Automatic identification of preferred music genres: an exploratory machine learning approach to support personalized music therapy

Music accompanies all phases of our lives, and when we reach old age, music becomes a direct symbol of nostalgia. Autobiographical memories are essential to an individual's sense of identity, continuity, and meaning. But some pathologies, such as dementia, can interrupt the memory storage process. Music can help recall and evoke memories and can be used in alternative treatments for dementia. This work aims to propose an architecture for a music recommendation system capable of recommending music according to musical genre, with the aim of helping music therapists in therapies addressed to elderly people with dementia in initial states. Here we used data from the public music database Emotify, which is composed of 400 songs labeled by 1595 participants in 7975 sessions. Both channels of the songs were windowed using 10s windows with 5s overlap. The data from these windows were represented by 34 time and frequency features. Then, we assessed and compared the performance of classifiers based on support vector machines, decisions trees and Bayesian network. The most suitable architecture in this experimental study was the Random Forest with 250 trees, with an accuracy of 83.42% +/- 1.72%, kappa statistic of 0.78 +/- 0.02, AUC-ROC of 0.99 +/- 0.00, sensitivity of 0.96 +/- 0.02, and specificity of 0.94 +/- 0.01. this exploratory study found promising results that indicates the possibility of building recommendation systems to support music therapy based on the automatic classification of songs according to the most appropriate musical genre for the patient.