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1,524 results for “Acoustics”
The Acoustic Environment of York Minster's Chapter House
<p>This repository contains the data set related to the paper “The Acoustic Environment of York Minster's Chapter House”, published in "Acoustics" as part of the "Special Issue Historical Acoustics: Relationships between People and Sound over Time" and available at: DOI: 10.3390/acoustics2010003</p> <p>This dataset contains the B-format Room Impulse Responses (RIR) in the Waveform Audio File standard Format (.wav) measured and simulated at a selected set of source-receiver combinations in the York Minster's Chapter House, used for the acoustical analysis performed as part of the CATHEDRAL ACOUSTICS project (CA-MRIR-YM-CH and CA-SRIR-YM-CH folders respectively). The .xls spreadsheets (CA-YM-CH-MRIR-ResultData-EnergyParameters and CA-YM-CH-SRIR-ResultData-EnergyParameters) include the results derived for the measured and simulated RIR used for the discussion presented in the manuscript.</p> <p>LICENCE.txt, METADATA.txt and README.txt contain a brief description of the folder contents, authors, other useful information.</p> <p>Details on the acoustic measurement campaigns and simulations can be found in the manuscript.</p> <p>Please cite both the paper and dataset if used.</p> <p>--------------------------------------------------</p> <p>This work is license under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0). (see https://creativecommons.org/licenses/by-nc-sa/4.0/)</p> <p>--------------------------------------------------</p> <p>Dataset curated by Lidia Álvarez-Morales, Theatre, Film, Television and Interactive Media Department, University of York.<br> Contact: lidia.alvarezmorales@york.ac.uk; lidiaalvarezmorales@gmail.com</p> <p>-------------------------------------------------</p> <p>Funding was provided by the European Union’s Horizon 2020 research and innovation programme (http://dx.doi.org/10.13039/501100007601) under the Marie Sklodowska-Curie grant agreement No 797586</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>
Vocal drum sounds in Human Beatboxing: an acoustic and articulatory exploration using electromagnetic articulography
<p>This dataset constitutes the supplementary material of a paper in review in the Journal of the Acoustical Society of America (JASA)</p>
WiggleZ Dark Energy Survey Baryon Acoustic Oscillation Random Catalogues
<p>Data products associated with the WiggleZ Dark Energy survey measurement of the Baryon Acoustic Oscillations (BAO), as described in Blake et al. arXiv:1108.2635 (2011, MNRAS, 418, 1707)</p> <p>Data is given for 6 WiggleZ regions (01, 03, 09, 11, 15, 22 hrs) in 3 different overlapping redshift slices (z=0.2-0.6, 0.4-0.8, 0.6-1.0), matching the datasets analyzed in the final WiggleZ baryon acoustic peak paper Blake et al. (2011, MNRAS, 418, 1707). For each sub-region the following files are given:</p> <p><strong>Correlation function:</strong> xi_**hr*.dat - correlation function measurement and covariance matrix for each sub-region. Format of the file:</p> <ul> <li>1st line: nbin, ngalaxy</li> <li>nbin lines: mean separation [Mpc/h], xi(s), sqrt[Cov(i,i)]</li> <li>nbin x nbin lines: i, j, Cov(i,j)</li> </ul> <p>The covariance matrix is from lognormal realizations, not jack-knife regions.</p> <p><strong>Combined correlation function:</strong> xi_combined*.dat - correlation function measurement for each redshift range combining the measurements in each sub-region, in the same format as above.</p> <p><strong>Integral constant:</strong> wigglez_ic.dat - integral constraint correction which should be added to the measured correlation function, format of the file is:</p> <ul> <li>Region [hr],</li> <li>zmin,</li> <li>zmax,</li> <li>integral constraint delta-xi</li> </ul> <p>We are considering adding another 90 random catalogues in the near future. For now the random catalogues used in the previous analysis are available below.</p>
Dataset from Annual Acoustic Presence of Fin Whale (Balaenoptera physalus) Offshore Eastern Sicily, Central Mediterranean Sea
<p>This dataset is form the study: </p> <p>Sciacca V., Caruso F.,Beranzoli L., Chierici F., De Domenico E., Embriaco D., Favali P., Giovanetti G., Larosa G., Marinaro G., Papale E., Pavan G., Pellegrino C., Pulvirenti S., Simeone F., Viola S., and G. Riccobene. "Annual Acoustic Presence of Fin Whale (<em>Balaenoptera physalus</em>) Offshore Eastern Sicily, Central Mediterranean Sea." PLoS ONE 10(11): e0141838. doi:10.1371/journal.pone.0141838</p> <p>The archives labeled YYYYMM_Spectrograms.zip contain the data from each month of passive acoustic recording -MM- of the years -YYYY- 2012 and 2013. Data consist of the spectrograms (1-50 Hz) of 10-min audio recordings, in PNG format files. These data were used in the cited study to verify the presence of fin whale calls.</p> <p>The archive labeled "NoiseData.zip" consists of two matrix (ASCII format) containing the recorded values of acoustic noise within the fin whale call frequency band.</p> <p> </p> <p> </p>
