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13 results for “Listening experiments”
Listening test results for sound field synthesis localization experiment
<p>Result files from the the localization experiments described in section 5.1 of Wierstorf [1].</p> <p>The results are visually summarized in Fig. 5.4, see https://github.com/hagenw/phd-thesis/tree/master/05_psychoacoustics/fig5_04</p> <p>[1] H. Wierstorf, Perceptual Assessment of Sound Field Synthesis, PhD dissertation, TU Berlin, 2014.</p>
Listening test results for sound field synthesis localization experiment -- head movement data
<p>This data set contains recorded head movements listeners did during several localisation tasks in the context of sound field synthesis. This is an add-on to the actual localisation results provided by [1].</p> <p>[1] Wierstorf, H. (2016). Listening test results for sound field synthesis localization experiment [Data set]. Zenodo. http://doi.org/10.5281/zenodo.55439</p>
Raw and post-processing data for using auditory models to mimic human listeners in reverse correlation experiments from the fastACI toolbox
<p><strong>Description</strong>: The current dataset provides all the stimuli (folder ../01-Stimuli/), raw data (folder ../02-Raw-data/) and post-processed data (../03-Post-proc-data/) used in the Forum Acusticum 2013 paper titled "Using auditory models to mimic human listeners in reverse correlation experiments from the fastACI toolbox" by the same authors. In this paper, we replicated the tone-in-noise experiment by Ahumada et al. (1975) but using an artificial listener instead of collecting data from real participants. The behavioural data were mimicked using an artificial listener based on 'king2019' (King et al., 2019) as a front-end model using a template-matching decision to indicate whether a 500-Hz tone was (or not) present in each of the noisy trials. This study offers a step-by-step guide of how can be an artificial listener integrated into fastACI.</p> <p><strong>Use these data</strong>: Download all these data, locate them in a local directory of your computer. If you have MATLAB and you downloaded a local copy of the fastACI toolbox (open access at: <a href="https://github.com/aosses-tue/fastACI">https://github.com/aosses-tue/fastACI</a>) you can recreate the figures of our paper. After downloading and initialising the toolbox (type 'startup_fastACI;', without quotation marks in MATLAB), run the script <strong>g20230501_FA_Artificial_listener_paper_figs.m</strong> (provided in this dataset) and follow the instructions on the screen to generate one of the four study figures. This script calls the function <strong>publ_osses2023b_FA_figs.m</strong> from the toolbox. </p> <p> </p>
Listening experiment and Stimuli for: Adjustable Deterministic Pseudonymization of Speech
<p>Web pages for listening experiment, with stimuli included, as reported in: Adjustable Deterministic Pseudonymization of Speech</p> <p>The listening experiments can be run locally offline. After unpacking the files, the listening experiment can be run locally or in a web site by pointing a web browser to the index.html file.</p> <p>A report discussing the pseudonymization results can be found at doi: 10.5281/zenodo.3773931</p> <p>A dataset created with this expriment can be found at doi: 10.5281/zenodo.3773936</p> <p>The <em>akouste</em> listening experiment software can be found at doi: 10.5281/zenodo.3712142 on Github</p> <p>The <em>Pseudonymize</em> <em>Speech</em> <em>Praat</em> script can be found at doi: 10.5281/zenodo.3712140</p> <p>The <em>Praat</em> speech software can be found at <em>www.praat.org</em></p>
Themed Evidence: Listening Experiences - Gold Standard
<p>Exploring digital resources in search of pieces of evidence relevant to a certain research theme is a difficult and important task for humanities research. The concept of evidence is a particularly difficult one, as it relates to a fact being reported in a text, which is relevant to a certain subject of enquiry. </p> <p>The Listening Experience Database Project (LED) - http://www.listeningexperience.org/ - is an initiative funded by the UK's Arts and Humanities Research Council (AHRC) aimed at collecting accounts of people’s private experiences of listening to music.</p> <p>This dataset is a gold standard for supporting the evaluation of competing methods for detecting themed evidence of "listening experience".</p>
Data set for: Adjustable Deterministic Pseudonymization of Speech Listening Experiment, Report of listening experiments
<p>Data set used in "Adjustable Deterministic Pseudonymization of Speech Listening Experiment". Includes Rmarkdown script.</p> <p> </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>
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>
Code and data for colouration experiment 'WFS in listening room'
<p>This data set contains stimuli, results and code for statistical analysis of a colouration experiment where humans had to rate the colouration of virtual sources synthesised by Wave Field Synthesis in free field and in different listening rooms compared to a real source as reference. The stimuli were presented via dynamic binaural synthesis with the <a href="http://www.spatialaudio.net/ssr/">SoundScape Renderer</a>. The source code is licensed under GPL v3.</p> <p>Overview:</p> <ul> <li>The folder 'stimuli' contains the impulse responses for the SoundScape Renderer as WAV files and anechoic audio content.</li> <li>The folder 'experimental_results' contains the data saved in the listening experiment as CSV files.</li> <li>The folder 'statistics' contains <a href="http://www.mathworks.com">Matlab</a> scripts that perform the statistical analysis.</li> </ul>
