Thesis Abstract

Abstract
The rapid growth of digital music content has led to an increasing need for efficient music genre classification algorithms. This thesis presents an in-depth analysis and comparison of various algorithms used for music genre classification. The primary objective of this research is to evaluate the performance of different algorithms in accurately classifying music into predefined genres. The study begins with an introduction that outlines the background of the research, highlights the problem statement, and defines the objectives of the study. The limitations and scope of the research are also discussed, along with the significance of the study in the field of music classification. The structure of the thesis is outlined to provide a roadmap for the reader, and key terms are defined to ensure clarity throughout the document. Chapter two presents a comprehensive literature review that covers ten essential aspects related to music genre classification algorithms. This section explores existing research, methodologies, and algorithms used in music genre classification, providing a foundation for the subsequent analysis and comparison. Chapter three details the research methodology employed in this study. Eight key components are discussed, including data collection methods, feature extraction techniques, preprocessing steps, algorithm selection criteria, evaluation metrics, parameter tuning, and experimental design. This chapter serves as a guide to the methodology used in evaluating the performance of music genre classification algorithms. Chapter four presents an elaborate discussion of the findings obtained from the evaluation of various classification algorithms. The results are analyzed, and the strengths and weaknesses of each algorithm are highlighted. The chapter also includes comparisons of algorithm performance based on accuracy, efficiency, and robustness in classifying music genres. Finally, chapter five provides a conclusion and summary of the thesis. The key findings of the research are summarized, and recommendations for future work are discussed. The implications of the study on the field of music genre classification are also highlighted, along with potential areas for further research and development. In conclusion, this thesis contributes to the advancement of music genre classification algorithms by providing a comprehensive analysis and comparison of different approaches. The findings of this research can inform the development of more accurate and efficient algorithms for classifying music genres in digital content platforms.

Thesis Overview