Analysis of Music Genre Classification Techniques using Machine Learning Algorithms

Thesis Abstract

Abstract
Music genre classification is a fundamental task in the field of music information retrieval, with applications ranging from music recommendation systems to music streaming platforms. This thesis presents an in-depth analysis of various music genre classification techniques using machine learning algorithms. The study aims to explore the effectiveness of different methods in accurately categorizing music into distinct genres. Chapter One provides an introduction to the research topic, including the background of the study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of key terms. The chapter sets the foundation for understanding the importance of music genre classification and the need for advanced techniques to improve classification accuracy. Chapter Two comprises a comprehensive literature review that examines existing research on music genre classification techniques. The chapter covers ten key areas, including feature extraction methods, machine learning algorithms, evaluation metrics, dataset selection, and genre taxonomy. By reviewing the current state-of-the-art approaches, this chapter aims to identify gaps in the literature and potential areas for improvement. Chapter Three outlines the research methodology employed in this study. It includes detailed descriptions of data collection, preprocessing techniques, feature extraction methods, model selection, parameter tuning, evaluation procedures, and performance metrics. The chapter also discusses the experimental setup and validation strategies used to assess the effectiveness of the classification techniques. Chapter Four presents a detailed discussion of the findings obtained from the experimental evaluation. The chapter analyzes the performance of various machine learning algorithms, such as Support Vector Machines, Random Forest, and Neural Networks, in classifying music genres. It also compares the results of different feature extraction techniques and parameter settings to determine the most effective approach for genre classification. Chapter Five serves as the conclusion and summary of the thesis. It highlights the key findings, contributions, and implications of the research. The chapter discusses the limitations of the study, future research directions, and potential applications of the proposed classification techniques in real-world scenarios. The thesis concludes with a reflection on the significance of the findings and their impact on the field of music genre classification using machine learning algorithms. Overall, this thesis provides a comprehensive analysis of music genre classification techniques using machine learning algorithms. By exploring the effectiveness of various methods and evaluating their performance, this study contributes to the advancement of music information retrieval systems and enhances our understanding of genre classification in the context of music analysis and recommendation systems.