Shared Acoustic Codes Underlie Emotional Communication in Music and Speech - Evidence from Deep Transfer Learning (Datasets)
<p>This repository contains the datasets used in the article "Shared Acoustic Codes Underlie Emotional Communication in Music and Speech - Evidence from Deep Transfer Learning" (Coutinho & Schuller, 2017). </p> <p>In that article four different data sets were used: SEMAINE, RECOLA, ME14 and MP (acronyms and datasets described below). The SEMAINE (speech) and ME14 (music) corpora were used for the unsupervised training of the Denoising Auto-encoders (domain adaptation stage) - only the audio features extracted from the audio files in these corpora were used and are provided in this repository. The RECOLA (speech) and MP (music) corpora were used for the supervised training phase - both the audio features extracted from the audio files and the Arousal and Valence annotations were used. In this repository, we provide the audio features extracted from the audio files for both corpora, and Arousal and Valence annotations for some of the music datasets (those that the author of this repository is the data curator).</p> <p>Below, you can find description of the various corpora, the details about the data stored in this repository and information on how to obtain the rest of the data used by Coutinho and Schuller (2017).</p> <p><strong>SEMAINE (speech)</strong></p> <p>The SEMAINE corpus (McKeown, Valstar, Cowie, Pantic & Schroder, 2012) was developed specifically to address the task of achieving emotion-rich interactions, and it is adequate for this task as it comprises a wide range of emotional speech. It includes video and speech recordings of spontaneous interactions between human and emotionally stereotyped `characters'. Coutinho & Schuller (2017) used a subset of this database (called <em>Solid-SAL</em>). The <em>Solid-SAL</em> dataset is freely available for scientific research purposes (see http://semaine-db.eu). This repository includes the audio features used in Coutinho & Schuller (2017) (under features/SEMAINE).</p> <p><strong>RECOLA (speech)</strong></p> <p>The RECOLA database (Ringeval, Sonderegger, Sauer & Lalanne, 2013) consists of multimodal recordings (audio, video, and peripheral physiological activity) of spontaneous dyadic interactions between French adults. Coutinho & Schuller (2017) used the RECOLA-Audio module which consists of the audio recordings of each participant in the dyadic phase of the task. In particular, they used the non-segmented high-quality audio signals (WAV format, 44.1kHz, 16bits), obtained through unidirectional headset microphones, of the first five minutes of each interaction. Annotations consist of time-continuous ratings of the level of Arousal and Valence dimensions of emotion perceived by each rater while seeing and listening the audio-visual recordings of each participant task. The publicly available annotated dataset includes only part of the data which amounts to a total number of 23 instances. The time frame length used by Coutinho & Schuller (2017) is 1s (the original annotations were downsampled). This repository includes the audio features used in Coutinho & Schuller (2017) (under features/RECOLA). To obtain the annotations you should contact the author of the original study (see https://diuf.unifr.ch/diva/recola/download.html for further details).</p> <p><strong>ME14 (music)</strong></p> <p>The MediaEval ``Emotion in Music'' task is dedicated to the estimation of Arousal and Valence scores continuously in time and value for song excerpts from the Free Music Archive. Coutinho and Schuller (2017) used the whole corpus (development and test sets for the 2014 challenge) which includes 1,744 songs belonging to 11 musical styles -- Soul, Blues, Electronic, Rock, Classical, Hip-Hop, International, Folk, Jazz, Country, and Pop (maximum of five songs per artist). This repository includes the audio features used in Coutinho & Schuller (2017) (under features/ME14). The full dataset (including annotations) can be obtained from http://www.multimediaeval.org/mediaeval2014/emotion2014/.</p> <p><strong>MP (music)</strong></p> <p>This is a corpus compiled specifically for this work described in Coutinho & Schuller (2017) using data collected in four previous studies. It consists of emotionally diverse full music pieces from a variety of musical styles (Classical and contemporary Western Art, Baroque, Bossa Nova, Rock, Pop, Heavy Metal, and Film Music). Annotations were obtained in controlled laboratory experiments whereby the emotional character of each piece was evaluated time-continuously in terms of levels of Arousal and Valence perceived by listeners (ranging between 35 to 52 in the four studies). In what follows, some details about the various studies are described.</p> <ul> <li>MP<sub>DB1</sub>: This subset of the MP corpus consists of the data reported by Korhonen (2004), and gently made available by the author. This dataset includes six full (or long excerpts) music pieces ranging from 151s to 315s in length (only classical music). Each piece was annotated by 35 participants (14 females). The time series correspondents to each music piece were collected at 1Hz. The golden standard for each piece was computed by averaging the individual time series across all raters. This repository includes the audio features used in Coutinho & Schuller (2017) (under features/MP/DB1). To obtain the labels please contact the author of the original study.