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1,524 results for “acoustics”
Acoustics, Audibility and Political Culture in the House of Commons, 1800-34
<p>This dataset includes the auralization results obtained from the acoustic models of the House of Commons in 1800-34, as part of the research with the homonymous paper submitted in the special issue "Parliamentary History Journal" (first submission September 2023). </p><p>The auralization results represent the perceived result from the acoustic models for the two discussed scenarios (full-occupied and half-full-occupied House of Commons). We present the results from 3 different speakers at 5 listening positions as shown in the images. </p><p>The anechoic sample is an excerpt of Henry Beaufoy's speech to the House of Commons in 1792 on the subject of the slave trade, performed by John Cooper (co-author) in the anechoic chamber at the Audiolab, University of York. The perceived differences and similarities of the recorded/simulated spaces as heard in these audio files help to further verify the results of the acoustic parameters presented in this paper.</p>
Diverse baleen whale acoustic occurrence around two sub-Antarctic Islands: A tale of residents and visitors
<p>This dataset contains the acoustic .wav file of all exemplar calls illustrated by the spectrograms in the manuscript figure, MS Excel Spreadsheet file with baleen whale call occurrence and environmental data, and the R code used for fitting the RF models. R codes must be run in the following manner:</p> <p>1. 01_tune_occ_enviro_rf_model_balance_baleen_whales</p> <p>2. 02_process_occ_enviro_rf_model_balance_baleen_whales</p> <p>The codes are self-explanatory given the comments contained therein, and the source code for fitting the codes is provided as 000_source_all.</p>
NAH rectangular plate dataset (Nearfield Acoustic Holography)
<p>Dataset of pressure and velocity fields for different isotropic rectangular plates generated with COMSOL Multiphysics software.</p>
Common Phone: A Multilingual Dataset for Robust Acoustic Modelling
<p><em>Release Date: 17.01.22</em></p> <p><strong>Welcome to Common Phone 1.0</strong></p> <p><strong>Legal Information</strong></p> <p><em>Common Phone</em> is a subset of the <em>Common Voice</em> corpus collected by <em>Mozilla Corporation</em>. By using <em>Common Phone</em>, you agree to the <a href="https://commonvoice.mozilla.org/en/terms">Common Voice Legal Terms</a>. <em>Common Phone</em> is maintained and distributed by speech researchers at the <a href="https://lme.tf.fau.de/">Pattern Recognition Lab</a> of Friedrich-Alexander-University Erlangen-Nuremberg (<a href="https://www.fau.de/">FAU</a>) under the <a href="https://creativecommons.org/publicdomain/zero/1.0/">CC0 license</a>.</p> <p>Like for <em>Common Voice</em>, you must not make any attempt to identify speakers that contributed to <em>Common Phone</em>.</p> <p><strong>About <em>Common Phone</em></strong></p> <p>This corpus aims to provide a basis for Machine Learning (ML) researchers and enthusiasts to train and test their models against a wide variety of speakers, hardware/software ecosystems and acoustic conditions to improve generalization and availability of ML in real-world speech applications.<br> The current version of <em>Common Phone</em> comprises 116,5 hours of speech samples, collected from 11.246 speakers in 6 languages:</p> <table align="center"> <thead> <tr> <th> <p><strong>Language</strong></p> </th> <th> <p><strong>Speakers</strong></p> </th> <th> <p><strong>Hours</strong></p> </th> </tr> </thead> <tbody> <tr> <td> </td> <td> <p><code>train</code> / <code>dev</code> / <code>test</code></p> </td> <td> <p><code>train</code> / <code>dev</code> / <code>test</code></p> </td> </tr> <tr> <td> <p>English</p> </td> <td> <p>4716 / 771 / 774</p> </td> <td> <p>14.1 / 2.3 / 2.3</p> </td> </tr> <tr> <td> <p>French</p> </td> <td> <p>796 / 138 / 135</p> </td> <td> <p>13.6 / 2.3 / 2.2</p> </td> </tr> <tr> <td> <p>German</p> </td> <td> <p>1176 / 202 / 206</p> </td> <td> <p>14.5 / 2.5 / 2.6</p> </td> </tr> <tr> <td> <p>Italian</p> </td> <td> <p>1031 / 176 / 178</p> </td> <td> <p>14.6 / 2.5 / 2.5</p> </td> </tr> <tr> <td> <p>Spanish</p> </td> <td> <p>508 / 88 / 91</p> </td> <td> <p>16.5 / 3.0 / 3.1</p> </td> </tr> <tr> <td> <p>Russian</p> </td> <td> <p>190 / 34 / 36</p> </td> <td> <p>12.7 / 2.6 / 2.8</p> </td> </tr> <tr> <td> <p><strong>Total</strong></p> </td> <td> <p>8417 / 1409 / 1420</p> </td> <td> <p>85.8 / 15.2 / 15.5</p> </td> </tr> </tbody> </table> <p> </p> <p>Presented <code>train</code>, <code>dev</code> and <code>test</code> splits are <strong>not identical</strong> to those shipped with <em>Common Voice</em>. Speaker separation among splits was realized by only using those speakers that had provided age and gender information. This information can only be provided as a registered user on the website. When logged in, the session ID of contributed recordings is always linked to your user, thus we could easily link recordings to individual speakers. Keep in mind this would not be possible for unregistered users, as their session ID changes if they decide to contribute more than once.<br> During speaker selection, we considered that some speakers had contributed to more than one of the six <em>Common Voice</em> datasets (one for each language). In <em>Common Phone</em>, a speaker will only appear in one language.<br> The dataset is structured as follows:</p> <ul> <li>Six top-level directories, one for each language.</li> <li>Each language folder contains: <ul> <li>[train|dev|test].csv files listing audio files, respective speaker ID and plain text transcript.</li> <li>meta.csv provides speaker information: age group, gender, language, accent (if available) and which of the three splits this speaker was assigned to. File names match corresponding audio file names except their extension.</li> <li>/grids/ contains phonetic transcription for every audio file in Praat TextGrid format.</li> <li>/mp3/ contains audio files in mp3, identical to those of <em>Common Voice</em>, e.g., sampling rates have been preserved and may vary for different files.</li> <li>/wav/ contains raw audio files in 16 bits/sample, 16 kHz single channel. They had been created from the original mp3 audios. We provide them for convenience, keep in mind that their source had undergone MP3-compression.</li> </ul> </li> </ul> <p><strong>Where does the phonetic annotation come from?</strong></p> <p>Phonetic annotation was computed via <a href="https://clarin.phonetik.uni-muenchen.de/BASWebServices/interface/Pipeline">BAS Web Services</a>. We used the regular Pipeline (G2P-MAUS) without ASR to create an alignment of text transcripts with audio signals. We chose International Phonetic Alphabet (IPA) output symbols as they work well even in a multi-lingual setup. <em>Common Phone</em> annotation comprises 101 phonetic symbols, including silence.</p> <p><strong>Why <em>Common Phone</em>?</strong></p> <ul> <li>Large number of speakers and varying acoustic conditions to improve robustness of ML models</li> <li>Time-aligned IPA phonetic transcription for every audio sample</li> <li>Gender-balanced and age-group-matched (equal number of female/male speakers in every age group)</li> <li>Support for six different languages to leverage multi-lingual approaches</li> <li>Original MP3 files plus standard WAVE files</li> </ul> <p><strong>Is there any publication available?</strong></p> <p><em>Yes, a paper describing Common Phone in detail is currently under revision for LREC </em><em>2022. You can access a pre-print version on arXiv entitled “<a href="https://arxiv.org/abs/2201.05912">Common Phone: A Multilingual Dataset for Robust Acoustic Modelling</a>”.</em></p>
INFORE22 Acoustic data from SLim Towed Array (SLiTA)
