[ad_1]

Introduction

Automated music genre classification is a challenging task in the field of music information retrieval. With the exponential growth of digital music collections, the need for automated systems to organize and classify music genres has become imperative. This thesis aims to explore the various methods and techniques utilized in the automatic classification of music genres and to provide insights into the current state-of-the-art technologies in this field.

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 Music genre classification
2.2 Feature extraction techniques
2.3 Machine learning algorithms
2.4 Deep learning in music genre classification
2.5 Evaluation metrics
2.6 Challenges and limitations
2.7 Previous studies and their findings
2.8 Trends in automated music genre classification
2.9 Applications of automated music genre classification
2.10 Gaps in the existing literature

Chapter Three: Research Methodology
3.1 Data collection
3.2 Preprocessing techniques
3.3 Feature selection and extraction
3.4 Model selection
3.5 Training and testing
3.6 Evaluation methods
3.7 Performance metrics
3.8 Validation techniques

Chapter Four: Discussion of Findings
4.1 Analysis of results
4.2 Comparison with existing methods
4.3 Interpretation of findings
4.4 Implications for future research
4.5 Recommendations for practitioners
4.6 Strengths and limitations of the study
4.7 Contributions to the field
4.8 Future directions

Chapter Five: Conclusion and Summary
5.1 Summary of key findings
5.2 Contributions to knowledge
5.3 Implications for practice
5.4 Limitations of the study
5.5 Recommendations for further research
5.6 Conclusion

Thesis Overview

Automated music genre classification is a complex and challenging task in the field of music information retrieval. With the exponential growth of digital music collections, the need for efficient and accurate systems to automatically classify music genres has become increasingly important. This thesis aims to investigate the various methods and techniques used in automated music genre classification and explore the current state-of-the-art technologies in this area.

Chapter One provides an introduction to the topic, highlighting the background of the study, the problem statement, the objectives, limitations, scope, significance, and structure of the thesis. The chapter also defines key terms relevant to the research.

Chapter Two presents a comprehensive literature review on music genre classification, discussing feature extraction techniques, machine learning algorithms, deep learning approaches, evaluation metrics, challenges, previous studies, trends, applications, and gaps in the existing literature.

Chapter Three outlines the research methodology, including data collection, preprocessing techniques, feature selection, model selection, training and testing procedures, evaluation methods, performance metrics, and validation techniques.

Chapter Four discusses the findings of the study, analyzing results, comparing with existing methods, interpreting findings, discussing implications for future research, providing recommendations for practitioners, identifying strengths and limitations, highlighting contributions to the field, and suggesting future directions.

Chapter Five concludes the thesis, summarizing key findings, outlining contributions to knowledge, discussing implications for practice, identifying study limitations, proposing recommendations for further research, and offering a conclusion on the overall project on Automated music genre classification.

[ad_2]

---

Purchase Detail

Download the complete project materials to this project with Abstract, Chapters 1 – 5, References and Appendix (Questionaire, Charts, etc), Click Here to place an order via whatsapp. Got question or enquiry; Click here to chat us up via Whatsapp.
You can also call 08111770269 or +2348059541956 to place an order or use the whatsapp button below to chat us up.
Bank details are stated below.

Bank: UBA
Account No: 1021412898
Account Name: Starnet Innovations Limited

The Blazingprojects Mobile App

Download and install the Blazingprojects Mobile App from Google Play to enjoy over 50,000 project topics and materials from 73 departments, completely offline (no internet needed) with monthly update to topics, click here to install.