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**Introduction**

In recent years, there has been a growing interest in using machine learning techniques for music genre classification. The ability to automatically categorize music into different genres has numerous practical applications in music recommendation systems, music streaming services, and content tagging. This thesis aims to explore the use of machine learning algorithms for music genre classification and to investigate the effectiveness of different feature extraction and classification methods in this context.

**Table of Contents**

**Chapter 1: Introduction**

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

**Chapter 2: Literature Review**

2.1 Overview of Music Genre Classification
2.2 Machine Learning Techniques for Music Genre Classification
2.3 Feature Extraction Methods
2.4 Classification Algorithms
2.5 Evaluation Metrics
2.6 Previous Studies on Music Genre Classification
2.7 Challenges and Limitations
2.8 Future Research Directions

**Chapter 3: Research Methodology**

3.1 Data Collection
3.2 Pre-processing of Music Data
3.3 Feature Extraction
3.4 Feature Selection
3.5 Model Selection
3.6 Model Training
3.7 Model Evaluation
3.8 Experimental Setup

**Chapter 4: Discussion of Findings**

4.1 Comparative Analysis of Feature Extraction Methods
4.2 Impact of Classification Algorithms on Performance
4.3 Effects of Feature Selection on Model Accuracy
4.4 Interpretation of Results
4.5 Discussion on Experimental Findings

**Chapter 5: Conclusion and Summary**

5.1 Summary of Findings
5.2 Conclusion
5.3 Recommendations for Future Research

**Thesis Overview**

The use of machine learning algorithms for music genre classification has gained significant attention due to its potential impact on various music-related applications. This thesis will delve into the exploration of different machine learning techniques, including feature extraction methods and classification algorithms, in the context of music genre classification. The study will also focus on evaluating the performance of these methods and identifying the challenges and limitations associated with them.

Chapter 1 provides an introduction to the research topic, outlining the background, problem statement, objectives, limitations, scope, significance, and structure of the thesis. Chapter 2 offers an in-depth literature review on music genre classification, machine learning techniques, feature extraction methods, classification algorithms, evaluation metrics, previous studies, challenges, and future research directions.

Chapter 3 details the research methodology, including data collection, pre-processing, feature extraction, selection, model training, evaluation, and the experimental setup. Chapter 4 presents a discussion of findings, analyzing the impact of different factors on the performance of music genre classification models. Finally, Chapter 5 concludes the thesis, summarizing the findings, drawing conclusions, and providing recommendations for future research in the field of machine learning for music genre classification.

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