</li> <li>MP<sub>DB2</sub>: The dataset by Coutinho & Cangelosi (2011) includes 9 full pieces (43s to 240s long) of classical music (romantic repertoire) annotated by 39 subjects (19 females). Values were recorded every time the mouse was moved with a precision of 1 ms. The resultant timeseries were then resampled (moving average) to a synchronous rate of 1 Hz. The golden standard for each piece was computed by averaging the individual time series across all raters. This repository includes the audio features (under features/MP/DB2) and labels (under annotations/MP/DB2) used in Coutinho & Schuller (2017).</li> <li>MP<sub>DB3</sub>: This dataset was collected by Coutinho & Dibben (2012) and it consists of 8 pieces of film music (84s to 130s long) taken from the late 20th century Hollywood film repertoire. Emotion ratings were given by 52 participants (26 females). The annotation procedure, data processing, and golden standard calculations were identical to MP<sub>DB2</sub>. This repository includes the audio features (under features/MP/DB3) and labels (under annotations/MP/DB3) used in Coutinho & Schuller (2017).</li> <li>MP<sub>DB4</sub>: This dataset was collected by Grewe, Nagel, Kopiez and Altenmüller (2007), and gently made available by the authors. It includes seven music pieces (127s to 502s in length) of heterogeneous styles (e.g., Rock, Pop, Heavy Metal, Classical). Each music piece was annotated by 38 participants (29 females) using an identical methodology to MP<sub>DB2</sub> and MP<sub>DB3</sub>. Data processing and golden standard calculations were also identical. This repository includes the audio features (under features/MP/DB4) used in Coutinho & Schuller (2017). To obtain the labels contact the authors of the original study</li> </ul> <p> </p> <p><strong>Bibliography</strong></p> <p>Coutinho, E., & Cangelosi, A. (2011). Musical emotions: predicting second-by-second subjective feelings of emotion from low-level psychoacoustic features and physiological measurements. <em>Emotion</em>, <em>11</em>(4), 921.</p> <p>Coutinho, E., & Dibben, N. (2013). Psychoacoustic cues to emotion in speech prosody and music. <em>Cognition & Emotion</em>, <em>27</em>(4), 658-684.</p> <p>Coutinho E, Schuller B (2017) Shared acoustic codes underlie emotional communication in music and speech—Evidence from deep transfer learning. PLoS ONE 12(6): e0179289. https://doi. org/10.1371/journal.pone.0179289.</p> <p>Grewe, O., Nagel, F., Kopiez, R., Altenmüller, E. (2007). Emotions over time: synchronicity and development of subjective, physiological, and facial affective reactions to music. <em>Emotion, 7</em>(4), pp. 774-788. DOI: 10.1037/1528-3542.7.4.774.</p> <p>Korhonen, M. (2004). Modeling Continuous Emotional Appraisals of Music Using System Identification. Available from: http://hdl.handle.net/10012/879.</p> <p>McKeown, G., Valstar, M., Cowie, R., Pantic, M., Schroder, M. (2012). The SEMAINE Database: Annotated Multimodal Records of Emotionally Colored Conversations between a Person and a Limited Agent. <em>IEEE Transactions on Affective Computing</em>, 3, pp. 5-17. DOI: http://doi.ieeecomputersociety.org/10.1109/T-AFFC.2011.20.</p> <p>Ringeval, F., Sonderegger, A., Sauer, J. & Lalanne, D. (2013). Introducing the RECOLA Multimodal Corpus of Remote Collaborative and Affective Interactions. In <em>Proceedings of the 2nd International Workshop on Emotion Representation, Analysis and Synthesis in Continuous Time and Space (EmoSPACE 2013)</em>, Shanghai, China. IEEE</p>
Data and code used in analyses for Simulated soundscapes and transfer learning boost the performance of acoustic classifiers under data scarcity
<p>Evaluation datasets, Python scripts, and computation environments used to conduct analyses for Simulated soundscapes and transfer learning boost the performance of acoustic classifiers under data scarcity. <br><br>transfer_learning_project.zip also contains a vignette describing the use of a generalized script for adapting these methods to novel acoustic classification tasks. </p> <p> </p>
NBM: an Open Dataset for the Acoustic Monitoring of Nocturnal Migratory Birds in Europe
