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Published October 7, 2023 | Version 0.1
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Data from : Evaluating and Predicting the Audibility of Acoustic Alarms in the Workplace Using Experimental Methods and Deep Learning

Authors/Creators

  • 1. ROR icon Institut National de Recherche et de Sécurité
  • 2. ROR icon Laboratoire Lorrain de Recherche en Informatique et ses Applications
  • 3. ROR icon Centre de Recherche en Neurosciences de Lyon

Contributors

  • 1. ROR icon Institut National de Recherche et de Sécurité
  • 2. ROR icon Laboratoire Lorrain de Recherche en Informatique et ses Applications
  • 3. ROR icon Centre de Recherche en Neurosciences de Lyon

Description

This repository serves as a complementary resource accompanying the academic paper titled "Evaluating and Predicting the Audibility of Acoustic Alarms in the Workplace Using Experimental Methods and Deep Learning". It comprises detailed information regarding the alarms and backgrounds used in the psychoacoustical experiment, alongside a dataset containing the acoustic data, metadata, and perceptual annotations.

All the data will be made available shortly. 

Concurrently, the code and model weights corresponding to the neural network section discussed in the paper will be accessible via the designated GitHub repository : https://github.com/effajr/predicting_alarm_audibility

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