Thesis Abstract

Abstract
The rapid advancement of machine learning algorithms has opened up new avenues for research in various domains, including music analysis and classification. This thesis investigates the application of machine learning algorithms in the analysis and classification of music genres. The primary objective of this research is to develop a robust and accurate system that can automatically classify music tracks into predefined genres based on their audio features. Chapter One provides an introduction to the research topic, discussing the background of the study, the problem statement, objectives, limitations, scope, significance, and the structure of the thesis. The chapter also defines key terms essential for understanding the study. Chapter Two presents a comprehensive literature review that explores existing studies related to music genre classification, machine learning algorithms, and audio feature extraction techniques. The review critically analyzes previous works and identifies gaps in the literature that this research seeks to address. Chapter Three outlines the research methodology employed in this study. It details the data collection process, the selection of machine learning algorithms, feature extraction techniques, model training, evaluation metrics, and the overall experimental setup. The chapter also discusses the validation methods used to ensure the reliability and validity of the results. Chapter Four presents the findings of the research, including the performance evaluation of the developed music genre classification system. The chapter discusses the accuracy, precision, recall, and F1-score of the classification model, highlighting its strengths and limitations. Additionally, it provides insights into the interpretability of the model and its potential applications in real-world scenarios. Chapter Five concludes the thesis with a summary of the key findings, implications of the research, contributions to the field, and recommendations for future research directions. The chapter reflects on the significance of the study in advancing the field of music analysis and classification using machine learning algorithms. In conclusion, this thesis contributes to the growing body of research on music genre classification by demonstrating the effectiveness of machine learning algorithms in automating the process. The developed system showcases promising results in accurately classifying music tracks into genres based on their audio features. This research lays the foundation for further exploration of machine learning techniques in music analysis and opens up possibilities for innovative applications in the music industry and beyond.

Thesis Overview

The project, "Analysis and Classification of Music Genres Using Machine Learning Algorithms," aims to explore the application of machine learning techniques in the field of music genre classification. Music genre classification is a fundamental task in the field of music information retrieval, as it plays a crucial role in music recommendation systems, music categorization, and content-based music retrieval. Traditional methods of music genre classification often rely on manual annotation or rule-based systems, which can be time-consuming and subjective. In contrast, machine learning algorithms offer an automated and data-driven approach to music genre classification. The research will involve the collection of a diverse dataset of music tracks spanning various genres, including but not limited to classical, jazz, rock, pop, hip-hop, and electronic music. Feature extraction techniques will be employed to extract relevant audio features from the music tracks, such as spectral features, rhythmic features, and timbral features. These features will serve as input to machine learning algorithms for training and classification purposes. Different machine learning algorithms, such as support vector machines, random forests, and deep learning models, will be explored and compared in terms of their performance in music genre classification. The research will investigate the impact of feature selection, model selection, and hyperparameter tuning on the classification accuracy and robustness of the models. Additionally, the project will address challenges and limitations in music genre classification using machine learning algorithms, such as data imbalance, noise in the audio signals, and the interpretability of the models. Techniques for improving the generalization and interpretability of the models will be investigated, including ensemble learning, feature engineering, and model explainability methods. The significance of this research lies in its potential to enhance music recommendation systems, music streaming platforms, and music analysis tools by providing more accurate and automated music genre classification capabilities. By leveraging machine learning algorithms, the project aims to improve the efficiency and effectiveness of music genre classification tasks, thereby benefiting music enthusiasts, researchers, and industry professionals in the field of music information retrieval.