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263 results for “listening”
Listening and watching: do camera traps or acoustic sensors more efficiently detect wild chimpanzees in an open habitat?
<p>1. With one million animal species at risk of extinction, there is an urgent need to regularly monitor threatened species. However, in practice this is challenging, especially with wide-ranging, elusive and cryptic species or those that occur at low density.<br> 2. Here we compare two non-invasive methods, passive acoustic monitoring (n=12) and camera trapping (n=53), to detect chimpanzees (Pan troglodytes) in a savanna-woodland mosaic habitat at the Issa Valley, Tanzania. With occupancy modelling we evaluate the efficacy of each method, using the estimated number of sampling days needed to establish chimpanzee absence with 95% probability, as our measure of efficacy.<br> 3. Passive acoustic monitoring was more efficient than camera trapping in detecting wild chimpanzees. Detectability varied over seasons, likely due to social and ecological factors that influence party size and vocalization rate. The acoustic method can infer chimpanzee absence with less than ten days of recordings in the field during the late dry season, the period of highest detectability, which was five times faster than the visual method.<br> 4. Synthesis and applications: Despite some technical limitations, we demonstrate that passive acoustic monitoring is a powerful tool for species monitoring. Its applicability in evaluating presence/absence, especially but not exclusively for loud call species, such as cetaceans, elephants, gibbons or chimpanzees provides a more efficient way of monitoring populations and inform conservation plans to mediate species-loss.</p>
Listening preferences for variations of pop mixes in a circular Wave Field Synthesis system
<p>This data set includes results for the listening tests described in<br> http://doi.org/10.5281/zenodo.61000, but this time applying the real<br> loudspeakers and not binaural synthesis.</p> <p>The folder `results/` contains the single results of the 41 test participants.<br> The folder `analisys/` contains the script `pc_matrices.py` for creating paired<br> comparison matrices across the participants for all conditions, that are stored<br> under `analysis/pc_matrices/`. It also contains the script `btl.R`, which<br> calculates a Bradley-Terry-Luce model after Wickelmayer et al. [1]. Its results<br> are stored under `analysis/btl/` and plotted in `analysis/btl_systems.pdf` and<br> `analysis/btl_mixing.pdf` for comparing the different reproduction systems<br> together for all conditions and independent for the different mixing parameters,<br> respectively. All steps to recreate the plot are listed in the script<br> `run_all.sh` in the `analysis/` folder.</p> <p>[1] https://cran.r-project.org/web/packages/eba/index.html</p>
Listening tests_Calleri et al.
<p>This audio files have been obtained through auralisations performed using Odeon (v.13) software. Results of the listening tests performed using such auralisations are presented in scientific works actually under review</p>
Dataset, test programs and analysis scripts for the related paper "Effects of reverberation on speech intelligibility in noise for hearing-impaired listeners"
<p>This dataset contains the data, test programs and analyses scripts used for a study submitted as a stage 2 registered report for Royal Society Open Science.</p>
Plots of interaural cues of listening experiment conditions (exact vs. experiment approximation)
<p>The plots show interaural cohehrence (IC) and interaural level difference (ILD) for a subset of experiment conditions presented in the JASA-EL publication 'Surrounding line sources optimally reproduce diffuse envelopment at off-center listening positions'. The experiment compared on-center with off-center listening (0.5 times radius). Off-center listening was simulated by remapped loudspeaker signals in a 24-channel loudspeaker setup. The nearest available loudspeaker was chosen for a direction shift, and the approximated angles (approx.) lead to negligible deviations in interaural cues compared to the interaural cues of an actually shifted listener (exact). Curves result from IC and ILD computations in 320 gammatone frequency bands, using a KU100 HRTF database.</p>
