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- The resulting decision tree and analysis of this interpretable model makes it possible to find predictive factors that influence therapeutic music listening outcomes. The strong subjectivity of therapeutic music listening suggests the use of machine learning techniques as an important and innovative approach to supporting music therapy practice.
www.sciencedirect.com/science/article/pii/S0169260719301555Machine learning techniques to predict the effectiveness of ...
Jan 14, 2023 · The experiment aims to create a machine-learning model that can predict whether a specific song has therapeutic effects on a specific person. The model considers a person’s musical and emotional characteristics but is also trained to consider solfeggio frequencies.
Jan 14, 2023 · The experiment aims to create a machine-learning model that can predict whether a specific song has therapeutic effects on a specific person. The model considers a person’s musical...
Mar 1, 2020 · The present study compared algorithmic music (based on sonorous music features aimed at therapeutic effects) and individualized music listening (based on subjective choices) to find possible predictivity factors of effectiveness related to these two types of music listening approaches.
- Alfredo Raglio, Marcello Imbriani, Chiara Imbriani, Paola Baiardi, Sara Manzoni, Marta Gianotti, Mau...
- 2020
Oct 1, 2019 · This paper introduces the development of a machine learning powered music therapy platform, SOJO AI, which personalizes therapeutic music sessions based on user feedback and data.
Here, we present a study to establish the main predictive factors of music listening's relaxation effects using machine learning methods. Methods: Three hundred and twenty healthy participants were evenly distributed by age, education level, presence of musical training, and sex.
- Alfredo Raglio, Marcello Imbriani, Chiara Imbriani, Paola Baiardi, Sara Manzoni, Marta Gianotti, Mau...
- 2020
Mar 1, 2020 · Highlights. Music listening is widely used in clinical settings to modulate arousal levels. Which and how music can be selected in therapeutic treatments are unclear points. Decision trees enable finding therapeutic predictivity factors of music interventions in healthy participants.
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Can machine learning predict music listening's relaxation effects?
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The literature shows the effectiveness of music listening, but which factors and what types of music produce therapeutic effects, as well as how music therapists can select music, remain unclear. Here, we present a study to establish the main predictive factors of music listening's relaxation effects using machine learning methods.
