Thesis Abstract

Abstract
Music genre classification is an essential 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 and comparison of various music genre classification algorithms to explore their effectiveness in accurately categorizing music into different genres. The study aims to provide insights into the performance, strengths, and limitations of these algorithms, ultimately guiding the development of more robust and accurate music genre classification systems. Chapter 1 provides the introduction to the research topic, background information, problem statement, objectives, limitations, scope, significance of the study, and the structure of the thesis. The chapter also includes definitions of key terms to establish a common understanding of the concepts discussed throughout the thesis. The introduction sets the stage for the subsequent chapters by outlining the research goals and context within which the study is conducted. Chapter 2 comprises a comprehensive literature review that examines existing research on music genre classification algorithms. The review covers various approaches, techniques, and methodologies used in music genre classification, highlighting their strengths, weaknesses, and potential areas for improvement. By synthesizing the findings from previous studies, this chapter establishes a solid foundation for the research methodology and analysis presented in subsequent chapters. Chapter 3 details the research methodology employed in this study, including data collection, preprocessing techniques, feature extraction methods, algorithm selection, model training, and evaluation metrics. The chapter outlines the experimental setup and procedures used to compare the performance of different music genre classification algorithms, ensuring a systematic and rigorous evaluation process. Chapter 4 presents a detailed discussion of the findings obtained from the experimental evaluation of the music genre classification algorithms. The chapter analyzes the performance metrics, accuracy, precision, recall, and F1 score of each algorithm, providing insights into their comparative effectiveness in classifying music genres. The discussion also explores the impact of various factors, such as feature selection, model complexity, and dataset size, on the classification performance. Chapter 5 concludes the thesis by summarizing the key findings, implications of the research, and recommendations for future work in the field of music genre classification. The chapter highlights the contributions of the study, identifies its limitations, and suggests avenues for further research to enhance the accuracy and robustness of music genre classification algorithms. In conclusion, this thesis contributes to the ongoing research efforts in music genre classification by conducting a comprehensive analysis and comparison of different algorithms. The findings of this study provide valuable insights for researchers, developers, and practitioners working in the field of music information retrieval, paving the way for the development of more effective and efficient music genre classification systems.

Thesis Overview