Thesis Abstract

Abstract
Music has the ability to evoke various emotions in listeners, making it a powerful tool for communication and expression. Understanding the emotional content of music is crucial for applications such as music recommendation systems, personalized playlists, and music therapy. In recent years, machine learning algorithms have been increasingly used to analyze and recognize emotions in music. This thesis presents a comprehensive analysis of music emotion recognition using machine learning algorithms. The study begins with an introduction to the importance of emotion recognition in music and provides a background of the research area. The problem statement highlights the challenges in accurately identifying emotions in music, while the objectives of the study outline the specific goals and aims of the research. The limitations and scope of the study are also discussed, setting the boundaries and focus of the research. The significance of the study is emphasized, demonstrating the potential impact of the research on various applications in the music industry. Chapter two presents a detailed literature review of existing studies and methodologies related to music emotion recognition and machine learning algorithms. The review covers key concepts, theories, and approaches in the field, providing a comprehensive overview of the current state of research in music emotion recognition. Chapter three outlines the research methodology, including the data collection process, feature extraction techniques, and machine learning algorithms used for emotion recognition in music. The chapter also discusses the evaluation metrics and methods employed to assess the performance of the emotion recognition system. Chapter four presents the findings of the study, including the experimental results and analysis of the performance of the machine learning algorithms in recognizing emotions in music. The chapter discusses the accuracy, precision, and recall rates of the emotion recognition system, highlighting the strengths and limitations of the approach. Finally, chapter five provides a conclusion and summary of the research thesis, presenting the key findings, contributions, and implications of the study. The conclusion discusses the significance of the research in advancing the field of music emotion recognition and suggests future research directions to further enhance the accuracy and effectiveness of emotion recognition systems in music. Overall, this thesis contributes to the growing body of knowledge in music emotion recognition and demonstrates the potential of machine learning algorithms in analyzing and recognizing emotions in music. The findings of this study have implications for various applications in the music industry, including music recommendation systems, emotional playlist generation, and personalized music therapy interventions.

Thesis Overview