Thesis Abstract

Abstract
The use of machine learning techniques in the field of music genre classification has gained significant attention in recent years due to the increasing volume and diversity of music available online. This thesis investigates the application of machine learning algorithms for automatic music genre classification. The primary objective is to develop a robust and accurate system that can effectively classify music tracks into different genres based on their 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 thesis, and key definitions of terms. The chapter sets the stage for understanding the importance of applying machine learning in music genre classification and outlines the research questions to be addressed. Chapter Two presents a comprehensive literature review on existing studies and methodologies related to music genre classification using machine learning techniques. The chapter covers topics such as feature extraction, feature selection, classification algorithms, evaluation metrics, and challenges faced in music genre classification tasks. Chapter Three details the research methodology employed in this study, including data collection, preprocessing steps, feature extraction techniques, model selection, performance evaluation, and experimental setup. The chapter outlines the steps taken to build and train the machine learning models for music genre classification. Chapter Four presents a detailed discussion of the findings obtained from the experiments conducted in this research. The chapter analyzes the performance of different machine learning algorithms in classifying music tracks into various genres and discusses the impact of different feature sets on classification accuracy. Chapter Five concludes the thesis by summarizing the key findings, discussing the implications of the research results, and suggesting areas for future work. The chapter also highlights the contributions of this study to the field of music genre classification and underscores the importance of utilizing machine learning techniques for automated music genre recognition. In conclusion, this thesis contributes to the ongoing research efforts in the field of music genre classification by demonstrating the effectiveness of machine learning algorithms in accurately categorizing music tracks into different genres. The findings of this study provide valuable insights into the application of machine learning in music analysis and pave the way for further advancements in automated music genre classification systems.

Thesis Overview

The project titled "Application of Machine Learning in Music Genre Classification" focuses on utilizing machine learning techniques to classify music into different genres. Music genre classification is a fundamental task in the field of music information retrieval, aiming to automatically categorize music tracks into predefined genres based on their audio features. This research overview will delve into the importance of music genre classification, the challenges involved, the proposed methodology, and the potential impact of the project. Music genre classification plays a crucial role in various applications such as music recommendation systems, music streaming platforms, and music analysis tools. By accurately categorizing music into genres, these systems can provide personalized recommendations to users, help in organizing music libraries, and facilitate music discovery. However, manual genre labeling of a vast amount of music data is time-consuming and subjective. Therefore, the automation of genre classification through machine learning algorithms presents an efficient and objective solution to this problem. The challenges in music genre classification stem from the inherent complexity and subjectivity of music genres. Different genres can share similar acoustic characteristics, making it challenging to differentiate between them accurately. Additionally, music genres are not always well-defined and can evolve over time, leading to ambiguity and overlap between genres. Addressing these challenges requires the development of robust machine learning models that can effectively capture the diverse characteristics of music genres. The proposed methodology for this project involves collecting a diverse dataset of music tracks representing various genres. Feature extraction techniques will be applied to extract relevant audio features from the music tracks, such as spectral features, rhythm patterns, and harmonic content. These features will then be used to train machine learning models, including algorithms like support vector machines, neural networks, and decision trees, to classify music into different genres. The potential impact of this research lies in its ability to enhance music recommendation systems and music analysis tools by providing more accurate and reliable genre classification. By leveraging machine learning algorithms, these systems can deliver more personalized and relevant music recommendations to users, improving the overall user experience. Furthermore, the research outcomes can contribute to the advancement of music information retrieval techniques and pave the way for future research in the field of music genre classification. In conclusion, the project "Application of Machine Learning in Music Genre Classification" aims to leverage machine learning techniques to automate the classification of music into genres, addressing the challenges of manual genre labeling and enhancing the effectiveness of music recommendation systems. Through this research, we seek to contribute to the field of music information retrieval and advance the understanding of music genre classification in the digital age.