Analysis of Music Genre Classification using Machine Learning Techniques

Thesis Abstract

Abstract
This thesis presents a comprehensive study on the analysis of music genre classification using machine learning techniques. The project aims to develop and evaluate machine learning models for accurately classifying music genres based on audio features. The research explores the significance of automated music genre classification in various applications such as music recommendation systems, music streaming platforms, and content organization in digital music libraries. The study begins with an introduction that provides an overview of the research topic, followed by a background of the study that discusses the evolution of music genre classification methods and the role of machine learning in this domain. The problem statement highlights the challenges faced in traditional genre classification approaches and the need for more efficient and accurate methods. The objectives of the study are defined to address these challenges and improve the performance of music genre classification systems. The limitations of the study and the scope of research are outlined to provide a clear understanding of the boundaries and focus of the project. The significance of the study is discussed in terms of its potential impact on music recommendation systems and user experience in music platforms. The structure of the thesis is presented to guide the reader through the organization of chapters and sections. Furthermore, key terms and concepts relevant to the research are defined to enhance understanding. The literature review in Chapter Two provides a comprehensive analysis of existing studies and methodologies related to music genre classification using machine learning techniques. Ten key aspects are discussed, including feature extraction methods, classification algorithms, evaluation metrics, and datasets used in previous research. The review highlights the strengths and limitations of different approaches and identifies gaps in the literature that this study aims to address. Chapter Three focuses on the research methodology employed in this study, including data collection, preprocessing, feature extraction, model selection, training, and evaluation. Eight key components of the methodology are elaborated upon to provide a detailed understanding of the experimental setup and procedures followed in the research. The chapter outlines the steps taken to ensure the reliability and validity of the study results. Chapter Four presents a detailed discussion of the findings obtained from the experiments conducted in the study. The performance of different machine learning models in classifying music genres is analyzed, and the results are compared to identify the most effective approaches. The chapter also explores the impact of various factors such as feature selection, model parameters, and dataset characteristics on classification accuracy. In Chapter Five, the conclusion and summary of the project thesis are provided, highlighting the key findings, contributions, and implications of the study. The limitations of the research are discussed, along with recommendations for future work and opportunities for further research in the field of music genre classification using machine learning techniques. Overall, this thesis contributes to the advancement of automated music genre classification systems by evaluating the effectiveness of machine learning models and proposing new approaches to enhance classification accuracy. The study provides valuable insights for researchers, practitioners, and developers working in the field of music information retrieval and computational musicology.