau.de/files/2020/01/Documentation_LOCATA_final_release_V1.pdf">https://www.locata.lms.tf.fau.de/files/2020/01/Documentation_LOCATA_final_release_V1.pdf</a></li> </ul>
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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.