IKA CI Music Preprocessing Listening Experiment Stimuli (2022)
<p><strong>IKA CI Music Preprocessing Listening Experiment Stimuli (2022)</strong></p> <p>This dataset contains the audio stimuli that have been presented to both cochlear implant (CI) and normal hearing (NH) listeners in the listening experiments in a study named</p> <p><em>“A Subjective Evaluation of Different Music Preprocessing Approaches in CI Listeners”</em></p> <p>It comprises the excerpts from the <strong>IKA CI Pop Music Dataset (IKA-CI-PMD)</strong> (<a href="https://doi.org/10.5281/zenodo.7060282">10.5281/zenodo.7060282</a>) both as unprocessed references and as processed versions where different music preprocessing strategies have been<br> applied.</p> <p>The <strong>IKA CI Pop Music Dataset</strong> is a dataset of music excerpts that has especially compiled to evaluate different music signal preprocessing strategies for CI listeners. The excerpts taken are from the <strong>MedleyDB </strong>multitrack dataset (<a href="https://medleydb.weebly.com/">https://medleydb.weebly.com/</a>) curated by <a href="https://steinhardt.nyu.edu/marl/research/resources/medleydb">Rachel Bittner et. al.</a>.</p> <p>This dataset is split into a 3-piece “training” set (excerpts T01 to T03) that has been used to familiarize the listeners with the experimental setup, and a 12-piece “test” set (E01 to E12) used in actual experiments.</p> <p>The following music preprocessing strategies are included:</p> <ul> <li>HPCA+P: Harmonic/percussive sound separation (HPSS) combined with PCA-based spectral complexity reduction [1]</li> <li>Cspl and Dspl: DNN-based remix of the harmonic and percussive portions of 4 source stems [2]</li> <li>HALCA: Remix based on a probabilistic model for melody extraction using shift invariant kernels in CQT domain [3] (accompaniment attenuated by 12 dB)</li> <li>MT remix: Oracle remixes of multitrack stems (other accompaniment attenuated by 12 dB)</li> </ul> <p>All stimuli are normalized to a loudness level of -27 LUFS. The signals are stored in the lossless FLAC format. The files contain stereo signals, where both channels are identical.</p> <p>The dataset has been compiled at the Ruhr University Bochum <a href="https://www.ruhr-uni-bochum.de/ika/index_en.html">Institute of Communication Acoustics</a> in 2022 by Johannes Gauer (johannes.gauer@rub.de) in collaboration with the fellow researchers Anil Nagathil, Benjamin Lentz, and Rainer Martin. Like MedleyDB and the IKA CI Pop Music Dataset, it is licensed under a <a href="http://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.</p> <p>For further details on the included excerpts refer to the IKA CI Pop Music Dataset (IKA-CI-PMD) (<a href="https://doi.org/10.5281/zenodo.7060282">10.5281/zenodo.7060282</a>).</p> <p>[1] B. Lentz, A. Nagathil, J. Gauer, and R. Martin, “Harmonic/Percussive sound separation and spectral complexity reduction of music signals for cochlear implant listeners,” in Proc IEEE Int Conf Acoust Speech Signal Process ICASSP, Barcelona, Spain, May 2020, pp. 8713–8717.</p> <p>[2] J. Gauer, A. Nagathil, K. Eckel, D. Belomestny, and R. Martin, “A versatile deep-neural-network-based music preprocessing and remixing scheme for cochlear implant listeners,” J. Acoust. Soc. Am., vol. 151, no. 5, pp. 2975–2986, May 2022.</p> <p>[3] B. Fuentes, R. Badeau, and G. Richard, “Harmonic Adaptive Latent Component Analysis of Audio and Application to Music Transcription,” IEEE Trans. Audio Speech Lang. Process., vol. 21, no. 9, pp. 1854–1866, Sep. 2013.</p>
Materials used in "Measuring audio-visual speech intelligibility under dynamic listening conditions using virtual reality"
