In noisy workplaces, the audibility of acoustic alarms is essential to ensure worker safety. In practice, some criteria are required in international standards to make sure that the alarms are "clearly audible". However, the recommendations may lead to overly loud alarms, thereby exposing workers to unnecessary high sound levels, especially when ambient sound levels are high themselves. For this reason, it appears necessary to properly assess the audibility of alarms at design stage. Existing psychoacoustical methods rely on repeated subjective measurements at different sound levels and therefore require time-consuming procedures. In addition, they must be repeated each time the alarm or sound environment changes. To overcome this issue, we propose a data-driven approach to estimate the audibility of new alarm signals without having to test each new condition experimentally. In this study, a convolutional neural network model is trained to perform a binary classification task on short sound clips labeled with the outcomes of psychoacoustical experiments. We propose a proof of concept of this approach and analyze its performance depending on the data used at training and the temporal context used by the networks to predict the audibility of the alarm.