<p>This is the dataset presented in the paper "NBM: an Open Dataset for the Acoustic Monitoring of Nocturnal Migratory Birds in Europe", which can be found here: <a href="https://arxiv.org/pdf/2412.03633">https://arxiv.org/pdf/2412.03633</a>.</p> <p> </p> <p><strong>Update 2025, June 13th</strong></p> <p>- A <strong>metadata file</strong> can now be found alongside the dataset ("metadata.csv"): it contains the recording date for original NBM files whenever available, and date and location for all but two of the files originating from Xeno-Canto. All audio samples in the train_nbm_orig folder were recorded in France, but no precise location is available for this collection.</p> <p>- Irregularities due to unconstrained text input in several annotation files have been fixed; in particular all annotated parasitic and background noise are now gathered in two classes: <strong>0: Background </strong>and<strong> 0: Other biophonia</strong>. Unidentified bird vocalizations fall under the <strong>Other </strong>category.</p> <p> </p> <p><strong>File description</strong></p> <p>Files come in three directories: train_nbm_orig, train_nbm_xc and test.</p> <p>Each is composed of a list of .wav files with its .txt annotations file. All bird vocalizations are linked to a <strong>species </strong>and are annotated both in <strong>time </strong>and <strong>frequency</strong>.</p> <p>The train dataset is composed of a total of 2,077 audio files and 13,359 annotations, for a total of ~38 hours of recording. Among these, 883 files were downloaded from Xeno-canto and finely hand-annotated.</p> <p> </p> <p><strong>Citation </strong>(Bibtex)</p> <pre>@article{airale2024nbm, title={NBM: an Open Dataset for the Acoustic Monitoring of Nocturnal Migratory Birds in Europe}, author={Airale, Louis and Pajot, Adrien and Linossier, Juliette}, journal={arXiv preprint arXiv:2412.03633}, year={2024} }<br><br></pre> <p><strong>License</strong></p> <p>This dataset is distributed under a non-commercial, non-derivative license (CC BY-NC-ND 3.0).</p> <p> </p> <p><strong>Acknowledgments</strong></p> <p><span lang="EN-US">Special thanks to all contributors to the initial NBM database: Adrien Pajot, Aymeric Mousseau, Christophe Mercier, Frédéric Cazaban, Gaëtan Mineau, Ghislain Riou, Guillaume Bigayon, Hervé Renaudineau, Kévin Leveque, Lionel Manceau, Mathurin Aubry, Maxence Pajot, Nidal Issa, Willy Raitière, and equal thanks to all anonymous further contributors.</span></p> <p><span lang="EN-US">Acknowledgments also go to all contributors of the Xeno-Canto database whose recordings where used to build this dataset:</span></p> <p><span lang="EN-US">Joost van Bruggen, Ricardo Hevia, Paul Kelly, Franck Hollander, Frédéric Cazaban, Ireneusz Oleksik, Irish Wildlife Sounds, Stanislas Wroza, Cedric Mroczko, Will Scott, Sergi, Chris Batty, Martin Billard, Juha Saari, Julien Bottinelli, Mark Pearson, Uku Paal, Christophe Mercier, Thierry THOMAS, Samuel Levy, Paul Coiffard, Lluís Brotons, Jorge Leitão, Alan Dalton, David Tattersley, Xavier Riera, Lars Mogensen, Feliu López i Gelats, José Manuel Reyes Páez, Jon Sparshott, Sergi Carreras, Marta Celej, julien Rochefort, Chèvremont Fabian, MILLON Xavier, Ed Stubbs, Niels Van Doninck, Jarek Matusiak, Anthony ROUX, Dan Lombard, C PAL, Ray Tsu </span><span lang="EN-US">诸仁</span><span lang="EN-US">, Martijn Verdoes, Pierrick Devoucoux, Manceau Lionel, Lionel Manceau, Vandousselaere Patrick, Volker Arnold, Lars Edenius, Krzysztof Deoniziak, Albert Noorlander, Robert Ekman, Oriol Baena, Mr Mark Shorten, Beatrix Saadi-Varchmin, Simon Kiesé, Robert Manzano, Martin Fousert, Simon Gillings, Lisette, Marco Dragonetti, Cédric PEIGNOT, Adrien CHARBONNEAU, Gosse Hoekstra, Christian Kerihuel, Koen Lepla, Daniele Baroni, Peter van Vlaardingen, Albert Subirà, Alain Malengreau, Julien Piette, Calum Mckellar, Mikael Litsgård, Martin Grienenberger, Sven Kransel, Oliwier Myka, Susanne Kuijpers, Pierre Foulquier, Paolo Zucca, Maties, Geoffrey Monchaux, Jean COURTIN, Pere Josa, Stein Ø. Nilsen, florent yvert, John Sirrett, Toby Carter, Grzegorz Lorek, Tom Jordan, Miguel Tirado, Helder Cardoso, Ignaas Robbe, James P, Corentin Rivière, Romuald Mikusek, David Santamaría Urbano, Diego Fernandez Martinez, Tanguy Loïs, Camille Vacher, Frank Pierik, Testaert Dominique, Piotr Szczypinski, Rafael Costas, Patrick Franke, Ad Hilders, J. Veeken, Thijs Calu, David Pennington, Nic Hallam, Albert Lastukhin, Dominique Guillerme, W. Agster, Nittert van de Water, Marcos Prada Arias, Francesco Barberini, Enrico Hübner, Luca Forneris, Graham Clarke, Mike Douglas, Johan Willner, Sébastien Arriubergé, Rafał Szczerbik, Alessandro Pavesi, Jarred Johnson, Tomasz Wałachowski, Cédric JOUVE, Tom Gheskiere, jesus carrion, David Darrell-Lambert, Graham Sparshott, Théo Hervé, Martin Sutherland, Quentin Giraud, Kévin Giraudin, LE ROY Renaud, August Thomasson, Marcin Sołowiej, Filippo Ceccolini, BirdingAlbufera, Armin Kreusel, Robert Thorpe, Sannier, Oscar Vilches, Paweł Szymański, Jerome Fischer, Matt Slaymaker, Gil Lissens, Sidney M, Matthias Feuersenger, JC Paniagua, Anita Rakitić, david m, Maxime Pirio, brickegickel, Mark Newsome, Sophie REVERDIAU, Oriol, Michaël Bridoux, Sonothèque ADVL, Kieran Nixon, Jochem verweij, Sjouke Scholten, Michael Brunhøj Hansen, Peter Stronach, Pere Josa Anguera, Esperanza Poveda, Johannes Dag Mayer, Pablo Valverde, Friedemann Arndt, Tristan Guillebot de Nerville, Adam Cross, Richard Drew, Cindie Arlaud, Petr Večeřa, Domagoj Tomičić, Guillaume Petitjean, riou, Peter Mattsson, Michał Jezierski, SCECB NATURA-Z, Karol Łanocha, Twan Mols, Klaus Fink, Dawid Jablonski, Arnold Meijer, Paul Ehlers, José Carlos Sires, manuel Grosselet, Birding The Strait, Charlie Bodin, Juan Carlos Paniagua, Nabholz Benoit, Fergus Crystal, Agris Celmins, Albert Cama, Iván Vega, John