Binaural auralizations of listening experiment stimuli (Envelopment / Engulfment / Spatial Granular Synthesis)
<p>Binaural auralizations of experiment stimuli with KU100 BRIRs, measured at the listening position of the experiments in the IEM CUBE (25-channel loudspeaker hemisphere). The files are 2-channel WAVs for headphone playback.</p>
Binaural auralizations of listening experiment stimuli (acoustic source models / LEV)
<p>Binaural auralizations (two-channel wav files) of loudspeaker experiment stimuli. </p>
PoD_audio_files: Audio files of the 23 works/excerpts of the Points of Discontinuity listening experiment (restricted access)
<p>This dataset contains the audio files of the 23 works/excerpts in the listening experiment that was carried out as part of the research project <em>Points of Discontinuity</em>. These audio files cannot be published in open access.</p> <p>Research data for the 23 works/excerpts can be accessed in the following zenodo datasets: </p> <ul> <li>Barrett, Natasha: Animalcules [excerpt]: <a href="https://doi.org/10.5281/zenodo.13981311" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13981311</a></li> <li>Berg, Alban: Vier Stücke für Klarinette und Klavier, Nr. 1: <a href="https://doi.org/10.5281/zenodo.13981574" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13981574</a> </li> <li>Boulez, Pierre: Le Marteau sans Maître, no. 7: <a href="https://doi.org/10.5281/zenodo.13982307" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13982307</a> </li> <li>Chen Yi: Ge Xu (Antiphony): <a href="https://doi.org/10.5281/zenodo.13982412" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13982412</a></li> <li>Feldman, Morton: Why Patterns?: <a href="https://doi.org/10.5281/zenodo.13982361" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13982361</a></li> <li>Ferneyhough, Brian: Time and Motion Study I: <a href="https://doi.org/10.5281/zenodo.13985098" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13985098</a></li> <li>Furrer, Beat: spur: <a href="https://doi.org/10.5281/zenodo.13985111" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13985111</a></li> <li>Harrison, Jonty: Klang [excerpt]: <a href="https://doi.org/10.5281/zenodo.13985494" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13985494</a></li> <li>Huber, Nicolaus A.: 6 Bagatellen für Kammerensemble, no. 6: <a href="https://doi.org/10.5281/zenodo.13985123" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13985123</a></li> <li>Kurtág, György : Quartetto per archi op. 1, no. 4: <a href="https://doi.org/10.5281/zenodo.13985139" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13985139</a></li> <li>Lachenmann, Helmut: Pression: <a href="https://doi.org/10.5281/zenodo.13985143" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13985143</a></li> <li>Lim, Liza: The Heart’s Ear [excerpt]: <a href="https://doi.org/10.5281/zenodo.13985156" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13985156</a></li> <li>Roads, Curtis: Half-life: <a href="https://doi.org/10.5281/zenodo.13987914" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13987914</a></li> <li>Saariaho, Kaija: Lichtbogen: <a href="https://doi.org/10.5281/zenodo.13985390" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13985390</a></li> <li>Schönberg, Arnold: Fünf Orchesterstücke op. 16, Nr. 1: <a href="https://doi.org/10.5281/zenodo.13985419" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13985419</a></li> <li>Scelsi, Giacinto: Quartetto no. 4: <a href="https://doi.org/10.5281/zenodo.13985403" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13985403</a></li> <li>Sciarrino, Salvatore: Sei capricci per violino, no. 1: <a href="https://doi.org/10.5281/zenodo.13985428" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13985428</a></li> <li>Spahlinger, Mathias: furioso [excerpt]: <a href="https://doi.org/10.5281/zenodo.13985439" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13985439</a></li> <li>Stockhausen, Karlheinz: Gruppen [excerpt]: <a href="https://doi.org/10.5281/zenodo.13985448" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13985448</a> </li> <li>Stravinsky, Igor: Trois pièces pour quatuor à cordes, no. 2: <a href="https://doi.org/10.5281/zenodo.13985460" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13985460</a></li> <li>Vande Gorne, Annette: Bois [excerpt]: <a href="https://doi.org/10.5281/zenodo.13985492" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13985492</a></li> <li>Varèse, Edgard: Ionisation [excerpt]: <a href="https://doi.org/10.5281/zenodo.13985468" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13985468</a></li> <li>Xenakis, Iannis: Pithoprakta: <a href="https://doi.org/10.5281/zenodo.13985480" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13985480</a></li> </ul>
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International Brain Laboratory public data
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OpenNeuro
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