Audio-Tokens: A toolbox for rating, sorting and comparing audio samples in the 
browser

Donhauser, Peter W.; Klein, Denise

doi:10.3758/s13428-022-01803-w

Item

ITEM ACTIONSEXPORT

Add to Basket

Local TagsRelease HistoryDetailsSummary

Released

Journal Article

Audio-Tokens: A toolbox for rating, sorting and comparing audio samples in the browser

MPS-Authors

Donhauser, Peter W.
Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Max Planck Society;
Poeppel Lab, Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Max Planck Society;

External Resource

https://link.springer.com/article/10.3758/s13428-022-01803-w
(Publisher version)

Fulltext (restricted access)

There are currently no full texts shared for your IP range.

Fulltext (public)

Donhauser_2022_Audio-TokensAToolbox.pdf
(Publisher version), 2MB

Supplementary Material (public)

There is no public supplementary material available

Citation

Donhauser, P. W., & Klein, D. (2022). Audio-Tokens: A toolbox for rating, sorting and comparing audio samples in the browser. Behavior Research Methods. doi:10.3758/s13428-022-01803-w.

Cite as: https://hdl.handle.net/21.11116/0000-000C-858F-D

Abstract

Here we describe a JavaScript toolbox to perform online rating studies with auditory material. The main feature of the toolbox is that audio samples are associated with visual tokens on the screen that control audio playback and can be manipulated depending on the type of rating. This allows the collection of single- and multidimensional feature ratings, as well as categorical and similarity ratings. The toolbox (github.com/pwdonh/audio_tokens) can be used via a plugin for the widely used jsPsych, as well as using plain JavaScript for custom applications. We expect the toolbox to be useful in psychological research on speech and music perception, as well as for the curation and annotation of datasets in machine learning.