Analysis and Classification of Music Genres Using Machine Learning Algorithms

Thesis Abstract

Abstract
The rapid advancement of machine learning algorithms has opened up new avenues for research in various domains, including music analysis and classification. This thesis investigates the application of machine learning algorithms in the analysis and classification of music genres. The primary objective of this research is to develop a robust and accurate system that can automatically classify music tracks into predefined genres based on their audio features. Chapter One provides an introduction to the research topic, discussing the background of the study, the problem statement, objectives, limitations, scope, significance, and the structure of the thesis. The chapter also defines key terms essential for understanding the study. Chapter Two presents a comprehensive literature review that explores existing studies related to music genre classification, machine learning algorithms, and audio feature extraction techniques. The review critically analyzes previous works and identifies gaps in the literature that this research seeks to address. Chapter Three outlines the research methodology employed in this study. It details the data collection process, the selection of machine learning algorithms, feature extraction techniques, model training, evaluation metrics, and the overall experimental setup. The chapter also discusses the validation methods used to ensure the reliability and validity of the results. Chapter Four presents the findings of the research, including the performance evaluation of the developed music genre classification system. The chapter discusses the accuracy, precision, recall, and F1-score of the classification model, highlighting its strengths and limitations. Additionally, it provides insights into the interpretability of the model and its potential applications in real-world scenarios. Chapter Five concludes the thesis with a summary of the key findings, implications of the research, contributions to the field, and recommendations for future research directions. The chapter reflects on the significance of the study in advancing the field of music analysis and classification using machine learning algorithms. In conclusion, this thesis contributes to the growing body of research on music genre classification by demonstrating the effectiveness of machine learning algorithms in automating the process. The developed system showcases promising results in accurately classifying music tracks into genres based on their audio features. This research lays the foundation for further exploration of machine learning techniques in music analysis and opens up possibilities for innovative applications in the music industry and beyond.