<p>The materials in this record are the audio and video files, together with various configuration files, used by the "SEAT" software in the study described in</p> <p>Moore, Green, Brookes & Naylor (2022) "Measuring audio-visual speech intelligibility under dynamic listening conditions using virtual reality"</p> <p>They are shared in this form so that the experiment may be reproduced. For any other use please contact the authors to obtain the original database(s) from which these materials are derived.</p> <p>The materials were created to be compatible with v0.3 of SEAT, which is available from <a href="https://github.com/ImperialCollegeLondon/sap-elospheres-audiovisual-test/releases/tag/v0.3">GitHub</a>. Note that the materials must be placed at <code>C:\seat_experiments\cafe_AV.</code></p>
Rendered Stimuli for "Spatial Analysis and Synthesis Methods: Subjective and Objective Evaluations Using Various Microphone Arrays in the Auralization of a Critical Listening Room"
<h2>Rendered Stimuli from the Subjective Evaluation</h2> <p>This archive (<code>Stimuli.zip</code>) contains the rendered stimuli used in the subjective evaluation of various spatial analysis and synthesis methods, as described in the paper "Spatial Analysis and Synthesis Methods: Subjective and Objective Evaluations Using Various Microphone Arrays in the Auralization of a Critical Listening Room" by Alan Pawlak, Hyunkook Lee, Aki Mäkivirta, and Thomas Lund.</p> <p>The stimuli are provided to improve the reproducibility of the study and to allow readers to listen to the same audio samples used in the subjective evaluation.</p> <h2>File Naming Convention:</h2> <p><code>SYSTEM_PROGRAMMEMATERIAL_AZIMUTH_ELEVATION_-26LUFS.wav</code></p> <p>- <code>SYSTEM</code>: The spatial analysis and synthesis method used (e.g., BSDM-6OM1-Omni, HO-SIRR, SDM-em32, etc.)<br>- <code>PROGRAMMEMATERIAL</code>: The anechoic audio sample used (Bongo, Speech, Orchestra)<br>- <code>AZIMUTH</code>: The azimuth angle of the sound source (e.g., 0, 30, 45, 90, 135)<br>- <code>ELEVATION</code>: The elevation angle of the sound source (e.g., 0, 45)</p> <h2>Audio File Specifications:</h2> <p>- Format: WAV<br>- Sample Rate: 48 kHz<br>- Bit Depth: 32-bit<br>- Loudness Normalization: -26 LUFS</p> <p>To use these stimuli, simply load the desired WAV file into your audio playback software.</p> <p>For more information about the study, please refer to the full paper.</p> <p>Pawlak, A., Lee, H., Mäkivirta, A. and Lund, T., 2024. Spatial Analysis and Synthesis Methods: Subjective and Objective Evaluations Using Various Microphone Arrays in the Auralization of a Critical Listening Room.</p>
Facial electromyography while listening to stories based on the moral foundations (New Zealand) [MV4]
<p>Raw data from a New Zealand replication of Cannon, P. R., Schnall, S., & White, M. (2011). Transgressions and expressions: Affective facial muscle activity predicts moral judgments. <em>Social Psychological and Personality Science, 2</em>, 325-331.</p> <p>Raw data are in proprietary format: Behavioural data is PST's Eprime 2 Professional (*.edat2) and Physiology data is Biopac's AcqKnowledge (*.ACQ). Text formatted raw data will be posted in a later version of the repository in a single archive once data collection is completed.</p>
Groove in drum patterns as a function of both rhythmic properties and listeners' attitudes
<p>Data set for article "Groove in drum patterns as a function of both rhythmic properties and listeners' attitudes".</p>
Data of Listening Experiments for Azimuthal Localisation in (Local) Sound Field Synthesis
<p>Data of two listening experiments conducted at University of Rostock, Germany. The study investigated the four (Local) Sound Field Synthesis techniques</p> <ul> <li>Wave Field Synthesis</li> <li>Near-Field-Compensated Higher-Order Ambisonics</li> <li>Local Wave Field Synthesis using Spatial Bandwidth Limitation</li> <li>Local Wave Field Synthesis using Virtual Secondary Sources</li> </ul> <p>The corresponding binaural room scanning (BRS) files for the binaural simulation can be found in the directory `brs`. The employed noise stimulus is contained in `stimuli`. The localisation results are stored in `results`. The `analysis` directory includes scripts for parsing the data.</p>
fUS imaging of ferret auditory cortex during passive listening of natural sounds
