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Introduction

Music plays a significant role in our daily lives, influencing our emotions, moods, and behavior. With the advancement of technology, music streaming platforms have become increasingly popular, offering users access to millions of songs at their fingertips. However, the abundance of music choices can be overwhelming, making it challenging for users to curate personalized playlists that cater to their specific emotional needs.

Music emotion recognition (MER) is a field of study that aims to develop algorithms capable of automatically detecting emotions in music. By leveraging computer algorithms to analyze music features such as tempo, rhythm, and pitch, researchers can identify the emotional content of a song and categorize it into different emotional categories such as happy, sad, energetic, or calming. This technology has the potential to revolutionize the way playlists are curated, allowing users to create personalized playlists based on their current emotional state or desired mood.

This thesis explores the application of music emotion recognition for playlist curation, investigating how MER can be used to enhance the user experience on music streaming platforms. By analyzing the emotional content of music, this research aims to provide personalized playlist recommendations that align with users’ emotional preferences, ultimately improving user satisfaction and engagement with music streaming services.

Table of Contents

Chapter 1: Introduction
1.1 Introduction
1.2 Background of study
1.3 Problem Statement
1.4 Objective of study
1.5 Limitation of study
1.6 Scope of study
1.7 Significance of study
1.8 Structure of the Thesis
1.9 Definition of Terms

Chapter 2: Literature Review
2.1 Overview of Music Emotion Recognition
2.2 Methods and Techniques for Emotion Detection in Music
2.3 Applications of Music Emotion Recognition in Playlist Curation
2.4 User Preferences in Music Listening
2.5 Personalization Algorithms in Music Streaming Platforms
2.6 Challenges and Limitations in Music Emotion Recognition
2.7 Previous Studies on Music Emotion Recognition for Playlist Curation
2.8 Impact of Music Emotion Recognition on User Experience
2.9 Future Directions in Music Emotion Recognition Research

Chapter 3: Research Methodology
3.1 Research Design
3.2 Data Collection
3.3 Feature Extraction
3.4 Emotion Classification Model
3.5 Evaluation Metrics
3.6 Experimental Setup
3.7 Data Analysis Techniques
3.8 Ethical Considerations

Chapter 4: Discussion of Findings
4.1 Overview of Dataset
4.2 Feature Extraction Results
4.3 Emotion Classification Accuracy
4.4 Comparison with Existing Methods
4.5 Implications for Playlist Curation
4.6 User Feedback and Satisfaction
4.7 Limitations of the Study
4.8 Recommendations for Future Research

Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Contributions to the Field
5.3 Practical Implications
5.4 Conclusion
5.5 Future Research Directions

This thesis aims to contribute to the growing body of knowledge on music emotion recognition and its potential applications in playlist curation. By exploring the intersection of music, emotion, and technology, this research seeks to enhance the user experience on music streaming platforms and provide personalized recommendations that resonate with users on an emotional level.

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