Thesis Abstract

Abstract
In the realm of music classification, the ability to automatically categorize music into distinct genres has become increasingly important due to the vast amount of digital music available today. This thesis explores the application of machine learning algorithms for music genre classification, aiming to improve the accuracy and efficiency of genre classification systems. The study begins with an introduction to the background and significance of music genre classification, highlighting the challenges faced in this area. The problem statement emphasizes the need for automated genre classification systems to handle the growing volume of music data. The objectives of the study are to develop and evaluate machine learning models for music genre classification, addressing the limitations and scope of the research. Chapter 2 presents a comprehensive literature review, examining existing studies and methodologies in music genre classification. Various machine learning algorithms, feature extraction techniques, and evaluation metrics are discussed to provide a foundation for the research. Chapter 3 outlines the research methodology, including data collection, preprocessing, feature extraction, model selection, and evaluation procedures. The experimental setup and evaluation metrics are detailed to ensure the validity and reliability of the results. Chapter 4 presents the findings of the study, showcasing the performance of different machine learning algorithms for music genre classification. The results are analyzed and discussed in-depth to identify the strengths and limitations of each approach. The impact of feature selection, model tuning, and data preprocessing on classification accuracy is also examined. Chapter 5 concludes the thesis by summarizing the key findings, highlighting the contributions of the study, and outlining future research directions in the field of music genre classification. Overall, this thesis contributes to the advancement of automated music genre classification systems by leveraging machine learning algorithms to enhance classification accuracy and efficiency. The findings of this study have implications for music recommendation systems, content tagging, and music discovery platforms, offering valuable insights for researchers and practitioners in the field of music information retrieval.

Thesis Overview

The project titled "Applying Machine Learning Algorithms for Music Genre Classification" aims to explore the application of machine learning algorithms in the domain of music genre classification. Music genre classification plays a crucial role in organizing and retrieving music collections, recommending music to users based on their preferences, and facilitating music search engines. Machine learning algorithms offer promising solutions to automate the process of classifying music into different genres based on audio features. The research will begin with a comprehensive introduction that sets the stage for the study by outlining the significance of music genre classification, the challenges involved, and the potential benefits of using machine learning algorithms in this context. The background of the study will provide a detailed overview of existing research in the field of music genre classification and highlight the gaps that this project aims to address. The problem statement will clearly define the research problem, emphasizing the need for more accurate and efficient methods for music genre classification. The objectives of the study will outline the specific goals that the research aims to achieve, such as developing and evaluating machine learning models for music genre classification. The limitations of the study will acknowledge the constraints and potential challenges that may impact the research outcomes, while the scope of the study will define the boundaries within which the research will be conducted. The significance of the study will underscore the potential impact of the research findings on the field of music information retrieval and related applications. The structure of the thesis will provide an overview of the organization of the research work, delineating the chapters and their respective contents. The definition of terms will clarify key concepts and terminology used throughout the thesis to ensure a common understanding among readers. Chapter Two will present a comprehensive literature review that synthesizes existing studies on music genre classification, machine learning algorithms, and relevant audio feature extraction techniques. This chapter will provide a theoretical foundation for the research and identify gaps in the existing literature that the project aims to address. Chapter Three will outline the research methodology, detailing the data collection process, feature extraction techniques, machine learning algorithms used, model evaluation methods, and performance metrics. This chapter will provide a transparent overview of the research methodology to ensure the reproducibility of the study. Chapter Four will present an elaborate discussion of the research findings, including the performance of different machine learning algorithms in classifying music genres, the impact of feature selection on classification accuracy, and the comparison of results with existing studies. This chapter will critically analyze the findings and draw conclusions based on the research outcomes. Finally, Chapter Five will offer a conclusion and summary of the project thesis, highlighting the key findings, contributions to the field, limitations of the study, and suggestions for future research directions. Overall, the research overview of "Applying Machine Learning Algorithms for Music Genre Classification" aims to advance the understanding of using machine learning algorithms for music genre classification and contribute to the development of more effective and efficient music information retrieval systems.