<p>Source data for paper: Distinct higher-order representations of natural sounds in human and ferret (BiorXiv, 2020), Landemard A, Bimbard C, Demené C, Shamma S, Norman-Haigneré S, Boubenec Y.</p> <p>This data repository contains several folders:<br>- <em>fUSData </em>contains raw data for all recording sessions. Information on data formatting can be found in README_fUSData text file.<br>- <em>Analysis</em> contains processed and denoised data. This data can be readily used to produce our figures using our publicly available scripts. This data can also be re-generated using data from <em>fUSData </em>folder using our denoising scripts. <br>- <em>AdditionalData </em>contains additional files necessary to run some of the analyses. </p> <p>Code implementing our denoising procedure and reproducing figures from the paper can be found on <a href="http://github.com/agneslandemard/naturalsounds_analysis">https://github.com/agneslandemard/naturalsounds_analysis </a></p> <p> </p>
Understanding degraded speech leads to perceptual gating of a brainstem reflex in human listeners
<p>The ability to navigate "cocktail-party" situations by focussing on sounds of interest over irrelevant, background sounds is often considered in terms of cortical mechanisms. However, subcortical circuits such as the pathway underlying the medial olivocochlear (MOC) reflex modulate the activity of the inner ear itself, supporting the extraction of salient features from auditory scene prior to any cortical processing. To understand the contribution of auditory subcortical nuclei and the cochlea in complex listening tasks, we made physiological recordings along the auditory pathway while listeners engaged in detecting non(sense)-words in lists of words. Both naturally spoken and intrinsically noisy, vocoded speech—filtering that mimics processing by a cochlear implant—significantly activated the MOC reflex, but this was not the case for speech in background noise, which more engaged midbrain and cortical resources. A model of the initial stages of auditory processing reproduced specific effects of each form of speech degradation, providing a rationale for goal-directed gating of the MOC reflex based on enhancing the representation of the energy envelope of the acoustic waveform. Our data reveals the co-existence of two strategies in the auditory system that may facilitate speech understanding in situations where the signal is either intrinsically degraded or masked by extrinsic acoustic energy. Whereas intrinsically degraded streams recruit the MOC reflex to improve representation of speech cues peripherally, extrinsically masked streams rely more on higher auditory centres to de-noise signals.</p>
Raw data for manuscript Semantic context can mask intelligibility declines at above-conversational speech levels in normal-hearing listeners
<p>Raw data for the manuscript in doc file. <br> Copied from the Matlab .m file. used for the analysis.</p> <p>To be updated.</p> <p>For details, contact me at mfer@health.sdu.dk</p>
The Effectiveness of Piano Therapy vs. Piano Listening on Manual Dexterity in the Elderly
ClinicalTrials.gov study NCT03372031. IPD Sharing: YES. Countries: 1. Publications: 4.
Dichotic Listening as a Predictor of Medication Response in Depression
ClinicalTrials.gov study NCT00296725. IPD Sharing: Not stated. Countries: 1. Publications: 6.
Listening Visits for Emotionally Distressed Mothers of Hospitalized Newborns
ClinicalTrials.gov study NCT03704948. IPD Sharing: NO. Countries: 1. Publications: 2.
Bringing Education Through Technology, Empathic Listening, and Research
ClinicalTrials.gov study NCT05214118. IPD Sharing: NO. Countries: 1. Publications: 0.
Fitness, Hearing and Quality of Life in Older Adults With Hearing Loss. Walk, Talk and Listen for Your Life
ClinicalTrials.gov study NCT02662192. IPD Sharing: NO. Countries: 1. Publications: 2.
The Listening Program® With Bone Conduction Headphones Changes Hypersensitivity to Sound and Behavioral Responses
ClinicalTrials.gov study NCT05009095. IPD Sharing: NO. Countries: 1. Publications: 1.
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.