Muddeman, Adrien Mauss, Gerard Troost, Serge Hoste, Stefano Miceli, Joan Balfagon, Ace, Frank A. Roos, Tom Wulf, Vincent Palomares, Marcel Tenhaeff, Gabriel Hasan, James Lidster, Frank van de Weijer, Herman van Oosten, Alain Verneau, Herman van der Meer, Romain SPELLER, Johan Lorentzon, Thomas ARMAND, Chris Beach, Jacob Spinks, András Schmidt, Jonathan van Erkel, James Spencer, Rowan Wakefield, LEPAGE Frédéric, Itziar Gutiérrez, Severin Racky, NEVILLE MADON, B Whyte, Bodhuin Maxime, Benoit Paepegaey, Ruysschaert, Soulier Pierrick, Frederik Fluyt, Thomas Roux, Eduardo Realinho, Simon Elliott, Oscar Vilches Mendoza, Juan Pita-Romero Caamaño, Gary Elton, Marc Hughes, Raul Pascual, Nicolas Selosse, Arnaud Hedel, Gaëtan Mineau, Peter Boesman, Steve Flynn, Gerry p oneill, Andy Hultberg, Helmut Schaffer, Luca Giussani, edouard dansette, Gavia Stellata, Robert Schouw, Sławomir Karpicki-Ignatowski, mvallespirc, Mark Plummer, Chacron, Seynaeve Adriaan, Kristian Whittaker, Andreas Pettersson, Rafał Kurowski, Delpit, Anja van Halbeek, Lars Burnus, Benjamin Schedl, Sreekumar Chirukandoth, Mathias Götz, Mikhail Velikanov, Paul Bourdin, Török Tamás, Mehmet Ali Demiral, Manuel Grosselet, Olivier Swift, Yoann Blanchon, Jacob Bosma, Alexis Bukowski, Götz Ellwanger, Guillaume Bigayon, Charlie BODIN, Olivier SWIFT, Manuel Grosselet, Tero Linjama, Ad Postma, Eetu Paljakka, Jan Hein van Steenis, Alwin van Lubeck, Jacobo Ramil MIllarengo, Andrew Cobley, guus van duin, Meinolf Ottensmann, Steve Thorpe, Antoine Salmon, Alain Beuget, Simon Busuttil, Bertrand Dallet, Bill Haines, Robbin van Dijk, Jacopo Barchiesi, Johan Jordaans, Simon BAUDOUIN, Nelson Conceição, Teet Sirotkin, Dean McDonnell, Jocce Ekstrom, Michael John O Mahony, Fred Prak, Joachim Pintens, Christophe Legrand, Daniel Beuker, G Berger, Steve Blain, Boris Delahaie, Will Langdon, David Melichar, Emmanuel Roy, Jonas Brüggeshemke, YvesDS, FRIEDRICH Richard</span></p>
Acoustic Keystroke Leakage on Smart Televisions (Accompanying Artifact)
<p>Smart Televisions (TVs) are internet-connected TVs that support video streaming applications and web browsers. Users enter information into Smart TVs through on-screen virtual keyboards. These keyboards require users to navigate between keys with directional commands from a remote controller. Given the extensive functionality of Smart TVs, users type sensitive information (e.g., passwords) into these devices, making keystroke privacy necessary. This work develops and demonstrates a new side-channel attack that exposes keystrokes from the audio of two popular Smart TVs: Apple and Samsung. This side-channel attack exploits how Smart TVs make different sounds when selecting a key, moving the cursor, and deleting a character. These properties allow an attacker to extract the number of cursor movements between selections from the TV's audio. Our attack uses this extracted information to identify the likeliest typed strings. Against realistic users, the attack finds up to 33.33% of credit card details and 60.19% of common passwords within 100 guesses. This vulnerability has been acknowledged by Samsung and highlights how Smart TVs must better protect sensitive data.</p>
AG-PT-set: Acoustic Guitar Playing Technique dataset
<p>This is the Acoustic Guitar Playing Technique dataset (AG-PT-set).<br>It contains <strong>15 hours and 55 minutes</strong> of monophonic recordings of <strong>12</strong> expressive guitar playing techniques (pitched and percussive). <br>Of these, <strong>10 hours and 4 minutes</strong> of recordings encompassing 8 of the 12 techniques have been labeled, meaning that onsets were identified at the millisecond level and their timestamp was saved, alongside playing technique information.<br>As a result, <strong>32,592</strong> individual notes have been labeled.<br>Recordings were performed by <strong>6 players</strong> on <strong>7</strong> different acoustic steel-string <strong>guitars</strong>.</p> <p>The techniques are the following:</p> <ol> <li><strong>Kick technique</strong> (<em>*percussive*</em>): producing a sound that resembles a kick drum by hitting the lower right part of the top of the guitar body;</li> <li><strong>Snare-A technique </strong>(<em>*percussive*</em>): producing a sound by hitting the lower right side of the guitar body;</li> <li><strong>Tom technique</strong> (<em>*percussive*</em>): producing a sound by hitting the area of the guitar body near the top of the end of the fretboard, using the thumb;</li> <li><strong>Snare-B technique</strong> (<em>*percussive*</em>): producing a sound by hitting the muted strings over the end of the fretboard;<br> </li> <li><strong>Natural Harmonics</strong> (pitched): plucking the strings while lightly touching the string with the fretting finger (i.e., not pressing the string fully), therefore letting only some harmonic overtones ring;</li> <li><strong>Palm Mute</strong>: partially muting the strings with the palm of the picking hand, resulting in a muffled sound.