Using Machine Learning for Music Genre Classification

Thesis Abstract

Abstract
Music genre classification is a fundamental task in music information retrieval, aimed at automatically recognizing and categorizing music based on its audio content. With the exponential growth of digital music collections, the need for efficient and accurate music genre classification systems has become increasingly important. Machine learning techniques have shown promising results in this field due to their ability to learn patterns and features from data. This thesis explores the application of machine learning algorithms for music genre classification, focusing on improving classification accuracy and robustness. Chapter One provides an introduction to the research topic, presenting the background of the study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of key terms. Chapter Two conducts a comprehensive literature review, discussing relevant studies, methodologies, and findings in the field of music genre classification using machine learning. Chapter Three outlines the research methodology, detailing the data collection process, feature extraction techniques, machine learning algorithms employed, model training, and evaluation methods. The chapter also discusses the preprocessing steps and parameter tuning strategies adopted to enhance classification performance. Chapter Four presents a detailed analysis and discussion of the research findings, including the experimental results, performance metrics, comparative evaluations, and insights gained from the classification process. The chapter highlights the strengths and limitations of the proposed approach and provides recommendations for future research directions. Chapter Five concludes the thesis by summarizing the key findings, contributions, and implications of the study. The conclusions drawn from the research are discussed in relation to the initial objectives, highlighting the significance of the findings and their potential impact on advancing music genre classification using machine learning techniques. Overall, this thesis contributes to the ongoing research in music genre classification by leveraging machine learning algorithms to enhance classification accuracy and efficiency. The findings of this study provide valuable insights for researchers, practitioners, and developers working in the field of music information retrieval and computational musicology.