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Using Deep Learning to Recognize Therapeutic Effects of Music Based on Emotions

Music is important in everyday life, and music therapy can help treat a variety of health issues. Music listening is a technique used by music therapists in various clinical treatments. As a result, music therapists must have an intelligent system at their disposal to assist and support them in sele...

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Autores principales: Modran, Horia Alexandru, Chamunorwa, Tinashe, Ursuțiu, Doru, Samoilă, Cornel, Hedeșiu, Horia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9861051/
https://www.ncbi.nlm.nih.gov/pubmed/36679783
http://dx.doi.org/10.3390/s23020986
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author Modran, Horia Alexandru
Chamunorwa, Tinashe
Ursuțiu, Doru
Samoilă, Cornel
Hedeșiu, Horia
author_facet Modran, Horia Alexandru
Chamunorwa, Tinashe
Ursuțiu, Doru
Samoilă, Cornel
Hedeșiu, Horia
author_sort Modran, Horia Alexandru
collection PubMed
description Music is important in everyday life, and music therapy can help treat a variety of health issues. Music listening is a technique used by music therapists in various clinical treatments. As a result, music therapists must have an intelligent system at their disposal to assist and support them in selecting the most appropriate music for each patient. Previous research has not thoroughly addressed the relationship between music features and their effects on patients. 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. The neural network developed has three layers: (i) an input layer with multiple features; (ii) a deep connected hidden layer; (iii) an output layer. K-Fold Cross Validation was used to assess the estimator. 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. During the training phase, a subset of the Million Dataset is used. The user selects their favorite type of music and their current mood to allow the model to make a prediction. If the selected song is inappropriate, the application, using Machine Learning, recommends another type of music that may be useful for that specific user. An ongoing study is underway to validate the Machine Learning model. The developed system has been tested on many individuals. Because it achieved very good performance indicators, the proposed solution can be used by music therapists or even patients to select the appropriate song for their treatment.
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spelling pubmed-98610512023-01-22 Using Deep Learning to Recognize Therapeutic Effects of Music Based on Emotions Modran, Horia Alexandru Chamunorwa, Tinashe Ursuțiu, Doru Samoilă, Cornel Hedeșiu, Horia Sensors (Basel) Article Music is important in everyday life, and music therapy can help treat a variety of health issues. Music listening is a technique used by music therapists in various clinical treatments. As a result, music therapists must have an intelligent system at their disposal to assist and support them in selecting the most appropriate music for each patient. Previous research has not thoroughly addressed the relationship between music features and their effects on patients. 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. The neural network developed has three layers: (i) an input layer with multiple features; (ii) a deep connected hidden layer; (iii) an output layer. K-Fold Cross Validation was used to assess the estimator. 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. During the training phase, a subset of the Million Dataset is used. The user selects their favorite type of music and their current mood to allow the model to make a prediction. If the selected song is inappropriate, the application, using Machine Learning, recommends another type of music that may be useful for that specific user. An ongoing study is underway to validate the Machine Learning model. The developed system has been tested on many individuals. Because it achieved very good performance indicators, the proposed solution can be used by music therapists or even patients to select the appropriate song for their treatment. MDPI 2023-01-14 /pmc/articles/PMC9861051/ /pubmed/36679783 http://dx.doi.org/10.3390/s23020986 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Modran, Horia Alexandru
Chamunorwa, Tinashe
Ursuțiu, Doru
Samoilă, Cornel
Hedeșiu, Horia
Using Deep Learning to Recognize Therapeutic Effects of Music Based on Emotions
title Using Deep Learning to Recognize Therapeutic Effects of Music Based on Emotions
title_full Using Deep Learning to Recognize Therapeutic Effects of Music Based on Emotions
title_fullStr Using Deep Learning to Recognize Therapeutic Effects of Music Based on Emotions
title_full_unstemmed Using Deep Learning to Recognize Therapeutic Effects of Music Based on Emotions
title_short Using Deep Learning to Recognize Therapeutic Effects of Music Based on Emotions
title_sort using deep learning to recognize therapeutic effects of music based on emotions
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9861051/
https://www.ncbi.nlm.nih.gov/pubmed/36679783
http://dx.doi.org/10.3390/s23020986
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