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Introduction

Music genre classification and recommendation is an important area of research in the field of music information retrieval. With the vast amount of music available online, users often struggle to find music that matches their preferences. Automatic genre classification and recommendation systems can help users discover new music that they are likely to enjoy, based on their listening history and preferences. This thesis aims to explore the current state of the art in music genre classification and recommendation, and propose novel approaches to improve the accuracy and effectiveness of these systems.

Chapter One: Introduction
1.1 Introduction
1.2 Background of study
1.3 Problem Statement
1.4 Objective of study
1.5 Limitation of study
1.6 Scope of study
1.7 Significance of study
1.8 Structure of the Thesis
1.9 Definition of Terms

Chapter Two: Literature Review
2.1 Overview of Music genre classification
2.2 Approaches to Music genre classification
2.3 Music genre recommendation systems
2.4 Machine learning techniques for genre classification
2.5 Evaluation metrics for genre classification
2.6 Challenges in music genre classification and recommendation
2.7 Related work in the field
2.8 Comparison of existing systems
2.9 Future trends in genre classification and recommendation
2.10 Gaps in the current research

Chapter Three: Research Methodology
3.1 Research design
3.2 Data collection
3.3 Data pre-processing
3.4 Feature extraction
3.5 Classification algorithms
3.6 Evaluation methodology
3.7 Performance metrics
3.8 Experimental setup

Chapter Four: Discussion of Findings
4.1 Results of genre classification experiments
4.2 Analysis of classification performance
4.3 Comparison of different algorithms
4.4 Recommendations for improving genre classification accuracy
4.5 Challenges and limitations
4.6 Future research directions
4.7 Implications for music recommendation systems

Chapter Five: Conclusion and Summary
5.1 Summary of key findings
5.2 Contributions to the field
5.3 Implications for music industry
5.4 Limitations and future work
5.5 Conclusion

Thesis Overview

Music genre classification and recommendation are crucial tasks in the field of music information retrieval. This thesis aims to explore the current state of the art in genre classification and recommendation systems, and propose novel approaches to improve their accuracy and effectiveness. Chapter one provides an introduction to the topic, outlining the background, problem statement, objectives, limitations, scope, significance, and structure of the thesis. Chapter two reviews the existing literature on music genre classification and recommendation, discussing different approaches, techniques, evaluation metrics, challenges, and future trends. Chapter three presents the research methodology, detailing the research design, data collection, pre-processing, feature extraction, classification algorithms, evaluation methodology, and experimental setup. Chapter four discusses the findings of genre classification experiments, analyzing classification performance, comparing different algorithms, and making recommendations for improvement. Chapter five concludes the thesis, summarizing key findings, contributions, implications, limitations, and future research directions. This thesis aims to advance the field of music genre classification and recommendation, helping users discover music that aligns with their preferences and interests.

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