Analysis and Comparison of Music Genre Classification Algorithms

Thesis Abstract

Abstract
The classification and categorization of music genres play a crucial role in various applications such as music recommendation systems, music retrieval, and music streaming platforms. This thesis presents a comprehensive analysis and comparison of different music genre classification algorithms to identify the most effective approach in accurately categorizing music into specific genres. The study focuses on evaluating the performance of machine learning algorithms in classifying music genres based on audio features. Chapter 1 provides an introduction to the research topic, highlighting the background of the study, the problem statement, objectives, limitations, scope, significance, structure of the thesis, and definitions of key terms. Chapter 2 presents a detailed literature review encompassing ten key aspects related to music genre classification algorithms, including existing approaches, challenges, and recent advancements in the field. Chapter 3 outlines the research methodology employed in this study, detailing the data collection process, feature extraction techniques, algorithm selection criteria, evaluation metrics, and experimental design. The methodology chapter also discusses the preprocessing steps and model training procedures used to compare the performance of different classification algorithms. In Chapter 4, the findings of the study are extensively discussed, analyzing the results obtained from the experiments conducted on various music datasets. The chapter includes a comparative analysis of the classification accuracy, computational efficiency, and robustness of different algorithms in classifying music genres. The discussion also addresses the strengths and limitations of each approach, providing insights into the effectiveness of different classification techniques. Finally, Chapter 5 presents the conclusion and summarizes the key findings of the research. The chapter discusses the implications of the study results, highlights the significance of the research findings, and offers recommendations for future research in the field of music genre classification algorithms. The conclusion section also reflects on the research objectives, discusses the contributions of the study to the existing literature, and suggests potential areas for further exploration. Overall, this thesis contributes to the advancement of music genre classification research by conducting a thorough analysis and comparison of various algorithms, providing valuable insights into the performance and effectiveness of different classification techniques. The findings of this study have implications for improving music recommendation systems, enhancing music retrieval accuracy, and optimizing music streaming platforms for better user experience.