Analysis and Prediction of Music Genre Classification Using Machine Learning Algorithms

Thesis Abstract

Abstract
This thesis presents a comprehensive investigation into the analysis and prediction of music genre classification using machine learning algorithms. Music genre classification plays a crucial role in various applications such as music recommendation systems, content organization, and music streaming platforms. The primary objective of this research is to explore the effectiveness of machine learning algorithms in accurately classifying music genres based on audio features. The study focuses on the application of supervised learning techniques to build predictive models capable of automatically categorizing music tracks into different genres. 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 definition of key terms. The chapter sets the foundation for the subsequent research chapters by outlining the research context and objectives. Chapter 2 conducts an extensive literature review on existing studies related to music genre classification, machine learning algorithms, feature extraction techniques, and performance evaluation metrics. The review synthesizes current knowledge in the field, identifies gaps in the literature, and provides a theoretical framework for the research. Chapter 3 details the research methodology employed in this study, including data collection, preprocessing, feature extraction, model selection, training, evaluation, and validation procedures. The chapter outlines the steps taken to implement machine learning algorithms for music genre classification and describes the evaluation metrics used to assess the performance of the models. Chapter 4 presents a detailed discussion of the findings obtained from the experiments conducted in this research. The chapter analyzes the performance of different machine learning algorithms in classifying music genres and examines the impact of various audio features on classification accuracy. The discussion provides insights into the strengths and limitations of the models developed in this study. Chapter 5 concludes the thesis by summarizing the key findings, discussing the implications of the research, and suggesting avenues for future work. The conclusion highlights the significance of using machine learning algorithms for music genre classification and emphasizes the potential applications of the research outcomes in the music industry. Overall, this thesis contributes to the field of music genre classification by demonstrating the efficacy of machine learning algorithms in accurately predicting music genres based on audio features. The research findings provide valuable insights for developing more sophisticated and efficient music classification systems, enhancing music recommendation services, and improving user experiences in music streaming platforms.