</li> <li><strong>Pick Near Bridge</strong> (pitched): plucking the string near the guitar bridge, producing sounds with great high-frequency content;</li> <li><strong>Pick Over the Soundhole</strong> (pitched): plucking the string over the soundhole, producing sounds with lower treble content and greater intensity;</li> <li><strong>Bending technique</strong> (pitched): pulling the strings, raising the pitch (half-tone interval);</li> <li><strong>Hammer-on technique</strong> (pitched): sharply bringing a finger down onto the fingerboard, creating a legato sound (half-tone interval);</li> <li><strong>Staccato</strong> (pitched): playing short notes;</li> <li><strong>Vibrato</strong> (pitched): Moving the fretting finger to warp the pitch and tone of the sound.</li> </ol> <p>Techniques 1 through 8 have been meticulously labeled.</p> <p> </p> <p>This dataset is presented in detail in the following conference paper:</p> <div>D. Stefani, G. A. Giudici, and L. Turchet. 2024. <strong>On the Importance of Temporally Precise Onset Annotations for Real-Time Music Information Retrieval: Findings from the AG-PT-set Dataset</strong>. In Proceedings of the 19th International Audio Mostly Conference: Explorations in Sonic Cultures (AM '24). </div> <p><a href="https://doi.org/10.1145/3678299.3678325">https://doi.org/10.1145/3678299.3678325</a></p>
Multigrid spatially constrained dispersion curve inversion package: towards distributed acoustic sensing surface wave imaging
<p>Surface wave methods, commonly applied in diverse fields, encounter challenges in complex subsurface environments due to limitations inherent in traditional inversion techniques. Conventional one-dimensional inversion (1DI), with its reliance on fixed grids and deterministic linear approaches, often introduces biases, diminishing lateral resolution. Laterally constrained inversion (LCI) improves robustness by addressing lateral coherency but falls short in delineating arbitrary interfaces due to its dependency on fixed grid models. The advent of Distributed Acoustic Sensing (DAS) technology offers extensive seismic data, yet its potential for high-resolution imaging remains underutilized. We introduce a Multigrid Spatially Constrained Dispersion Curve Inversion (MCI) method to overcome these challenges, aiming to harness high-resolution DAS surface wave imaging capabilities. </p> <p>The package includes essential scripts and models required to replicate key figures from the study by Guan et al. (2023, currently under review). These codes are designed to help readers evaluate the effectiveness of the MCI approach using synthetic demonstrations. Additionally, the package includes a refined 2D Vs (shear wave velocity) model derived from a DAS (Distributed Acoustic Sensing) field study conducted in Imperial Valley, California. This model offers new insights into the regional fault system, underscoring the importance of enhanced spatial resolution in large-scale geophysical investigations.</p> <p>It is organized into three directories and contains a total of 14 files. The directory structure is as follows:<br>├── DAS field data<br>│ ├── Pltmodels.m<br>│ ├── README.txt<br>│ ├── field_models.pdf<br>│ ├── model_1DI.mat<br>│ ├── model_LCI.mat<br>│ └── model_MCI.mat<br>├── MCI_Main<br>│ ├── DisForward.p<br>│ ├── InvForward.p<br>│ ├── InvJacobian.p<br>│ ├── MCI.p<br>│ ├── readme.txt<br>│ └── whitejet3.m<br>└── Synthetic demos<br> ├── MCI_Main.m<br> └── syndata.mat</p>
Data from: movement or plasticity: acoustic responses of a torrent frog to stream geophony
<p>Vocalization is the main form of communication in many animals, including frogs, which commonly emit advertisement calls to attract females and maintain spacing. In noisy environments such as streams, mechanisms to maximize signaling efficiency may include vocal plasticity and/or movement of individuals to quieter sections, but which strategy is used is still uncertain. We investigated the influence of stream geophony on the advertisement call of the torrent frog <em>Hylodes perere</em> in the Atlantic Rainforest, southeastern Brazil. In a mark-recapture study, we tested if males remain in their territories and thus adjust their advertisement calls to maximize their communication. We ran mixed linear and generalized models to verify the relation of call parameters and stream geophony, body size and environmental temperature. We found that males remained in the same location across time, increased call intensity in noisier environments but did not reduce call effort. Males also increased the dominant frequency in these situations, suggesting a modulation in this parameter. Our results indicate that territoriality is an important factor to males to increase call intensity to surpass stream noise instead of repositioning along the stream. However, because call effort was maintained, we suggest that sexual selection is crucial in this system, favoring males that better detect others and adjust their call efficiency. This is the first study to evaluate simultaneously frog movements and adaptations to geophony, which contributes to the investigation of the concomitant environmental and sexual selective pressures in species that communicate in noisy environments.</p>
