Mind-wandering happens when one's current train of thought, related to a specific task, is interrupted, due to internal disconnected thoughts. This phenomenon is highly subjective, and its detection is really important due to the internal understanding of the human mind that can be obtained. Several methods have been used in order to detect mind-wandering, such as thought probes, self-reports, electrophysiological measures or even eye-based tracking methods, however the detection of mind-wandering solely from sound has not been researched about. Therefore, this study is going to investigate if automatic detection of mind-wandering through sound is feasible. In this work, this question is tackled through a machine learning approach, where a linear SVM model is trained through acoustic features. Two methods of oversampling are considered, due to the high data imbalance between the classes, and these two are evaluated and compared. The approach is evaluated through different metrics, such as recall, precision, F1-score and accuracy, but also a comparison with other techniques is done. Results of this work show that sound as its own is not a reliable way of automatically detecting mind-wandering. These results however, might be implementation specific, as the ground truth values of mind-wandering were created through the means of perceived mind-wandering, and the techniques of random oversampling and SMOTE were also used. These could be causes of unreliability of the research. Future work should should take this into consideration and also apply this approach to a different data set, to assess its feasibility.