# 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. music listening; music therapy; algorithmic music; Melomics-Health; artificial neural network; semantic connectivity map +
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cultural components. It creates musical pieces (primarily melodic lines) that do not strictly adhere to conventional musical rules. Such melodies can also be modulated, adjusted, and adapted according to therapeutic needs and user responses. Melomics-Health is an algorithm designed for creating therapeutic music. Its creation aims to establish a connection between musical structures and parameters and their potential therapeutic effects. This algorithm has been tested in various clinical settings [7; 19; 20; 21], mainly for relaxation/deactivation, activation, or distraction purposes. A recent study [22] has also demonstrated the physiological impact (on the parameters of the cardiovascular autonomic system) of Melomics-Health songs, indicating different and opposing effects depending on whether the musical parameters of the composed songs were deactivating (relaxing) or activating. In addition to these experiments, several studies have been conducted using machine learning techniques to identify potential predictive indicators of success in achieving a deactivating/relaxing state. These indicators are applicable to both algorithmic and conventional self-selected music listening programs, where subjects select their own songs. These techniques provide crucial support for therapists, as they guide music choices based on therapeutic goals and help reduce the high level of subjectivity associated with the effects produced by musical stimuli. The current study aligns with this perspective and aims to identify the variables connected to the main outcome (improvement in relaxation) related to an experiment involving both conventional and algorithmic music listening approaches [23]. We present a general methodology for this type of analysis, utilizing neural networks to generate semantic connectivity maps that illustrate the relationships between various factors related to the music listening experience. Specifically, we analyzed the correlation between the study's variables and the relaxation condition for both self-selected and algorithmic music listening experiences.