<p>Acoustic data stem from hydrophone array SLiTA (SLim Towed Array [*]) towed by marine robots during INFORE22 experiments on 28<sup>th</sup> February and 1<sup>st</sup> March 2022. Two series of acquisitions are shared, one for each OEX.</p> <p>Data have been generated during the INFORE22 sea trial, run by CMRE from 21st February to 1st March 2022 in the Gulf of La Spezia to experiment and validate the CMRE hybrid robotic network in support of the INFORE maritime use case [**]. See also https://zenodo.org/record/637272</p> <p>Acoustic data are shared in binary format (.dat), and are complemented by Matlab scripts (.m) and header textual descriptors (sliva-header-specification.2.x0.txt). </p> <p>The archive (ACOUSTIC.zip) is split in multiple (60) files of 650MB each, which can me joined/combined with archive software (e.g., 7zip). </p> <p>For a full description of the dataset, see [***]</p> <p><strong>Conditions for use and distributions</strong></p> <p>This dataset is provided by NATO STO CMRE within the condition stated in the H2020 INFORE Grant and Consortium Agreement (GA. no. 825070) . The creation of derived products, as well the use in scientific publications must be pre-approved by CMRE and acknowledged. </p> <p><strong>Non liability clause</strong></p> <p>These data and software are provided in the scope of INFORE by NATO STO CMRE, in compliance with the INFORE open data strategy. Data and software are provided as they are. NATO and NATO STO CMRE decline any responsibility for bugs and any damage or accidental issue that the use of those data and software could cause. </p> <p><strong>References</strong></p> <p>[*] Alain Maguer, Rodney Dymond, Piero Guerrini, Luigi Troiano, Vittorio Grandi, Alberto Figoli, Claudio Olivero, Alessandro Sapienza, Stefano Fioravanti, John Potter Receiving and transmitting acoustic systems for AUV/gliders. Proceedings of the 3rd International Conference and Exhibition on Underwater Acoustic Measurements: Technologies and Results, 21-26 June, 2009, Nafplion, Greece. </p> <p>[**] Gabriele Ferri, Raffaele Grasso, Elena Camossi, Francesca de Rosa, Alessandro Faggiani, Kevin LePage, Konstantina Bereta, Marios Vodas, Dimitris Kladis, Antonis Kontaxakis, Nikos Giatrakos, Antonios Deligiannakis, Maritime Use Case: Final Evaluation Report Work Package 3 Tasks 3.3 INFORE Deliverable D3.3</p> <p>[***] Nikos Giatrakos, Antonios Deligiannakis, Arnau Montagud, Miguel Ponce de León, Thaleia Ntiniakou, Holger Arndt, Stefan Burkard, Konstantina Bereta, Marios Vodas, Dimitris Kladis, Raffaele Grasso, Gabriele Ferri, Arjan Vermeij, Alessandro Faggiani, Elena Camossi, Kevin Le Page: Data Management Plan V3 Work Package 8 Task 8.3 INFORE Deliverable 8.6</p> <p> </p> <p> </p> <p> </p>
Dataset of Spatial Room Impulse Responses in a Variable Acoustics Room for Six Degrees-of-Freedom Rendering and Analysis
<p>Room acoustics measurements are used in many areas of audio research, from physical acoustics modelling and speech enhancement to virtual reality applications. This paper documents the technical specifications and choices made in the measurement of a dataset of spatial room impulse responses (SRIRs) in a variable acoustics room. Two spherical microphone arrays are used: the mh Acoustics Eigenmike em32 and the Zylia ZM-1, capable of up to fourth- and third-order Ambisonic capture, respectively. The dataset consists of three source and seven receiver positions, repeated with five configurations of the room's acoustics with varying levels of reverberation. Possible applications of the dataset include six degrees-of-freedom (6DoF) analysis and rendering, SRIR interpolation methods, and spatial dereverberation techniques. </p> <p>Accompanying paper on details of the dataset measurement: https://arxiv.org/abs/2111.11882</p> <p>Changelog:</p> <p>V 1.0 - Initial version.<br> V 1.1 - SOFA files updated to latest Matlab API (1.1.3), 'SingleRoomDRIR' convention, with SourcePosition and ListenerPosition z data corrected. Changed ListenerPosition and SourcePosition x data so that it follows the convention of origin in bottom left corner (rather than the previous bottom right). Fixed the swapped x and y labels in 6dof_source_and_receiver_positions.pdf.</p>
Dataset for Surface waves prediction based on acoustic backscattering
