Thesis Abstract

Abstract
The rapid growth of digital music platforms and the ever-expanding music industry have created a need for efficient methods to analyze and classify music genres. This thesis explores the application of machine learning techniques in the analysis and classification of music genres. The primary objective is to develop a model that can accurately categorize music into different genres based on audio features. The thesis begins with a comprehensive introduction that outlines the background of the study, the problem statement, objectives, limitations, scope, significance, and the structure of the thesis. The literature review in Chapter Two covers ten key studies and research works related to music genre classification, machine learning algorithms, and audio feature extraction methods. Chapter Three details the research methodology used in this study, including data collection, preprocessing, feature extraction, model selection, and evaluation metrics. It also discusses the implementation of machine learning algorithms such as Support Vector Machines, Random Forest, and Convolutional Neural Networks for genre classification. Chapter Four presents a thorough discussion of the findings obtained from the experiments conducted in this study. It analyzes the performance of different machine learning models in classifying music genres and identifies the most effective techniques for accurate genre classification. In the final chapter, Chapter Five, the thesis concludes with a summary of the key findings, implications of the research, limitations of the study, and recommendations for future research in the field of music genre analysis using machine learning techniques. Overall, this thesis contributes to the advancement of music genre classification through the application of machine learning algorithms. The results obtained demonstrate the potential of machine learning techniques in accurately analyzing and categorizing music genres, which can benefit music recommendation systems, playlist generation, and music production processes.

Thesis Overview

The project titled "Analysis and Classification of Music Genres Using Machine Learning Techniques" aims to explore the application of machine learning techniques in the analysis and classification of music genres. Music plays a significant role in our daily lives, and the ability to automatically categorize music into different genres can have various practical applications, such as music recommendation systems, content organization, and music indexing. Machine learning, with its ability to learn patterns and make predictions from data, offers a promising approach to automate the process of genre classification. The research will begin with an in-depth exploration of the existing literature on music genre classification and machine learning techniques. This will provide a solid foundation for understanding the current state of the art, key challenges, and potential opportunities in the field. The literature review will cover topics such as feature extraction, feature selection, model selection, and evaluation metrics commonly used in music genre classification tasks. The methodology chapter will detail the approach taken to collect and preprocess music data, extract relevant features from the audio signals, select appropriate machine learning models, train and evaluate the models, and optimize the classification performance. The research will leverage a diverse dataset of music tracks spanning various genres to ensure the robustness and generalizability of the classification models. The discussion of findings chapter will present the results of the experimental evaluation, including the performance metrics of the machine learning models, the impact of different feature representations on classification accuracy, and the insights gained from the analysis of misclassifications. This section will also discuss the strengths and limitations of the proposed approach and suggest potential directions for future research. Finally, the conclusion and summary chapter will provide a comprehensive overview of the key findings, contributions, and implications of the research. It will summarize the main outcomes of the study, discuss the practical relevance of the results, and highlight the significance of using machine learning techniques for music genre classification tasks. Overall, this research project aims to advance the field of music information retrieval by demonstrating the effectiveness of machine learning techniques in analyzing and classifying music genres. By developing accurate and efficient classification models, the study seeks to contribute to the development of more intelligent music recommendation systems and enhance the overall user experience in accessing and exploring music content."