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Introduction

In today’s digital age, music streaming services have become increasingly popular, allowing users to access a vast array of songs and create personalized playlists. However, many users struggle with the time-consuming task of curating playlists that align with their mood or desired emotional experience. Music emotion recognition (MER) technology has emerged as a potential solution to this problem, with the ability to analyze the emotional content of music and generate playlists tailored to specific emotions. This thesis aims to explore the application of MER for playlist curation, investigating how this technology can enhance the music listening experience for users.

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 One: Introduction
– Introduction
– Background of Study
– Problem Statement
– Objective of Study
– Limitation of Study
– Scope of Study
– Significance of Study
– Structure of the Thesis
– Definition of Terms

Chapter Two: Literature Review
– Overview of Music Emotion Recognition
– Music and Emotion Processing
– Applications of Music Emotion Recognition
– Playlist Curation and User Experience
– Existing MER Algorithms
– Evaluation Metrics
– Challenges in MER Technology
– User Preferences in Music
– Emotion Representation in Music
– Commercial Applications of MER

Chapter Three: Research Methodology
– Research Design
– Data Collection
– Feature Extraction
– Machine Learning Models
– Evaluation Process
– User Studies
– Ethical Considerations
– Data Analysis Techniques

Chapter Four: Discussion of Findings
– Analysis of MER Algorithms
– Comparison of Playlist Curation Methods
– User Feedback and Recommendations
– Implications for Music Streaming Services
– Future Research Directions
– Practical Implementation Considerations
– Limitations of the Study

Chapter Five: Conclusion and Summary
– Summary of Findings
– Contributions to the Field
– Practical Recommendations
– Conclusion
– Future Research Directions

Thesis Overview

This thesis explores the application of Music Emotion Recognition (MER) technology for playlist curation, aiming to enhance the music listening experience for users. The introduction provides background information on the study, outlining the problem statement, objectives, limitations, scope, significance, and structure of the thesis. Chapter one also defines key terms related to MER and playlist curation.

The literature review in chapter two offers an overview of existing research on MER, music and emotion processing, playlist curation, and user preferences in music. It discusses the applications of MER technology, existing algorithms, evaluation metrics, challenges, and commercial applications. Chapter two sets the foundation for the research methodology in chapter three, detailing the research design, data collection, feature extraction, machine learning models, evaluation process, user studies, ethical considerations, and data analysis techniques.

Chapter four presents a thorough discussion of the research findings, analyzing MER algorithms, playlist curation methods, user feedback, implications for music streaming services, and future research directions. It also highlights practical implementation considerations and limitations of the study. The conclusion and summary in chapter five provide a concise overview of the findings, contributions to the field, practical recommendations, and future research directions.

Overall, this thesis aims to expand the knowledge and understanding of employing MER technology for playlist curation, offering insights into how this technology can revolutionize the music streaming experience for users.

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