Thesis Abstract

Abstract
Music genre classification is a fundamental task in music information retrieval, with applications in music recommendation, playlist generation, and music content organization. This thesis presents an in-depth analysis and comparison of various music genre classification algorithms to evaluate their performance and identify the most effective approach. The study aims to contribute to the advancement of music genre classification techniques and provide insights for researchers and practitioners in the field. The thesis begins with a comprehensive introduction that outlines the background of the study, problem statement, objectives, limitations, scope, significance, and the structure of the thesis. Chapter one provides a foundation for understanding the importance of music genre classification and sets the stage for the subsequent chapters. Chapter two comprises a detailed literature review that explores existing studies, methodologies, and algorithms related to music genre classification. The review covers topics such as feature extraction, machine learning algorithms, deep learning techniques, and evaluation metrics used in music genre classification research. By synthesizing and analyzing the literature, this chapter aims to provide a comprehensive overview of the current state-of-the-art in the field. Chapter three focuses on the research methodology employed in this study. It outlines the research design, data collection methods, feature extraction techniques, model selection, training, and evaluation processes. The chapter also discusses the experimental setup, including the datasets used, parameter tuning, and performance evaluation metrics. In chapter four, the findings of the study are presented and discussed in detail. The performance of various music genre classification algorithms is compared based on accuracy, precision, recall, and F1-score metrics. The strengths and weaknesses of each algorithm are analyzed, and insights into their suitability for different music genres are provided. Additionally, the impact of feature selection, model complexity, and dataset size on classification performance is explored. Finally, chapter five offers a summary of the key findings, conclusions, and recommendations derived from the study. The implications of the results for future research directions and practical applications in music genre classification are discussed. The thesis concludes with a reflection on the contributions of this study to the field of music information retrieval and a call for further exploration of advanced classification algorithms for improved music genre recognition. In conclusion, this thesis contributes to the advancement of music genre classification by providing a comprehensive analysis and comparison of existing algorithms. By evaluating the performance of different approaches and identifying their strengths and limitations, this study offers valuable insights for researchers and practitioners seeking to enhance the accuracy and efficiency of music genre classification systems.

Thesis Overview

The project titled "Analysis and Comparison of Music Genre Classification Algorithms" aims to explore and evaluate various algorithms used in the classification of music genres. With the ever-increasing volume of digital music content available online, the need for efficient and accurate music genre classification algorithms has become essential to enhance music recommendation systems, music search engines, and personalized playlist generation. This research will delve into the background of music genre classification, including its significance in the field of music information retrieval. The study will begin by introducing the concept of music genre classification and its relevance in organizing and categorizing music based on shared characteristics such as rhythm, instrumentation, and vocal style. The project will then outline the problem statement, highlighting the existing challenges and limitations faced by current music genre classification algorithms. By analyzing and comparing different algorithms, the research aims to identify strengths, weaknesses, and areas for improvement in music genre classification techniques. The objectives of the study will be clearly defined to guide the research process. These objectives will include evaluating the performance of different algorithms in classifying music genres, comparing their accuracy and efficiency, and identifying the most effective algorithm for music genre classification. The research methodology section will detail the approach taken to conduct the study, including data collection, feature extraction, algorithm implementation, and evaluation metrics. Various machine learning and data mining techniques will be explored and applied to classify music genres based on audio features. Subsequently, the findings from the analysis and comparison of music genre classification algorithms will be discussed in detail. The results will provide insights into the performance of different algorithms, their suitability for various music genres, and potential areas for future research and development. In conclusion, this research aims to contribute to the advancement of music genre classification algorithms by providing a comprehensive analysis and comparison of existing techniques. By understanding the strengths and limitations of different algorithms, this study seeks to enhance the accuracy and efficiency of music genre classification systems, ultimately improving the user experience in music recommendation and discovery platforms.