<p>Underwater acoustic measurements dataset is represented by three types of files “.raw", “.mat", ".dat" as follows:<br> * “.raw" format also represented by three types of data.<br> - “...search.raw" files contain complex envelop from all hydrophones calculated at four emitted frequencies<br> - “...chan.raw" files is a signal in a wide band from one of the hydrophones - for control and noise analysis.<br> - the largest files are the raw wideband signal from all hydrophones. One such file was saved per eight-hour sound emission cycle.<br> * Spectrogram files are saved in MATLAB format “.mat” v7 . Phasing of the antenna array (all-round view) and calculation of window spectra near each emitted pulse was carried out.<br> * ".dat" files contain features of the average spectrum of the backscattered signal.<br> * Direct measurements of surface wave characteristics, made by a Datawell DWR-G4 wave-rider buoy, accompanied the acoustic measurements. This data is included too.</p> <p>In this archive, we upload all available files of the "dat" and "mat" type and a limited number of "raw" files. You may unpack all ".tar.gz" files into one folder, preserving the directory tree, existing in the archives.</p> <p>Users should refer to the included “.pdf” file for the data format description and to a published preprint for a description of the experimental conditions and instrumentation characteristics. See [arXiv:arXiv:2204.10153] via <a href="https://arxiv.org/abs/2204.10153">https://arxiv.org/abs/2204.10153</a> (Also check when the link is updated to the journal paper) </p> <p>The authors are grateful to their colleges, who helped during the expedition. Data acquisition would be impossible without their contribution. This research was supported by the Russian Science Foundation, grant number 20-77-10081 (the expedition and motivation for study) and the State Contract with the Ministry of Education and Science of the Russian Federation, grant number 0030-2021-0017 (the instruments for underwater acoustic measurements).</p>
Video Examples from: Creating Audio Object-focused Acoustic Environments for Room-Scale Virtual Reality
<p>Video recordings illustrating the issues and possible solutions mentioned in the paper.</p> <p>Please use headphones when watching the videos.</p>
Nonlinear spectral analysis of ion acoustic solitons arising from a streaming charged object using the numerical inverse scattering transform data
<p>Data files used in the publication: "Nonlinear spectral analysis of ion acoustic solitons arising from a streaming charged object using the numerical inverse scattering transform", submitted to Physics of Plasma August 2022. To be used in conjunction with analysis software KVIST.</p> <p>KVIST can be found at:</p> <ul> <li>https://doi.org/10.5281/zenodo.7017043</li> <li>https://github.com/Planetary-Surfaces-and-Spacecraft-Lab/KVIST</li> </ul> <p>Data files generated with:</p> <p>Truitt, A. (2020). Simulation of Forced Korteweg De Vries Equation as Applied to Small Orbital Debris. Digital Repository at the University of Maryland. https://doi.org/10.13016/FOR0-XJYD</p>
Segmentations of the core of the acoustic radiation in HCP data
<p><strong>Description of the repository</strong>:</p> <p>The goal of our paper (<a href="https://doi.org/10.3389/fneur.2022.934650">https://doi.org/10.3389/fneur.2022.934650</a>) was to segment the acoustic radiation (AR), one of the most important white matter fiber bundles of the hearing system. This repository contains the segmentations masks of the AR we created from 105 subjects of the Human Connectome Project (HCP) young adult dataset (<a href="https://www.humanconnectome.org/study/hcp-young-adult">https://www.humanconnectome.org/study/hcp-young-adult</a>). These subjects are exactly the same used by Wasserthal et al. (2018) <a href="https://doi.org/10.1016/j.neuroimage.2018.07.070">https://doi.org/10.1016/j.neuroimage.2018.07.070</a>.</p> <p>In the file "data_training.tar.gz", one directory was created per HCP subject. Every directory contains the file "bundle_masks_AR.nii.gz" that contains the binary masks for the left and right AR.