Data for: Collective signalling is shaped by feedbacks between signaller variation, receiver perception, and acoustic environment in a simulated communication network
<p>Communication takes place within a network of multiple signallers and receivers. Social network analysis provides tools to quantify how an individual's social positioning affects group dynamics, and the subsequent biological consequences. However, network analysis is rarely applied to animal communication, likely due to the logistical difficulties of monitoring natural communication networks. We generated a simulated communication network to investigate how variation in individual communication behaviours generates network effects, and how this communication network's structure feeds back to affect future signalling interactions. We simulated competitive acoustic signalling interactions among chorusing individuals and varied several parameters related to communication and chorus size to examine their effects on calling output and social connections. Larger choruses had higher noise levels, and this reduced network density and altered the relationships between individual traits and communication network position. Hearing sensitivity interacted with chorus size to affect both individuals' positions in the network and the acoustic output of the chorus. Physical proximity to competitors influenced signalling, but a distinctive communication network structure emerged when signal active space was limited. Our model raises novel predictions about communication networks that could be tested experimentally, and identifies aspects of information processing in complex environments that remain to be investigated. </p>
Acoustic features as a tool to visualize and explore marine soundscapes: Applications illustrated using marine mammal Passive Acoustic Monitoring datasets
<p>Passive Acoustic Monitoring (PAM) is emerging as a solution for monitoring species and environmental change over large spatial and temporal scales. However, drawing rigorous conclusions based on acoustic recordings is challenging, as there is no consensus over which approaches, and indices are best suited for characterizing marine and terrestrial acoustic environments.</p> <p>Here, we describe the application of multiple machine-learning techniques to the analysis of a large PAM dataset. We combine pre-trained acoustic classification models (VGGish, NOAA & Google Humpback Whale Detector), dimensionality reduction (UMAP), and balanced random forest algorithms to demonstrate how machine-learned acoustic features capture different aspects of the marine environment.</p> <p>The UMAP dimensions derived from VGGish acoustic features exhibited good performance in separating marine mammal vocalizations according to species and locations. RF models trained on the acoustic features performed well for labelled sounds in the 8 kHz range, however, low and high-frequency sounds could not be classified using this approach.</p> <p>The workflow presented here shows how acoustic feature extraction, visualization, and analysis allow for establishing a link between ecologically relevant information and PAM recordings at multiple scales.</p> <p>The datasets and scripts provided in this repository allow replicating the results presented in the publication. </p>
IEEE ICME 2024 Grand Challenge: Semi-supervised Acoustic Scene Classification under Domain Shift Evaluation Dataset
<p>The Chinese Acoustic Scene (CAS) 2023 dataset is a large-scale dataset that serves as a foundation for research related to environmental acoustic scenes. The dataset includes 10 common acoustic scenes, with a total duration of over 130 hours. Each audio clip is 10 seconds long with metadata about the recording location and timestamp. The dataset was collected by members of the <em>Joint Laboratory of Environmental Sound Sensing at the School of Marine Science and Technology, Northwestern Polytechnical University</em>. The data collection period spanned from April 2023 to September 2023, covering 22 different cities across China. The CAS 2023 dataset was collected using the XS-SN-2BE1 manufactured by <em>Xi'an Lianfeng Acoustic Technologies Co., Ltd</em> (https://www.lfxstek.com/). </p> <p>The ICME 2024 <em>Semi-supervised Acoustic Scene Classification under Domain Shift</em> challenge (https://2024.ieeeicme.org/grand-challenge-proposals/, https://ascchallenge.xshengyun.com/) dataset consists of development (https://zenodo.org/records/10616533) and evaluation datasets, all derived from the CAS 2023 dataset. The evaluation dataset includes 1,100 recordings, where data are selected from 12 cities, with 5 unseen cities specifically chosen to provide a more comprehensive evaluation of submissions under domain shift.</p> <p>Baseline: https://github.com/JishengBai/ICME2024ASC</p> <p>Acoustic scenes (10): Bus, Airport, Metro, Restaurant, Shopping mall, Public square, Urban park, Traffic street, Construction site, Bar</p>
Data from: Acoustic surveillance of bats along the Green and Colorado Rivers
