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.

Thesis Overview

The project titled "Analysis and Visualization of Music Emotion Recognition using Machine Learning Techniques" aims to explore the intersection of music and artificial intelligence by leveraging machine learning algorithms to analyze and visualize music emotion recognition. Music has the unique ability to evoke various emotions in listeners, and this project seeks to understand how machine learning can be utilized to identify and categorize these emotional responses. By applying advanced machine learning techniques to music data, this research will delve into the complex relationships between musical features and emotional responses. Through a comprehensive literature review, this project will examine existing studies on music emotion recognition, machine learning in music analysis, and related research in the field. By synthesizing this knowledge, the research will establish a solid foundation for the subsequent analysis and interpretation of the findings. The methodology of this project will involve collecting a diverse dataset of music tracks across different genres and styles to ensure a comprehensive analysis. The dataset will be preprocessed to extract relevant features that capture the emotional content of the music. Machine learning models, such as deep neural networks and decision trees, will be trained on this data to predict and classify emotional attributes present in the music. The findings of this research will be presented in a detailed discussion that highlights the effectiveness of machine learning techniques in music emotion recognition. The analysis will delve into the accuracy and efficiency of the models developed, as well as the insights gained from the visualization of emotional patterns in music data. In conclusion, this project will contribute to the growing body of knowledge at the intersection of music and artificial intelligence. By demonstrating the potential of machine learning in analyzing and visualizing music emotion recognition, this research aims to provide valuable insights for music researchers, industry professionals, and AI enthusiasts. Ultimately, the project seeks to enhance our understanding of how technology can be harnessed to uncover the intricacies of emotional experiences in music, paving the way for innovative applications in music analysis and creation.