</p> <p>In our paper, we used these masks to train TractSeg. The file best_weights_ep110.npz contains the weights after training TractSeg that can be used in inference for targeting the AR. For using these weights on new data, one can use TractSeg with the option "--exp_name best_weights_ep110.npz". Please read the documentation of TractSeg and our paper for more information.</p> <p> </p> <p>If you use the training data or the pre-trained network, please cite our publication:</p> <p>Malin Siegbahn, Cecilia Engmér Berglin, Rodrigo Moreno. Automatic segmentation of the core of the acoustic radiation in humans. Frontiers in Neurology (2022) 13:934650. doi: 10.3389/fneur.2022.934650</p>
InsectSet32: Dataset for automatic acoustic identification of insects (Orthoptera and Cicadidae)
<p>This dataset contains recordings of 32 sound producing insect species with a total 335 files and a length of 57 minutes. The dataset was compiled for training neural networks to automatically identify insect species while comparing adaptive, waveform-based frontends to conventional mel-spectrogram frontends for audio feature extraction. This work was <a href="https://doi.org/10.1371/journal.pcbi.1011541">published</a> in PLOS Computational Biology and this dataset can be used to replicate the results, as well as other uses. The scripts for audio processing and the machine learning implementations are published on <a href="https://github.com/mariusfaiss/InsectSet32-Adaptive-Representations-of-Sound-for-Automatic-Insect-Recognition">Github</a>.</p> <p>The recordings are split into two datasets. Roughly half of the recordings (147) are of nine species belonging to the order Orthoptera. These recordings stem from a dataset that was originally compiled by <a href="https://orcid.org/0000-0002-8929-2737">Baudewijn Odé</a> (unpublished). </p> <p>The remaining recordings (188) are of 23 species in the family Cicadidae. These recordings were selected from the Global Cicada Sound Collection hosted on <a href="https://bio.acousti.ca/">Bioacoustica</a> (<a href="https://doi.org/10.1093/database/bav054">doi.org/10.1093/database/bav054</a>), including recordings published in <a href="https://doi.org/10.3897/BDJ.3.e5792">doi.org/10.3897/BDJ.3.e5792</a> & <a href="https://doi.org/10.11646/zootaxa.4340.1">doi.org/10.11646/zootaxa.4340.1</a>. Many recordings from this collection included speech annotations in the beginning of the recordings, therefore the last ten seconds of audio were extracted and used in this dataset. </p> <p>All files were manually inspected and files with strong noise interference or with sounds of multiple species were removed. Between species, the number of files ranges from four to 22 files and the length from 40 seconds to almost nine minutes of audio material for a single species. The files range in length from less than one second to several minutes. All original files were available with sample rates of at least 44.1 kHz or higher but were resampled to 44.1 kHz mono WAV files for consistency. The annotation files contain information for each recording, including the file name, species name and identifier, as well as the data subset they were included in for training the neural network (training, test, validation).</p>
Datasets for the paper "Finite element modelling of the vibro-acoustic response in dielectric elastomer membranes"
<p>This upload contains the numerical datasets used for the numerical plots reported in the paper "Finite element modelling of the vibro-acoustic response in dielectric elastomer membranes" by G. Moretti et al., In <em>Electroactive Polymer Actuators and Devices (EAPAD) XXIV</em> (https://doi.org/10.1117/12.2612784).</p> <p>Please refer to the readme file for information on the files structure and content. </p>
TUT Acoustic scenes 2017, Evaluation & Development datasets, processed image
