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ShareScore release 0.9.0
Dataset results
3 results for “listening events”
Auditory stream segregation and selective attention for cochlear implant listeners: Evidence from behavioral measures and event-related potentials
<p>Data set generated for the study "Auditory stream segregation and selective attention for cochlear implant listeners: Evidence from behavioral measures and event-related potentials" </p> <ol> <li><strong>behavioral.txt</strong>: d' scores obtained by the listeners on the deviant detection task. <ul> <li>subject: listener ID</li> <li>distractor: Electrode separation condition</li> <li>deviant: Deviant triplet</li> <li>d: d' scores</li> <li>exp: experimental session (BEH / ERP)</li> </ul> </li> <li><strong>ERP_by_condition.txt</strong>: <ul> <li>Subject: listener ID</li> <li>Type: Sound type (Target / Distractor)</li> <li>Dev: Deviant condition. Early = deviant triplets 1 or 2. Late = deviant triplet 3 or <em>none.</em></li> <li>rep: Triplet number</li> <li>sound: sound number within the triplet</li> <li>amplitude: amplitude difference between the active and the passive listening conditions.</li> </ul> </li> </ol> <p> </p>
Deezer listening events dataset
<p><strong>What does this dataset contain?</strong></p> <p>This dataset contains over 700 million time-stamped listening events collected from 3.4M anonymised users on the music streaming service Deezer, occurred between March and August 2022. It includes 50k anonymised songs, among the most popular ones on the service as well as their pre-trained embedding vectors, calculated by our internal model. All files are in parquet format which could be read by using <code>pandas.read_parquet</code> function.</p> <p> </p> <p><strong>What could this dataset be used for?</strong></p> <p>This dataset could be used for collaborative filtering as well as sequential recommendation (including both next-item and next-session recommendations).</p> <p> </p> <p><strong>Citation</strong></p> <p>If you use this dataset, please cite following paper:</p> <pre><span>@inproceedings</span>{<span>tran-recsys2024</span>, <span>title</span>=<span><span>{</span>Transformers Meet ACT-R: Repeat-Aware and Sequential Listening Session Recommendation<span>}</span></span>, <span>author</span>=<span><span>{</span>Viet-Anh Tran, Guillaume Salha-Galvan, Bruno Sguerra and Romain Hennequin<span>}</span></span>, <span>booktitle</span> = <span><span>{</span>Proceedings of the 18th ACM Conference on Recommender Systems<span>}</span></span>, <span>year</span> = <span><span>{</span>2024<span>}</span></span> }</pre>
Bayesian Surprise Predicts Human Event Segmentation in Story Listening
<p>This repository contains data and code for the paper: <a href="https://psyarxiv.com/qd2ra/">https://psyarxiv.com/qd2ra/</a></p><p>The embeddings and outputs from GPT-2 are in the extract-embeddings-data.zip file.</p><p>The button press data is in the button-press-proportions.zip file. Button press measures computed using kernel density estimates are stored in files with the suffix "density".</p><p>The derived measures of disfluency are in the disfluency_measures.zip file. In this folder the files {story_name}_processed.csv contain the disfluency measures for each story.</p><p>The results from the analysis are in the results.zip and supplementary_results_kernel_density.zip files.</p><p>The code is available at <a href="https://github.com/manojneuro/bayesiansurprise">https://github.com/manojneuro/bayesiansurprise</a></p>
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