Cargando…
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...
Autores principales: | , , , , |
---|---|
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 |
_version_ | 1784874744810569728 |
---|---|
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. |
format | Online Article Text |
id | pubmed-9861051 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT modranhoriaalexandru usingdeeplearningtorecognizetherapeuticeffectsofmusicbasedonemotions AT chamunorwatinashe usingdeeplearningtorecognizetherapeuticeffectsofmusicbasedonemotions AT ursutiudoru usingdeeplearningtorecognizetherapeuticeffectsofmusicbasedonemotions AT samoilacornel usingdeeplearningtorecognizetherapeuticeffectsofmusicbasedonemotions AT hedesiuhoria usingdeeplearningtorecognizetherapeuticeffectsofmusicbasedonemotions |