<p>Unseparated Pulse Energy Spectrogram</p> <p>Processed audio data.</p> <p>Sound source separation is a <strong>preliminary</strong> for <strong>acoustic scene classification</strong>. It can be argued that rare sound detection can be performed without separation, but in most cases it also depends on it.</p> <p>I have come up with the theory that the full <strong>time-domain</strong>, or if assumptions are made on the amplitude-waveform or the phase-profile, even the <strong>sequence</strong> of events can be <strong>discarded</strong> for acoustic scene classification.</p> <p>For short time frame bins, a <strong>statistical representation</strong> should be enough to correctly identify the scene. Even more so, if deep learning methods are applied.</p> <p>I have also come up with the theory that <strong>energy</strong> scalograms are applied <strong>pulse-length</strong> or waveform/profile-length wise. This can enhance the input representation for machine learning.</p> <p>Furthermore I have used derivatives of the time signal and applied similar signal processing methods to them. For visualisation I have added them to the original scalogram in different colors. The use of <strong>derivatives</strong> is very much <strong>distorted</strong>, if the sound is not separated.</p>
Dataset: scattering of acoustic waves by vortices
<p>This dataset contains the data associated with the following paper: V. Clair & G. Gabard, Spectral broadening of acoustics waves by convected vortices, <em>Journal of Fluid Mechanics</em>, 841, pp. 50-80, 2018.</p>
[Data] Qualify-As-You-Go: Sensor Fusion of Optical and Acoustic Signatures with Contrastive Deep Learning for Multi-Material Composition Monitoring in Laser Powder Bed Fusion Process
<p><br>Growing demand for multi-material Laser Powder Bed Fusion (LPBF) faces process control and quality monitoring challenges, particularly in ensuring precise material composition. This study explores optical and acoustic emission signals during LPBF processes with multiple materials, addressing challenges in process control and ensuring accurate material composition. Experimental data from processing five powder compositions were collected using a custombuilt monitoring system in a commercial LPBF machine. The research categorised signals from LPBF processing various compositions, enhancing prediction accuracy by combining optical with acoustic data and training convolutional neural networks using contrastive learning. Latent spaces of trained models using two contrastive loss functions, clustered acoustic and optical<br>emissions based on similarities, aligning with five compositions. Contrastive learning and sensor fusion were found to be essential for monitoring LPBF processes involving multiple materials. This research advances the understanding of multi-material LPBF, highlighting sensor fusion strategies’ potential for improving quality control in additive manufacturing. Data set for this work is hosted here</p>
Supporting Movies from: Seismo-acoustic observations of crashing ocean waves: Investigating surf monitoring at Coal Oil Point Reserve, Santa Barbara, California
<div> <div> <div> <p>This repository includes supplementary movies from the manuscript titled, "Seismo-acoustic observations of crashing ocean waves: Investigating surf monitoring at Coal Oil Point Reserve, Santa Barbara, California," submitted to the Journal of Geophysical Research: Solid Earth.</p> <p> </p> <p>Movies S1 and S2. These two movies taken during array deployment 4 on October 20, 2023 show the NW tip of Coal Oil Point at the left of the field of view and Sands Beach northwest of that toward the right. Frames have the same figure layout as Figure 4 of the main text.</p> <p>Movie S3. Same as Movies S1 and S2 but with the NW tip of Coal Oil Point at the right of the field of view and Devereux Beach southeast of that toward the left.</p> </div> </div> </div>
[Dataset] Simultaneous laser ultrasonic measurement of sound velocities and thickness of plates using combined mode local acoustic spectroscopy
