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5 results for “cocktail party”
Experimentally parametrising the cocktail party nightmare
<p><strong>Experimental data featured in the publication:</strong></p> <p><strong>Modelling active sensing reveals echo detection even in large groups of bats, Beleyur & Goerlitz 2019, Proceedings of the National Academy of Sciences</strong></p> <p>also see the associated preprint:</p> <p><strong>Active sensing in groups : (what) do bats hear in the sonar cocktail party nightmare?, Beleyur & Goerlitz 2019, BiorXiv preprint,<a href="http://https://www.biorxiv.org/content/10.1101/817734v1"> </a></strong><a href="http://https://www.biorxiv.org/content/10.1101/817734v1">https://www.biorxiv.org/content/10.1101/817734v1</a></p> <p> </p> <p>This repository has experimental data collected to :</p> <p>1) Measure bistatic and monostatic target strengths of a stuffed <em>Myotis myotis</em> bat.</p> <p>2) Measure the amount of acoustic shadowing multiple bats cause when they fly in groups.</p> <p>Please refer to the Jupyter notebooks in the 'analysis folder' for more details of the analysis and experimental workflows.</p> <p>The zipped folder has *all* audio recordings made from the start to the end of experiments. The contents of each folder, in alphabetical order:</p> <ul> <li>analysis_code : Jupyter notebooks that detail the experimental setup and analysis used to process the .WAV files in various other folders. The Jupyter notebooks provide the rationale for an experimental setup and the analysis workflows used.</li> <li> effect_of_lights : Some indoor lights can give off weird electronic and ultrasonic noise. This folder has a few recordings made to check if the lights in the experimental room would affect the recording system.</li> <li>experiment_recordings : series of recordings (raw WAV files) that were used to estimate the monostatic and bistatic target strengths of the stuffed bat, and a sphere (control object).</li> <li>how_much_Atm_abs : A folder that estimated how much of the 'shadowing' I saw (when the mic and speaker were in line with the bat in between) was from atmospheric absorption, and how of the sound level drop was actually shadowing.</li> <li>matched_filter : some trial codes - not used in any way finally</li> <li>> mic_sensitivity : Our lab had many Avisoft CM16 condenser microphones in various working conditions. This folder contains the raw data and analsis Jupyter Notebooks which describe why and how I chose the final microphone used for all proceeding experiments</li> <li>micA02_directionality : Recordings that quantify the directionality of the finally chosen Avisoft Cm16 mic with serial number A02</li> <li>multi_object_shadowing : Recordings that quantify how much sound is shadowed by the presence of bats themselves. Upto six model bats were hung in a line with microphone and speaker on either side. The amount of shadowing was calculated by the drop in recording level in comparison to no bats.</li> <li>num_instances : HRG had suggested to check the effect of running multiple instances vs one instance of Avisoft USGH recorder software. He had noticed that when recording and playback need to be done they are best done with two instances. Playback + recording worked well on just one instance with my laptop.</li> <li>shadowing : some initial calculations of how much delay would be there between the playback and echo reflecting off the bat at various mic-speaker positions.</li> <li>speaker_directionality : We had a bunch of speaker types in our lab including commercial Avisoft membrane speakers and custom-built Polaroid speakers. I quantified the differences between different models and individual speakers to see which speaker produced the loudest sound. The directionality of each speaker was also quantified. The loudest speaker was then used for all proceeding experiments. The Jupyter notebook and recordings made to determine the loudest speaker and its direcitonality is also in this folder.</li> <li> v_short_pulse : a series of trial recordings to see if the Polaroid speaker was capable of playing back very short pulses (~100 microseconds). Not used to produce any results.</li> <li> video : a series of timelapses made to broadly document the experimental setup and the process of data collection.</li> <li>which_USG416 : we had two Avisoft USG 416's lying around in the lab. I knew that one of them was glitchy and perhaps lost samples while recording. This folder contains a few recordings made to check which one had the lower background noise and if both worked.</li> <li>why_the_blips : at some point during the recordings in Raum 1.03 I began to notice a series of odd spikes in the recordings. The spikes stopped happening after some point. The recordings here were made to try and figure out where the spikes were coming from.</li> <li>Relevant files lying outside in the folder : <ul> <li>multi_obstacle_playback_v3.WAV : playback signal used in the acoustic shadowing experiment. The python module used to generate this file is missing - and was likely deleted as an oversight.</li> <li>all_pbks_stereo_1mpbk_version.wav : playback signal used in the bistatic and monostatic target strength experiment.</li> <li>make_playback_sound.py : python module used to create <em>'all_pbks_stereo_1mpbk_version.wav'</em></li> <li>Speaker+and+mic+properties.html : An initial report done detailing the speaker and microphone properties. This report was generated before start of experiments. Since there were multiple mics and speakers on hand I wanted to measure them all to understand which particular mic and speaker were suitable for the ensonification experiments.</li> </ul> </li> </ul> <p> All other files not mentioned specifically by name are a product of iterative testing over the course of the experiment.</p> <p><strong>License </strong></p> <p>All the code and data in this repository are licensed with a <a href="http://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</a> license. Please access the hyperlink for details.</p> <p><strong>Citing this dataset</strong></p> <p>If you use any of the raw data or code in this repository please cite the associated manuscript above, along with the Zenodo DOI of this repository. </p> <p><strong>Further Information </strong></p> <p>For further questions on this dataset please contact me at thejasvib@gmail.com</p> <p>Last updated : November 2019 , Thejasvi Beleyur</p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
Data from: Individual differences in selective attention predict speech identification at a cocktail party
Listeners with normal hearing show considerable individual differences in speech understanding when competing speakers are present, as in a crowded restaurant. Here, we show that one source of this variance are individual differences in the ability to focus selective attention on a target stimulus in the presence of distractors. In 50 young normal-hearing listeners, the performance in tasks measuring auditory and visual selective attention was associated with sentence identification in the presence of spatially separated competing speakers. Together, the measures of selective attention explained a similar proportion of variance as the binaural sensitivity for the acoustic temporal fine structure. Working memory span, age, and audiometric thresholds showed no significant association with speech understanding. These results suggest that a reduced ability to focus attention on a target is one reason why some listeners with normal hearing sensitivity have difficulty communicating in situations with background noise.
Data from: Individual differences in selective attention predict speech identification at a cocktail party
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Data from: Bats enhance their call identities to solve the cocktail party problem
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Cross-modal interactions at the audiovisual cocktail-party revealed by behavior, ERPs, and neural oscillations
<p>This repository contains the raw EEG data sets associated with the following publication:</p> <p>Klatt, L. I., Begau, A., Schneider, D., Wascher, E., & Getzmann, S. (2023). Cross-modal interactions at the audiovisual cocktail-party revealed by behavior, ERPs, and neural oscillations. <em>NeuroImage</em>, <em>271</em>, 120022. https://doi.org/10.1016/j.neuroimage.2023.120022</p> <p>The associated OSF repository, containing the aggregated behavioral and EEG data files (e.g., mean RTs, mean N2ac amplitudes, mean N2pc amplitudes) as well as analysis scripts and JASP files can be found here: https://osf.io/asx5v/</p> <p>Please see the README file in the OSF repository for further information regarding the shared datasets (e.g., the difference between "coded" and "coded_wo_doubleAK" datasets). The data are shared under the following licence: CC BY-NC-SA 4.0 (https://creativecommons.org/licenses/by-nc-sa/4.0/). </p> <p>To obtain access to this Zenodo repository, a data user agreement must be signed.</p>
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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