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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 ...
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
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.
- Alfredo Raglio, Marcello Imbriani, Chiara Imbriani, Paola Baiardi, Sara Manzoni, Marta Gianotti, Mau...
- 2020
Jan 14, 2023 · The current paper focuses on identifying and predicting whether music has therapeutic benefits. A machine learning model is developed, using a multi-class neural network to classify emotions into four categories and then predict the output.
- 10.3390/s23020986
- 2023/01
- Sensors (Basel). 2023 Jan; 23(2): 986.
Engaging in active listening and music-making activities (especially for at risk age groups) can be particularly beneficial, and the practice of music therapy has been shown to be helpful in a range of use cases across a wide age range.
Machine learning techniques to predict the effectiveness of music therapy: A randomized controlled trial Alfredo Raglio, Marcello Imbriani, Chiara Imbriani, Paola Baiardi, Sara Manzoni, Marta Gianotti, Mauro Castelli , Leonardo Vanneschi , Francisco Vico, Luca Manzoni
- Alfredo Raglio, Marcello Imbriani, Chiara Imbriani, Paola Baiardi, Sara Manzoni, Marta Gianotti, Mau...
- 2020
Increasingly, the scientific literature has shown how even listening to music related to the patient’s personal tastes (preferred music listening) and by-passing the direct relationship with the patient, can produce therapeutic effects in different clinical settings.
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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. Introduction
