Application of Machine Learning Algorithms for Music Genre Classification

Thesis Abstract

Abstract
The rapid growth of digital music content has led to an increasing need for efficient methods to categorize music into different genres automatically. In response to this demand, this research explores the application of machine learning algorithms for music genre classification. The study aims to develop a robust system that can accurately classify music tracks into predefined genre categories based on their audio features. 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. The chapter sets the foundation for understanding the importance of automated music genre classification and the role of machine learning algorithms in achieving this goal. Chapter Two presents a comprehensive review of relevant literature on music genre classification, machine learning algorithms, feature extraction techniques, and existing studies related to the research topic. The review synthesizes and analyzes existing knowledge to identify gaps in the current literature and guide the development of the research methodology. Chapter Three outlines the research methodology, including data collection, preprocessing, feature extraction, model selection, training, and evaluation. The chapter details the steps involved in implementing machine learning algorithms for music genre classification, highlighting the selection criteria and rationale behind each decision. Chapter Four presents a detailed discussion of the findings obtained from the experimentation and evaluation of the developed music genre classification system. The chapter analyzes the performance metrics, compares different machine learning algorithms, and discusses the implications of the results in the context of automated music genre classification. Chapter Five provides a conclusion and summary of the research thesis, summarizing the key findings, discussing the contributions to the field, highlighting the limitations of the study, and suggesting directions for future research. The chapter concludes by emphasizing the significance of applying machine learning algorithms for music genre classification and the potential impact on the music industry. Overall, this research contributes to the ongoing efforts to automate music genre classification using machine learning algorithms, offering insights into the effectiveness and challenges of implementing such systems. The findings of this study have the potential to enhance music recommendation systems, music streaming services, and music content management platforms, ultimately benefiting both music producers and consumers in the digital age.