# Artificial Neural Networks for a Semantic Map of Variables in a Music Listening-Based Study
## 2 Materials and Methods  
The study analyzed data from a previous research study [23], which aimed to evaluate the short-term effects of listening to conventional self-selected music compared to algorithmic music. In this study, three hundred and twenty healthy participants were recruited and divided into two groups based on musical training or practice, stratified by sex, age, and education. Each final subgroup was then randomized to either conventional or Melomics-Health music listening. Before starting the experiment, the researchers assessed the participants for any health-related problems and excluded individuals with deafness/hyperacusis issues, severe neurological/psychiatric diseases in the last year, and those showing a low level of cooperation or refusal. To evaluate the participants' relaxation levels, a Visual Analogue Scale (VAS) was used. The variation in relaxation levels, obtained by comparing VAS scores before and after music listening, was considered the dependent variable, with three possible categories: increase, decrease, or no variation. After randomization, the participants listened to Melomics-Health or conventional self-selected music (using earphones) in a comfortable setting. Each music listening session lasted about 9 min. In the conventional music listening group, before the experiment, each subject was asked to select a list of 2-3 preferred relaxing pieces of music. In the Melomics-Health study, participants listened to three musical compositions created via the Melomics-Health algorithm. These melodies were specifically composed using music parameters selected by the researchers, aimed at promoting relaxation and deactivation for therapeutic purposes. In the present study, bypassing results concerning the relaxation levels obtained by different types of music listening (conventional or algorithmic), the connection between variables related to the listeners was analyzed to determine the characteristics of \"responders\" to two kinds of music stimuli. The Melomics-Health group is utilizing algorithmic music generated through Melomics-Health technology. This music is presently being tested to create relaxing therapeutic compositions, particularly beneficial for addressing temporary symptoms commonly encountered in clinical scenarios, such as pre-surgical anxiety, stress, and pain. The Melomics-Health compositions are constructed as a sequence of fragments, each defined independently by various parameters:  
* Timbre: indicating the instrument's name;
* Pitch range: representing the lowest and highest notes achievable within the instrument's range (spanning from C0 to B7);
* Time signature: denoting the rhythm with options like 2/4, 3/4, 4/4, 3/8, 6/8, 12/8, and other supported signatures;
* Key: specified with the tonic's name, along with the minus or plus symbol to indicate minor or major scales, and the corresponding notes of the scale in scientific pitch notation (A to G, with sharps or flats where applicable). If a note refers to a specific octave, its octave number is also indicated;
* Tempo: measured in beats per minute (bpm), equivalent to adagio, andante, allegro, etc. ;
* Intervals: indicating the allowable pitch differences between notes (e.g., 2-3, 2-4);
* Rhythm: representing the density of note durations, expressed as probabilities for each duration;
* Dynamics: indicated by terms like \"piano\" (p) or \"mezzoforte\" (mf);
* Duration: specifying the overall length of the fragment in seconds. The process of creating each fragment's musical structure involves dividing its total duration into beats based on the tempo. This resembles allocating a table with all possible note durations (according to the various parameters) present and then selecting concrete notes using a stochastic process [24]. Thus, notes are selected based on their probabilities, which is also part of the parameters.