Project Abstract

Abstract
The music industry has witnessed a tremendous growth in the digital era, leading to a vast amount of music content available online. With the diverse range of music genres, it has become challenging for users to navigate through the vast music libraries to find the songs that align with their preferences. In this context, the application of machine learning techniques for the analysis and classification of music genres has gained significant attention. This research project aims to explore the effectiveness of machine learning algorithms in automatically classifying music into different genres based on audio features. Chapter One provides an introduction to the research topic, discussing the background of the study, problem statement, objectives, limitations, scope, significance, structure of the research, and definition of key terms. The introduction highlights the importance of music genre classification in enhancing music recommendation systems and user experience. Chapter Two delves into a comprehensive literature review on music genre classification, machine learning techniques, and existing research studies in this domain. The chapter explores various methodologies and algorithms used for music genre classification and highlights the strengths and limitations of previous studies. Chapter Three outlines the research methodology employed in this study, including data collection, preprocessing, feature extraction, model selection, and evaluation metrics. The chapter details the steps taken to collect music datasets, extract relevant features, and train machine learning models for genre classification. Chapter Four presents the findings of the research, including the performance evaluation of different machine learning algorithms in classifying music genres. The chapter discusses the accuracy, precision, recall, and F1-score of the models and provides insights into the effectiveness of various feature extraction techniques. Chapter Five concludes the research by summarizing the key findings, discussing the implications of the study, and suggesting future research directions. The conclusion highlights the potential of machine learning techniques in enhancing music genre classification systems and recommends further exploration in this area. Overall, this research project contributes to the advancement of music genre classification using machine learning techniques and provides valuable insights for researchers, developers, and music enthusiasts interested in improving music recommendation systems and user experience in the digital music landscape.

Project Overview

The project topic "Analysis and Classification of Music Genre using Machine Learning Techniques" focuses on the application of machine learning algorithms to analyze and classify music genres. Music genre classification is a fundamental task in the field of music information retrieval, and it plays a crucial role in various applications such as music recommendation systems, content-based music retrieval, and music streaming services. Traditional methods of genre classification often rely on manual annotation or rule-based systems, which can be time-consuming and subjective. In contrast, machine learning techniques offer automated and data-driven solutions that can effectively analyze large volumes of music data and accurately classify music genres. Machine learning algorithms, such as support vector machines, decision trees, and neural networks, can be trained on a dataset of audio features extracted from music tracks to learn patterns and relationships within the data. These algorithms can then be used to predict the genre of unseen music tracks based on their audio features. By leveraging the power of machine learning, researchers and music enthusiasts can gain valuable insights into the underlying characteristics of different music genres and improve the accuracy and efficiency of genre classification tasks. The project aims to explore various machine learning techniques and algorithms for music genre classification and evaluate their performance in terms of accuracy, speed, and scalability. By conducting experiments on a diverse dataset of music tracks spanning different genres, the project seeks to identify the most effective machine learning models for genre classification and investigate the impact of different audio features on classification performance. Additionally, the project will explore the potential challenges and limitations associated with using machine learning techniques for music genre classification, such as data imbalance, feature selection, and model interpretability. Overall, the project seeks to contribute to the field of music information retrieval by demonstrating the effectiveness of machine learning techniques in analyzing and classifying music genres. By developing robust and accurate genre classification models, the project aims to enhance the capabilities of music recommendation systems and provide music enthusiasts with better tools for exploring and discovering music across diverse genres.