Thesis Abstract

Abstract
The classification of music genres is a fundamental task in music information retrieval and has attracted significant research interest in recent years. This thesis presents an in-depth analysis of music genre classification techniques using machine learning algorithms. The primary objective of this study is to explore the effectiveness of various machine learning algorithms in accurately categorizing music into different genres. Chapter one provides an introduction to the research topic, highlighting the background of the study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of terms. Chapter two presents a comprehensive literature review covering ten key aspects related to music genre classification techniques, machine learning algorithms, and previous research studies in this field. Chapter three details the research methodology employed in this study, including data collection, preprocessing techniques, feature extraction methods, model selection, training, and evaluation procedures. It also discusses the datasets used and the performance metrics considered for evaluating the classification models. In chapter four, the findings of the research are extensively discussed, focusing on the comparative analysis of different machine learning algorithms such as Support Vector Machines, Random Forest, K-Nearest Neighbors, and Neural Networks for music genre classification. The results obtained from experimental evaluations are presented, analyzed, and interpreted to draw meaningful conclusions. Finally, chapter five offers a comprehensive summary and conclusion of the thesis, highlighting the key findings, implications of the research, limitations, and future research directions. The study concludes with recommendations for improving the accuracy and efficiency of music genre classification systems using machine learning algorithms. This thesis contributes to the existing body of knowledge in the field of music genre classification by providing valuable insights into the performance of various machine learning algorithms. The findings of this research will be beneficial to researchers, practitioners, and developers working in the domain of music information retrieval and automated music classification systems.

Thesis Overview

The project titled "Analysis of Music Genre Classification Techniques Using Machine Learning Algorithms" aims to explore and evaluate the effectiveness of applying machine learning algorithms in the classification of music genres. Music genre classification is a fundamental task in music information retrieval, with applications in recommendation systems, music streaming services, and music analysis. Traditional methods of music genre classification often rely on manual feature extraction and rule-based systems, which can be labor-intensive and may not capture the complex patterns present in music data. Machine learning algorithms offer a promising alternative by automatically learning patterns and relationships within the music data. This project seeks to investigate the performance of various machine learning techniques, such as Support Vector Machines, Random Forest, and Neural Networks, in accurately classifying music genres based on audio features. By leveraging a large dataset of music samples spanning multiple genres, the project aims to compare the effectiveness of different algorithms in accurately predicting genre labels. The research will also explore the impact of feature selection and dimensionality reduction techniques on the classification performance, aiming to identify the most relevant features for distinguishing between music genres. Additionally, the project will investigate the scalability and computational efficiency of the machine learning algorithms in handling large music datasets, considering real-world applications where processing speed and memory usage are critical factors. Furthermore, the project will conduct a comprehensive evaluation of the classification results, considering metrics such as accuracy, precision, recall, and F1 score to assess the performance of the algorithms across different music genres. The findings of this research will provide valuable insights into the strengths and limitations of using machine learning algorithms for music genre classification and offer practical recommendations for improving classification accuracy and efficiency. Overall, this project represents a significant contribution to the field of music information retrieval by advancing the understanding of how machine learning algorithms can be effectively applied to automate the classification of music genres. The outcomes of this research have the potential to enhance music recommendation systems, music indexing, and content-based music retrieval, ultimately benefiting both music enthusiasts and industry professionals in the music domain.