Artificial Neural Networks for a Semantic Map of Variables in a Music Listening-Based Study

Abstract

Music listening is widely used in therapeutic music-based interventions across various clinical contexts. However, relating the diverse and overlapping musical elements to their potential effects is a complex task. Furthermore, the considerable subjectivity of musical preferences and perceptual components of music, influenced by factors like cultural and musical background, personality structure of the user, and clinical aspects (in the case of diseases), adds to the difficulty. This paper analyzes data derived from a previous randomized controlled study involving a healthy population (n = 320). The study aimed to induce relaxation through music listening experiences using both conventional and algorithmic approaches. The main goal of the current research is to identify potential relationships among the variables investigated during the experiment. To achieve this, we employed the Auto Contractive Map (Auto-CM), a fourth-generation artificial neural network (ANN). This approach allows us to quantify the strength of association between each of the variables with respect to all others in the dataset. The main results highlighted that individuals who achieved a state of relaxation by listening to music composed by Melomics-Health were predominantly over 49 years old, female, and had a high level of education and musical training. Conversely, for conventional (self-selected) music, the relaxing effect was correlated with the male population, aged less than 50 years, with a high level of education and musical training. Future studies conducted in clinical settings could help identify “responder” populations based on different types of music listening approaches.