electronics Article Employing Subjective Tests and Deep Learning for Discovering the Relationship between Personality Types and Preferred Music Genres Aleksandra Dorochowicz 1, Adam Kurowski 1,2 and Bo˙zenaKostek 1,2,* 1 Faculty of Electronics, Telecommunications and Informatics, Multimedia Systems Department, Gdansk University of Technology, Gabriela Narutowicza 11/12, 80-233 Gda´nsk,Poland;
[email protected] (A.D.);
[email protected] (A.K.) 2 Faculty of Electronics, Telecommunications and Informatics, Audio Acoustics Laboratory, Gdansk University of Technology, Gabriela Narutowicza 11/12, 80-233 Gda´nsk,Poland * Correspondence:
[email protected]; Tel.: +48-58-3472717 Received: 13 October 2020; Accepted: 25 November 2020; Published: 28 November 2020 Abstract: The purpose of this research is two-fold: (a) to explore the relationship between the listeners’ personality trait, i.e., extraverts and introverts and their preferred music genres, and (b) to predict the personality trait of potential listeners on the basis of a musical excerpt by employing several classification algorithms. We assume that this may help match songs according to the listener’s personality in social music networks. First, an Internet survey was built, in which the respondents identify themselves as extraverts or introverts according to the given definitions. Their task was to listen to music excerpts that belong to several music genres and choose the ones they like. Next, music samples were parameterized. Two parametrization schemes were employed for that purpose, i.e., low-level MIRtoolbox parameters (MIRTbx) and variational autoencoder neural network-based, which automatically extract parameters of musical excerpts. The prediction of a personality type was performed employing four baseline algorithms, i.e., support vector machine (SVM), k-nearest neighbors (k-NN), random forest (RF), and naïve Bayes (NB).