Thesis Abstract

Abstract
The rapid growth of digital music content calls for efficient methods of organizing and categorizing music genres to enhance user experience and content recommendation systems. This thesis explores the implementation of machine learning algorithms for music genre classification, aiming to develop a robust and accurate system for automatically identifying genres in music tracks. The study begins with a comprehensive review of existing literature on music genre classification, machine learning techniques, and their applications in the music domain. The research methodology section outlines the data collection process, feature extraction methods, model selection, and evaluation metrics used to train and test the machine learning algorithms. Chapter Four delves into the discussion of findings, presenting the results of the experiments conducted to evaluate the performance of various machine learning algorithms in classifying music genres. The chapter highlights the strengths and limitations of each algorithm, providing insights into their effectiveness and potential for real-world applications. Lastly, Chapter Five concludes the thesis by summarizing the key findings, discussing the implications of the study, and suggesting future research directions in the field of music genre classification using machine learning. Keywords Machine Learning, Music Genre Classification, Feature Extraction, Model Evaluation, Data Analysis, Recommendation Systems.

Thesis Overview

The project titled "Implementing Machine Learning Algorithms for Music Genre Classification" aims to explore the application of machine learning algorithms in the context of music genre classification. Music genre classification is a crucial task in the field of music information retrieval, as it helps in organizing and categorizing vast amounts of music data based on their stylistic characteristics. Traditional methods of genre classification have limitations in handling the complexity and variability of musical features, which makes machine learning an attractive approach due to its ability to learn patterns and make predictions from data. The research will begin with a comprehensive review of existing literature on music genre classification, machine learning algorithms, and their applications in music analysis. This review will provide a solid foundation for understanding the current state of the art, identifying gaps in the research, and informing the selection of appropriate algorithms for the classification task. The project will then focus on the research methodology, which will involve data collection, preprocessing, feature extraction, model selection, training, and evaluation. Various machine learning algorithms such as Support Vector Machines, Random Forest, and Neural Networks will be implemented and compared to determine their effectiveness in accurately classifying music genres. The discussion of findings will present the results of the experiments conducted on a dataset of music samples, showcasing the performance metrics, such as accuracy, precision, recall, and F1 score, of the different algorithms. The analysis will highlight the strengths and limitations of each algorithm in classifying music genres and provide insights into the factors influencing classification accuracy. Finally, the conclusion and summary will recap the key findings of the research, discuss the implications of the results, and suggest future research directions. The project aims to contribute to the field of music information retrieval by demonstrating the potential of machine learning algorithms in improving the accuracy and efficiency of music genre classification systems.