<p>Research data for the purpose of reproducing the results presented in the journal publication titled "Simultaneous laser ultrasonic measurement of sound velocities and thickness of plates using combined mode local acoustic spectroscopy"</p>
METU SPARG Eigenmike em32 Acoustic Impulse Response Dataset v0.1.0
<p><strong>DESCRIPTION</strong></p> <p>This dataset includes acoustic impulse response (AIR) measurements made using an Eigenmike em32 and the room impulse response measurements carried out at the same position using an Alctron M6 measurement microphone. The measurements were made in classroom S05 at the METU Graduate School of Informatics on 23 January 2018. </p> <p><strong>LICENSE</strong></p> <p>The dataset is released under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).</p> <p><strong>MEASUREMENTS</strong></p> <p>The classroom in which the measurements were made has a high reverberation time (T60 ≈ 1.12 s) when empty. The room is approximately rectangular and has the dimensions 6.5 × 8.3 × 2.9 m. AIR measurements were made at 240 points on a rectilinear grid of 0.5 m horizontal and 0.3 m vertical resolution surrounding the array. The array was positioned at a height of 1.5 m. The measurement planes were positioned at the heights of 0.9, 1.2, 1.5, 1.8 and 2.1 m from the floor level. These positions cover the whole azimuth range and an elevation range of approximately ±50◦ above and below the horizontal plane.</p> <p>The sound source was a Genelec 6010A two-way loudspeaker whose acoustic axis pointed at the vertical axis of the array at all measurement positions. Logarithmic sine sweep method was used for the AIR measurements. </p> <p><strong>FILE FORMAT</strong></p> <p>The AIRs and RIRs are provided as 16-bit signed integer WAVE files. The sampling rate is 48 kHz.</p> <p><strong>NAMING CONVENTION</strong></p> <p>There are two folders: em32 and alctron. The former includes AIR measurements, and the latter includes the RIR measurements. Each of these folders include 244 subfolders where each subfolder is named as ABC, from 000 through to 664. See documentation.pdf for details.</p> <p>Note that the measurements right above and below the array are not ideal since the acoustic axis of the loudspeaker did not face the array. Therefore, these measurements are considered unfit and were not used in the publications given below.</p> <p><strong>HOW TO CITE</strong></p> <p>The dataset has the DOI number 10.5281/zenodo.2635758 and can be cited as:</p> <p><strong>Orhun Olgun, & Huseyin Hacihabiboglu. (2019). METU SPARG Eigenmike em32 Acoustic Impulse Response Dataset v0.1.0 (Version 0.1.0) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.2635758</strong></p> <p>The data presented here were used in one journal article and two conference papers as of the time of writing this document. Please also consider citing these papers if you find this dataset to be useful in your research:</p> <p>[1] Coteli, M. B., Olgun, O., and Hacihabiboglu, H. (2018). Multiple Sound Source Localization With Steered Response Power Density and Hierarchical Grid Refinement. IEEE/ACM Trans. Audio, Speech and Language Process., 26(11), 2215-2229.</p> <p>[2] Olgun, O. and Hacihabiboglu, H., (2018) "Localization of Multiple Sources in the Spherical Harmonic Domain with Hierarchical Grid Refinement and EB-MUSIC". In Proc. 2018 16th Int. Workshop on Acoust. Signal Enhancement (IWAENC-18) (pp. 101-105), Tokyo, Japan.</p> <p>[3] Coteli, M. B., and Hacihabiboglu, H., (2019), "Multiple Sound Source Localization with Rigid Spherical Microphone Arrays Via Residual Energy Test", Proc. 2019 IEEE Int. Conf. on Acoust., Speech and Signal Process., (ICASSP-19), Brighton, UK.</p>
Atmospheric, hydrodynamic and water quality observations from environmental-quality stations, water level sensors, acoustic Doppler velocimeters, and meteorological stations located at the Guadalquivir river estuary (2008 - 2010)
