Analysis and Visualization of Music Emotion Recognition using Machine Learning Techniques

Thesis Abstract

Abstract
The ability to understand and interpret human emotions through music is a complex and fascinating area of research. In recent years, advancements in machine learning techniques have enabled the development of algorithms that can automatically recognize and analyze emotional content in music. This thesis presents a comprehensive study on the analysis and visualization of music emotion recognition using machine learning techniques. Chapter 1 introduces the research topic by providing an overview of the background of the study, defining the problem statement, stating the objectives of the study, discussing the limitations and scope of the research, highlighting the significance of the study, outlining the structure of the thesis, and defining key terms. Chapter 2 conducts a thorough literature review on music emotion recognition, machine learning algorithms, and visualization techniques. This chapter explores existing studies, methodologies, and tools used in the field, providing a solid foundation for the research. Chapter 3 details the research methodology employed in this study. It covers the data collection process, feature extraction techniques, machine learning models used for emotion recognition, evaluation metrics, and data visualization methods. The chapter also discusses the experimental setup and data analysis procedures. Chapter 4 presents the findings of the study, including the performance evaluation of different machine learning models for music emotion recognition, visualization of emotional content in music, and comparison of results with existing studies. The chapter provides detailed discussions and analysis of the results obtained from the experiments conducted. Chapter 5 concludes the thesis by summarizing the key findings, discussing the implications of the research, and suggesting future directions for further research in the field of music emotion recognition using machine learning techniques. The conclusion also highlights the contributions of the study to the existing body of knowledge and its potential applications in real-world scenarios. Overall, this thesis contributes to the growing body of research on music emotion recognition by providing insights into the application of machine learning techniques for analyzing and visualizing emotional content in music. The findings of this study have the potential to enhance our understanding of how emotions are expressed and perceived through music, opening up new possibilities for the development of intelligent systems that can interact with users on an emotional level.