<p><em>Aim</em>: Emerging research shows how bioindicators, specifically bats, can serve as a means for monitoring conservation and management of riparian corridors for multiple taxonomic groups. To track changes in composition or abundance of bioindicator species, researchers must attain a baseline in species presence and relative activity. We examined the spatial and temporal patterns of bat community composition and activity along a 1,000-mile river corridor to determine species diversity trends by latitude and habitat.</p> <p><em>Location</em>: Colorado River Basin</p> <p><em>Methods</em>: Here we describe the results from an acoustic bat survey conducted opportunistically on the 2019 Sesquicentennial Colorado River Exploring Expedition. This broad, 1,000-mile survey provides a baseline for species distributions over a large geographic range.</p> <p><em>Results</em>: In total, we collected 63 nights of acoustic data over 70-days and recorded over 59,000 files equating to 45,363 call files (≥2 pulses). 18,490 (41% of call files) were identified to species (n = 19 bat species). We applied non-metric multidimensional scaling to characterize spatiotemporal patterns of activity between species, as well as compared bat activity among river features and local environmental conditions (i.e., temperature and time since sunset) using an information theoretic approach.</p> <p><em>Conclusion</em>: Species composition varied by physiographic region and adjacent river habitat, thus providing a quantifiable measure of determining habitat quality along this major river system and providing baseline information for using bats as bioindicators of habitat quality</p>
Vibroscape analysis reveals acoustic niche overlap and plastic alteration of vibratory courtship signals in ground-dwelling wolf spiders
<p>Soundscape ecology has enabled researchers to investigate natural interactions among biotic and abiotic sounds as well as their influence on local animals. To expand the scope of soundscape ecology to encompass substrate-borne vibrations (i.e. vibroscapes), we developed methods for recording and analyzing sounds produced by ground-dwelling arthropods to characterize the vibroscape of a deciduous forest floor using inexpensive contact microphone arrays followed by automated sound filtering and detection in large audio datasets. Through the collected data, we tested the hypothesis that closely related species of <em>Schizocosa</em> wolf spider partition their acoustic niche. In contrast to previous studies on acoustic niche partitioning, two closely related species - <em>S. stridulans</em> and <em>S. uetzi</em> - showed high acoustic niche overlap across space, time, and/or signal structure. Finally, we examined whether substrate-borne noise, including anthropogenic noise (e.g., airplanes) and heterospecific signals, promotes behavioral plasticity in signaling behavior to reduce the risk of signal interference. We found that all three focal <em>Schizocosa</em> species increased the dominant frequency of their vibratory courtship signals in noisier signaling environments. Also, <em>S. stridulans</em> males displayed increased vibratory signal complexity with an increased abundance of <em>S. uetzi</em>, their sister species with which they are highly overlapped in the acoustic niche.</p>
[Data] Acoustic emission signature of martensitic transformation in Laser Powder Bed Fusion of Ti6Al4V-Fe, supported by operando X-ray diffraction
<p>The dataset for this study focuses on investigating Acoustic Emission (AE) monitoring in the Laser Powder Bed Fusion (LPBF) process, using premixed Ti6Al4V-(x wt%) Fe, where x = 0, 3, and 6. By employing a structure-borne AE sensor, we analyze AE data statistically, uncovering notable discrepancies within the 50-750 kHz frequency range. Leveraging Machine Learning (ML) methodologies, we accurately predict composition for particular processing conditions. These fluctuations in AE signals primarily arise from unique microstructural alterations linked to martensitic phase transformation, corroborated by operando synchrotron X-ray diffraction and post-mortem SEM and EBSD analysis. Moreover, cracks are evident at the periphery of the printed parts, stemming from local inadequate heat input during the blending of Ti6Al4V with added Fe powder. These cracks are discerned via AE signals subsequent to the cessation of the laser beam, correlating with the presence of brittle intermetallics at their junction. This study highlights for the first time the potential of AE monitoring in reliably detecting footprints of martensitic transformations during the LPBF process. Additionally, AE is shown to prove valuable for assessing crack formations, particularly in scenarios involving premixed powders and necessitating precise selection of processing parameters, notably at part edges.</p>
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Fig. 2 in Acoustic Signalling In Eurasian Penduline Tits Remiz Pendulinus: Repertoire Size Signals Male Nest Defence
Fig. 2. SonogramsofsometypicalsyllabletypesofEurasianpendulinetits. Songbouts mayconsistofvarioussyllables (topandbottomsonograms) ormayincludemonotone
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.