Machine Learning-Based Classification of Turkish Music for Mood-Driven Selection

Nazime Tokgöz; Ali Değirmenci; Ömer Karal

Article Details

Machine Learning-Based Classification of Turkish Music for Mood-Driven Selection

Authors:

Nazime Tokgöz | Ali Değirmenci | Ömer Karal

Publisher:

Çanakkale Onsekiz Mart University

Main Topics:

Computer Software

Journal:

Journal of Advanced Research in Natural and Applied Sciences Vol.10 Issue 2, pp.312-328

Document Type:

Article

Publication Date:

6/2024

Document Language:

English

Document ID (Org No.):

2001320338

pISSN:

2757-5195

Keywords:

Abstract:

 Music holds a significant role in our daily lives, and its impact on emotions has been a focal point of research across various disciplines, including psychology, sociology, and statistics. Ongoing studies continue to explore this intriguing relationship. With advancing technology, the ability to choose from a diverse range of music has expanded. Recent trends highlight a growing preference for searching for music based on emotional attributes rather than individual preferences or genres. The act of selecting music based on emotional states is important on both a universal and cultural level. This study seeks to employ machine learning-based methods to classify four different music genres using a minimal set of features. The objective is to facilitate the process of choosing Turkish music according to ones mood. The classification methods employed include Decision Tree, Random Forest RF, Support Vector Machines SVM, and k-Nearest Neighbor, coupled with the Mutual Information MI feature selection algorithm. Experimental results reveal that, with all features considered in the dataset, RF achieved the highest accuracy at 0.8098. However, when the MI algorithm was applied, SVM exhibited the best accuracy at 0.8068. Considering both memory consumption and accuracy, the RF method emerges as a favorable choice for selecting Turkish music based on emotional states. This research not only advances our understanding of the interaction between music and emotions but also provides practical insights for individuals who want to shape their music according to their emotional preferences.

 Music holds a significant role in our daily lives, and its impact on emotions has been a focal point of research across various disciplines, including psychology, sociology, and statistics. Ongoing studies continue to explore this intriguing relationship. With advancing technology, the ability to choose from a diverse range of music has expanded. Recent trends highlight a growing preference for searching for music based on emotional attributes rather than individual preferences or genres. The act of selecting music based on emotional states is important on both a universal and cultural level. This study seeks to employ machine learning-based methods to classify four different music genres using a minimal set of features. The objective is to facilitate the process of choosing Turkish music according to ones mood. The classification methods employed include Decision Tree, Random Forest RF, Support Vector Machines SVM, and k-Nearest Neighbor, coupled with the Mutual Information MI feature selection algorithm. Experimental results reveal that, with all features considered in the dataset, RF achieved the highest accuracy at 0.8098. However, when the MI algorithm was applied, SVM exhibited the best accuracy at 0.8068. Considering both memory consumption and accuracy, the RF method emerges as a favorable choice for selecting Turkish music based on emotional states. This research not only advances our understanding of the interaction between music and emotions but also provides practical insights for individuals who want to shape their music according to their emotional preferences.

 Music holds a significant role in our daily lives, and its impact on emotions has been a focal point of research across various disciplines, including psychology, sociology, and statistics. Ongoing studies continue to explore this intriguing relationship. With advancing technology, the ability to choose from a diverse range of music has expanded. Recent trends highlight a growing preference for searching for music based on emotional attributes rather than individual preferences or genres. The act of selecting music based on emotional states is important on both a universal and cultural level. This study seeks to employ machine learning-based methods to classify four different music genres using a minimal set of features. The objective is to facilitate the process of choosing Turkish music according to ones mood. The classification methods employed include Decision Tree, Random Forest RF, Support Vector Machines SVM, and k-Nearest Neighbor, coupled with the Mutual Information MI feature selection algorithm. Experimental results reveal that, with all features considered in the dataset, RF achieved the highest accuracy at 0.8098. However, when the MI algorithm was applied, SVM exhibited the best accuracy at 0.8068. Considering both memory consumption and accuracy, the RF method emerges as a favorable choice for selecting Turkish music based on emotional states. This research not only advances our understanding of the interaction between music and emotions but also provides practical insights for individuals who want to shape their music according to their emotional preferences.

 Music holds a significant role in our daily lives, and its impact on emotions has been a focal point of research across various disciplines, including psychology, sociology, and statistics. Ongoing studies continue to explore this intriguing relationship. With advancing technology, the ability to choose from a diverse range of music has expanded. Recent trends highlight a growing preference for searching for music based on emotional attributes rather than individual preferences or genres. The act of selecting music based on emotional states is important on both a universal and cultural level. This study seeks to employ machine learning-based methods to classify four different music genres using a minimal set of features. The objective is to facilitate the process of choosing Turkish music according to ones mood. The classification methods employed include Decision Tree, Random Forest RF, Support Vector Machines SVM, and k-Nearest Neighbor, coupled with the Mutual Information MI feature selection algorithm. Experimental results reveal that, with all features considered in the dataset, RF achieved the highest accuracy at 0.8098. However, when the MI algorithm was applied, SVM exhibited the best accuracy at 0.8068. Considering both memory consumption and accuracy, the RF method emerges as a favorable choice for selecting Turkish music based on emotional states. This research not only advances our understanding of the interaction between music and emotions but also provides practical insights for individuals who want to shape their music according to their emotional preferences.

Article Details

Machine Learning-Based Classification of Turkish Music for Mood-Driven Selection

Authors:

Nazime Tokgöz | Ali Değirmenci | Ömer Karal

Publisher:

Çanakkale Onsekiz Mart University

Main Topics:

Computer Software

Journal:

Journal of Advanced Research in Natural and Applied Sciences Vol.10 Issue 2, pp.312-328

Document Type:

Article

Publication Date:

6/2024

Document Language:

English

Document ID (Org No.):

2001320338

pISSN:

2757-5195

Classification

emotions

feature selection

music genres

mutual information

Issues

Article Details

Machine Learning-Based Classification of Turkish Music for Mood-Driven Selection

Authors: Nazime Tokgöz | Ali Değirmenci | Ömer Karal

Publisher: Çanakkale Onsekiz Mart University

Main Topics: Computer Software

Journal: Journal of Advanced Research in Natural and Applied Sciences Vol.10 Issue 2, pp.312-328

Document Type: Article

Publication Date: 6/2024

Document Language: English

Document ID (Org No.): 2001320338

pISSN: 2757-5195

Classification

emotions

feature selection

music genres

mutual information

Issues

Authors:

Nazime Tokgöz | Ali Değirmenci | Ömer Karal

Publisher:

Çanakkale Onsekiz Mart University

Main Topics:

Computer Software

Journal:

Journal of Advanced Research in Natural and Applied Sciences Vol.10 Issue 2, pp.312-328

Document Type:

Article

Publication Date:

6/2024

Document Language:

English

Document ID (Org No.):

2001320338

pISSN:

2757-5195