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.

Thesis Overview

The project titled "Using Machine Learning for Music Genre Classification" aims to explore the application of machine learning algorithms in the field of music genre classification. Music genre classification is a fundamental task in music information retrieval and has various practical applications in music recommendation systems, content organization, and music streaming services. Traditional methods of music genre classification rely on manual feature extraction and rule-based classification techniques, which can be time-consuming and may not capture the complex patterns present in music data. Machine learning offers a promising approach to automate the process of music genre classification by utilizing advanced algorithms to learn patterns and relationships within the data. This project seeks to investigate the effectiveness of machine learning models, such as support vector machines, neural networks, and decision trees, in accurately classifying music into different genres based on audio features. The research will involve collecting a diverse dataset of music tracks spanning various genres, including rock, pop, jazz, classical, electronic, and hip-hop. Audio features such as tempo, rhythm, pitch, timbre, and spectral features will be extracted from the music tracks to represent the musical content. These features will serve as input to the machine learning models, which will be trained and evaluated using techniques such as cross-validation and performance metrics like accuracy, precision, recall, and F1-score. The project will also explore the impact of different feature representations, model architectures, and hyperparameters on the classification performance. Additionally, the research will investigate the robustness of the machine learning models to variations in the dataset, such as noise, imbalanced class distributions, and data augmentation techniques. By conducting this research, valuable insights can be gained into the capabilities and limitations of using machine learning for music genre classification. The findings of this study have the potential to contribute to the advancement of music information retrieval systems and enhance the user experience in music platforms by providing more accurate and personalized music recommendations based on genre preferences.