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.

Thesis Overview

The project titled "Application of Machine Learning Algorithms for Music Genre Classification" aims to explore the utilization of machine learning algorithms for the classification of music genres. In recent years, the exponential growth of digital music platforms has led to a massive increase in the volume of music available to listeners. However, this abundance has also made it challenging for users to navigate and explore music that aligns with their preferences. Music genre classification plays a crucial role in organizing and recommending music to users based on their tastes and preferences. The research will delve into the application of machine learning algorithms, such as support vector machines, decision trees, and neural networks, to automatically classify music into different genres based on audio features. By analyzing the audio signals of songs, these algorithms can learn to differentiate between genres such as rock, pop, classical, jazz, and electronic music. This automated classification process can enhance music recommendation systems, music streaming services, and music search engines, providing users with more personalized and accurate music suggestions. The project will begin with a comprehensive review of existing literature on music genre classification, machine learning algorithms, and audio feature extraction techniques. This background study will lay the foundation for understanding the current state of research in the field and identify gaps that this project aims to address. The research methodology will involve collecting a diverse dataset of music tracks spanning various genres, extracting relevant audio features from the songs, and training machine learning models to classify the music into different genres. The performance of the models will be evaluated using metrics such as accuracy, precision, recall, and F1 score to assess their effectiveness in genre classification. The findings of the study will be discussed in detail, highlighting the strengths and limitations of different machine learning algorithms in classifying music genres. The results will provide insights into the performance of these algorithms and their potential applications in real-world music recommendation systems. In conclusion, this research project on the "Application of Machine Learning Algorithms for Music Genre Classification" aims to advance the field of music information retrieval by leveraging machine learning techniques to enhance music organization and recommendation systems. By automating the genre classification process, this project has the potential to revolutionize the way users discover and explore music, ultimately enhancing their overall music listening experience.