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.

Thesis Overview

The project titled "Analysis and Comparison of Music Genre Classification Algorithms" aims to investigate and evaluate various algorithms used in the classification of music genres. Music genre classification is a fundamental task in music information retrieval, with applications in music recommendation systems, music streaming platforms, and music analysis. The project will focus on analyzing the performance of different machine learning algorithms in accurately classifying music into predefined genres. The research will begin with a comprehensive literature review to explore existing studies, algorithms, and techniques in music genre classification. This review will provide a foundation for understanding the current state of the art in this field and identify potential gaps or areas for improvement. Following the literature review, the project will delve into the research methodology, which will involve collecting and preprocessing a diverse dataset of music tracks across various genres. Feature extraction techniques will be applied to represent the audio content in a format suitable for machine learning algorithms. Different classification algorithms, such as Support Vector Machines, Random Forest, and Neural Networks, will be implemented and evaluated based on their performance metrics, including accuracy, precision, recall, and F1 score. The research findings will be presented in a detailed discussion that compares the strengths and weaknesses of the different classification algorithms in classifying music genres. The analysis will highlight the effectiveness of each algorithm in accurately categorizing music and provide insights into the factors influencing their performance. In conclusion, the project will summarize the key findings, implications, and contributions to the field of music genre classification. The significance of the study lies in its potential to enhance the accuracy and efficiency of music genre classification algorithms, ultimately improving user experience in music recommendation and discovery platforms. By analyzing and comparing various algorithms, this research aims to advance the understanding of music genre classification techniques and pave the way for future developments in this area.