Thesis Abstract

Abstract
With the continuous growth of digital music libraries and streaming platforms, the need for efficient music genre classification systems has become increasingly important. This thesis explores the application of machine learning algorithms in the classification of music genres. The main objective of this research is to develop a robust and accurate classification system that can automatically categorize music tracks into different genres based on their audio features. The study begins with an introduction to the background of music genre classification and the problem statement surrounding the manual classification of music. The objectives of the study include developing a machine learning model that can accurately classify music genres, identifying the limitations and scope of the study, and highlighting the significance of the research in the field of music technology. The structure of the thesis is outlined to provide a roadmap for the reader through the various chapters. Chapter two presents a comprehensive literature review on existing methods and techniques used in music genre classification. Ten key aspects are discussed, including feature extraction methods, machine learning algorithms, evaluation metrics, and challenges faced in genre classification tasks. The literature review provides a foundation for understanding the current state of the art in the field and identifies gaps that will be addressed in this research. Chapter three details the research methodology employed in this study. Eight key components are described, including data collection and preprocessing, feature extraction, model selection, training and testing procedures, parameter tuning, and evaluation methods. The methodology outlines the steps taken to develop and evaluate the machine learning model for music genre classification. Chapter four presents an in-depth discussion of the findings obtained from the experimentation and evaluation of the machine learning model. The results are analyzed, and the performance of the classification system is assessed based on various evaluation metrics such as accuracy, precision, recall, and F1 score. The findings are compared with existing approaches, and insights are provided on the effectiveness of the proposed model. Finally, chapter five summarizes the key findings of the research and provides conclusions based on the outcomes of the study. The implications of the research are discussed, and recommendations for future work are outlined. The thesis concludes with reflections on the contributions of the study to the field of music genre classification and the potential impact of machine learning in automating music categorization processes. Overall, this thesis contributes to the advancement of music technology by demonstrating the effectiveness of machine learning algorithms in automating music genre classification tasks. The research provides valuable insights into the development of accurate and efficient classification systems for organizing and managing large music collections in digital platforms.

Thesis Overview

The project titled "Application of Machine Learning in Music Genre Classification" aims to explore the use of machine learning algorithms to automatically classify music into different genres. Music genre classification is a fundamental task in music information retrieval, aiding in music recommendation systems, playlist generation, and music search engines. With the exponential growth of digital music libraries, manual genre labeling has become a time-consuming and labor-intensive process. The research will focus on developing and implementing machine learning models that can effectively analyze audio features and patterns to accurately categorize music tracks into various genres, such as rock, pop, jazz, classical, electronic, etc. The project will leverage existing music datasets and employ techniques such as feature extraction, data preprocessing, model training, and evaluation to achieve the classification objectives. Key aspects of the research will include selecting appropriate audio features that capture essential characteristics of music genres, exploring different machine learning algorithms (e.g., support vector machines, neural networks, decision trees) for classification tasks, and optimizing the model performance through parameter tuning and cross-validation. Furthermore, the project will investigate the impact of various factors, such as dataset size, class distribution, feature selection, and model complexity, on the classification accuracy and generalization capabilities of the machine learning models. The research will also address challenges related to noisy data, class imbalance, and model interpretability in the context of music genre classification. Overall, the project aims to contribute to the advancement of music information retrieval systems by demonstrating the effectiveness of machine learning techniques in automating the genre classification process. The findings and insights derived from this research could potentially enhance music recommendation algorithms, improve user experience in music streaming platforms, and facilitate music discovery based on genre preferences.