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12 results for “acoustic localization”
Dataset accompanying the publication: Acoustic cues of keyboard mechanics enable auditory localization of upright piano tones
<p>Dataset accompanying the publication: Acoustic cues of keyboard mechanics enable auditory localization of upright piano tones (in J. Acoust. Soc. Am., 2024)</p>
[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>
Robust Acoustic Reflector Localization for Robots
<p>In this repository, we share our MATLAB code and dataset used to perform the experiments listed within our paper "<strong>Robust Acoustic Reflector Localization for Robots.</strong>"</p>
A model-based approach to acoustic reflector localization with a robotic platform
<p>Constructing a spatial map of an indoor environment, e.g., a typical office environment with glass surfaces, is a difficult and challenging task. Current state-of-the-art, e.g., camera- and laser-based approaches are unsuitable for detecting transparent surfaces. Hence, the spatial map generated with these approaches are often inaccurate. In this paper, a method that utilizes echolocation with sound in the audible frequency range is proposed to robustly localize the position of an acoustic reflector, e.g., walls, glass surfaces etc., which could be used to construct a spatial map of an indoor environment as the robot moves. The proposed method estimate the acoustic reflector’s position, using only a single microphone and a loudspeaker that are present on many socially assistive robot platforms such as the NAO robot. The experimental results show that the proposed method could robustly detect an acoustic reflector up to a distance of 1.5 m in more than 60% of the trials and works efficiently even under low SNRs. To test the proposed method, a proof-of-concept robotic platform was build to construct a spatial map of an indoor environment.</p> <p>This dataset is made available with IROS 2020 paper: https://ieeexplore.ieee.org/abstract/document/9341437</p> <p>code could be found on Github: https://github.com/irtiq7/iROS2020</p>
Acoustic Emission dataset for impact localization: numerical and experimental case studies
<h1>Acoustic Emission dataset for Defect Detection in Aluminum plates</h1> <h2>Simulated data</h2> <h3>File name: Simulation.zip</h3> <p>Simulated AE signals based on a ray-tracing algorithm taking into consideration reflections with the mechanical boundaries of the medium (reflection up to the 4th order), which corresponds to a 1x1x0.003 m square aluminum plate.</p> <p>Each signal has been created by simulating the propagation between a transmitter (Tx, index from 1 to 40) and a Receiver (Rx, index from 1 to 25), grouped by Tx position and saved as a .mat struct ('data') containing the following fields:</p> <ul> <li>data.Rx = 2 x 25 matrix containing in the first and second row the x and y axis of the Rx position</li> <li>data.Rx = 2 x 25 matrix containing in the first and second row the x and y axis of the Tx position</li> <li>data.data = 8000 x 25 matrix containing the transmitted, propagated AE signal from Tx to Rx organized by column. Each AE instance constitutes of 8000 samples acquired at a sampling frequency of 2 MHz (indicated in the file name), one for each Rx given that Tx position.</li> <li>data.Label = 1x25 vector containing the ToA labels associated with each of the 25 Tx-rx pairs (per Tx position) computed by means of the Akaike Information Criterion. </li> </ul> <h2>Experimental data</h2> <h3>File name: Test_x0.xx_y0.yyFs2MHz_1x1x0.003_Al.csv</h3> <p>Experimental data collected with custom AE instrumentation as described in <a href="https://www.mdpi.com/1424-8220/22/3/1091">Ref 1.</a> </p> <p>One single file is a collection of 3 tests (three repetitions of the impact event at the same position), each of them containing three signals acquired simultaneously by three sensors located in proximity of three corners of a 1x1x0.003 aluminum plate having the same geometrical and numerical characteristics of the numerical one. The sensors acquire 5000 samples at a rate of 2 MHz (indicated in the file name) with a pre-trigger window of 1500 samples. The specific coordinates of the sensors are:</p> <ul> <li>s1 [x = 0.05, y = 0.95] m</li> <li>s2 [x = 0.05, y = 0.05] m</li> <li>s3 [x = 0.95, y = 0.05] m</li> </ul> <p>There are 9 files associated with as many impact positions, indicated by the "x0.xx_y0.yy" entry in the file name, with 0.xx and 0.yy corresponding to the x and y coordinate, respectively. Excitation has been provided by means of a waveform generator exciting a 3-cycle sinusoidal wave with central frequency of 250 kHz. More deatils about the electronics and the full setup are provided in the same reference above. </p> <p> </p> <p><em>This research work has been carried out within the Intelligent Sensor Systems Lab@University of Bologna, Italy. </em></p> <p><em>For any needs, warning or curiosities, please contact federica.zonzini@unibo.it</em></p>
FIGURE1. Breeding, migrating and wintering distributions of Palearctic Anthus [rubescens] japonicus and Nearctic Anthus rubescens rubescens/alticola subspecies groups (from BirdLife International 2022; illustration @Andrew Birch). Circles indicate origins of sequenced individuals and triangles indicate origins of analysed recordings of calls. Localities outside of the usual range of the species complex (e.g., Ireland, Oman and Israel) are not figured here. in --Molecular--and--acoustic--evidence--support--the--species--status--of--Anthus rubescens rubescens and--Anthus [rubescens] japonicus--(Passeriformes:--Motacillidae)
FIGURE1. Breeding, migrating and wintering distributions of Palearctic Anthus [rubescens] japonicus and Nearctic Anthus rubescens rubescens/alticola subspecies groups (from BirdLife International 2022; illustration @Andrew Birch). Circles indicate origins of sequenced individuals and triangles indicate origins of analysed recordings of calls. Localities outside of the usual range of the species complex (e.g., Ireland, Oman and Israel) are not figured here.
Data and Code for [Localization of Mechanical and Electrical Defects in Dry-Type Transformers Using an Optimized Acoustic Imaging Approach]
Open the record for dataset details and reuse information.
Offset-Controlled Localized Velocity Inversion in the $\tau-p$ Domain Using Acoustic Traveltimes: Modeling and Application in Borehole Measurements
Open the record for dataset details and reuse information.
The Effects of High Energy Acoustic Shock Wave Therapy on Local Skin Perfusion and DFUs
ClinicalTrials.gov study NCT03640988. IPD Sharing: NO. Countries: 1. Publications: 0.
Acoustic Cluster Therapy (ACT) With Chemotherapy for the Treatment of Locally Advanced Pancreatic Cancer
ClinicalTrials.gov study NCT06850623. IPD Sharing: NO. Countries: 2. Publications: 0.
Virtual Acoustic Localization Experiment
ClinicalTrials.gov study NCT02602171. IPD Sharing: Not stated. Countries: 0. Publications: 0.
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.