<p>The dataset included in this repository was obtained during the project entitled “Propuesta metodológica para diagnósticar las consecuencias de las actuaciones humanas en el estuario del Guadalquivir” funded by the Autoridad Portuaria de Sevilla (APS), by the Consejería de Innovación, Ciencia y Empresa (Junta de Andalucía), CTM2011-22580, MedEX (CTM2008-04036-E) and PR11-RNM-7722. The data were collected in real time from 2008 until 2010 with a remote monitoring system installed by the Institute of Marine Sciences of Andalusia (ICMAN-CSIC) (Navarro et al., 2011).</p> <p> </p> <p>The environmental quality station recorded turbidity, temperature, conductivity, normalized turbidity, dissolved oxygen, oxygen, oxygen saturation, percentage of oxygen saturation, fluorescence, normalized fluorescence, and salinity every thirty minutes. Current data were measured every 15 minutes by means of acoustic current profilers. The former datasets were obtained at several depths and different locations along the Guadalquivir estuary. Water level sensors recorded the position of the free water surface every 10 minutes at several locations along the Guadalquivir estuary. Wind velocity and direction and solar radiation were measured every 10 minutes in a meteorological station at the mouth of the Guadalquivir estuary.</p> <p>Brief description of dataset.</p> <ul> <li> <p>velocities.csv (in m/s)</p> </li> <li> <p>Turbidity.csv (in Volts), temperature (in Celsius), conductivity (in Siemens/m), normalized turbidity (in FNU), dissolved oxygen (mg/L), oxygen (in Volts), fluorescence (in Volts), normalized fluorescence (in Volts), oxygen saturation (mg/L), percentage of oxygen saturation (%), salinity (in PSU).</p> </li> <li> <p>qual_Salmedina.csv, R_mean (mean radiative flux in W/m²), R_max (max radiative flux in W/m²), Rel_humidity (relative humidity in %), D_mean (wind mean direction in degrees), D_max (wind maximum direction in degrees), D_sig (standard deviation of the wind direction in degrees), V_mean (mean wind velocity in m/s), V_max (maximum wind velocity in m/s), V_sig (standard deviation of the wind velocity in m/s), P_atm_mean (mean atmospheric pressure in mbar), T_mean (mean air temperature in Celsius), T_max (maximum air temperature in Celsius), T_sig (standard deviation of the air temperature in Celsius).</p> </li> <li> <p>Sealevel.csv (in meters)</p> </li> </ul> <p>A wide description of the datasets can be found in Navarro et al (2011).</p> <p>Contact person: infogdfa@ugr.es (or mcobosb@ugr.es)</p>
Acoustic Guitar Timbre Thematic Analysis
<p>A perceptual study was conducted to investigate listener perceptions of acoustic guitar timbre, encompassing descriptive and preference analysis, and the impact of guitar playing style on perceived timbre similarity and preference.</p> <p>The listening test was based on recordings of ten different steel-string acoustic guitars at various price points, sourced from the online music retailer Thomann (https://www.thomann.de/). For each guitar, recordings of three different songs were used, each with a different playing style: picking (mainly individual notes played with a combination of fingers and pick), strumming (mainly chords played with a pick), and fingerstyle (strings plucked with fingers rather than a pick).</p> <p>The study was completed by 27 participants (8 female, 19 male, mean age: 27) of 14 different nationalities. Participants had an advanced musical proficiency (Goldsmiths Musical Sophistication Index General Sophistication score of 97.85) and 11 of them played guitar as their primary instrument. Participants listened to each guitar in each of the three playing styles and were asked to describe the instrument's timbre, what they liked and what they disliked.</p> <p>We conducted a thematic analysis of the participant answers to the three questions (timbre description, timbre like, timbre dislike) for the ten guitars using a combination of deductive and inductive approaches. This dataset provides the analysis codebook, theme and code statistics.</p> <p>Details about the thematic analysis and the study can be found in the accompanying publication currently under revision (the reference will be added in due course).</p>
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