Analysis and Comparison of Music Genre Classification Algorithms

Thesis Abstract

Abstract
This thesis investigates the analysis and comparison of music genre classification algorithms. Music genre classification is a fundamental task in the field of music information retrieval, with applications in recommendation systems, music organization, and content-based music retrieval. The primary objective of this study is to analyze and compare different algorithms for music genre classification to identify their strengths, weaknesses, and performance metrics. This research is motivated by the need for accurate and efficient music genre classification systems that can adapt to diverse musical styles and characteristics. The thesis begins with an introduction that outlines the background of the study, presents the problem statement, defines the objectives, discusses the limitations and scope of the study, highlights the significance of the research, and provides an overview of the thesis structure. The literature review in Chapter Two explores existing studies, methodologies, and algorithms for music genre classification, providing a comprehensive overview of the current state of the field. Chapter Three focuses on the research methodology employed in this study. This chapter details the dataset used, the preprocessing steps, feature extraction techniques, and the selection of classification algorithms. It also discusses the evaluation metrics and experimental setup used to compare the performance of the algorithms. The methodology chapter aims to provide a clear and transparent framework for conducting the research and analyzing the results. Chapter Four presents a detailed discussion of the findings obtained from the experiments conducted in this study. The chapter compares the performance of different music genre classification algorithms based on accuracy, precision, recall, F1 score, and computational efficiency. The findings highlight the strengths and weaknesses of each algorithm and provide insights into their applicability in real-world music classification tasks. Finally, Chapter Five concludes the thesis by summarizing the key findings, discussing the implications of the research, and suggesting directions for future work. The conclusion reflects on the research objectives, the contributions of the study to the field of music genre classification, and the potential impact on music information retrieval systems. Overall, this thesis aims to contribute to the advancement of music genre classification algorithms and provide valuable insights for researchers and practitioners in the field of music information retrieval.