Analysis and Visualization of Music Emotion Recognition using Machine Learning Techniques

Thesis Abstract

Abstract
This thesis presents a comprehensive study on the analysis and visualization of music emotion recognition using machine learning techniques. The ability to automatically recognize and interpret emotions expressed in music has significant implications for various applications, including music recommendation systems, personalized music playlists, and emotional analysis of music content. The use of machine learning algorithms offers a promising approach to address the complexities of music emotion recognition by leveraging patterns and features extracted from music data. Chapter 1 provides an introduction to the research topic, highlighting the background of the study, the problem statement, objectives of the study, limitations, scope, significance of the study, and the structure of the thesis. The chapter also includes definitions of key terms related to music emotion recognition and machine learning techniques. Chapter 2 presents a comprehensive literature review on music emotion recognition and machine learning techniques. The chapter discusses relevant studies, methodologies, and approaches in the field, providing a foundation for the research study. Chapter 3 details the research methodology employed in this study. The chapter covers the data collection process, feature extraction techniques, machine learning algorithms used for emotion recognition, evaluation metrics, and experimental setup. Additionally, it discusses the steps taken to preprocess the music data and optimize the model performance. Chapter 4 presents an elaborate discussion of the findings obtained from the experiments conducted in this study. The chapter includes the analysis of the results, the performance of the machine learning models in music emotion recognition tasks, and the visualization techniques used to interpret the emotional content of music. Chapter 5 concludes the thesis by summarizing the key findings, implications of the research study, and recommendations for future work. The chapter also discusses the potential applications of music emotion recognition using machine learning techniques and the significance of the study in advancing the field of music technology. In conclusion, this thesis contributes to the growing body of research on music emotion recognition by exploring the effectiveness of machine learning techniques in analyzing and visualizing emotional content in music. The findings of this study provide valuable insights into the potential applications of music emotion recognition systems and pave the way for further advancements in the field.