Thesis Abstract

Abstract
The continuous growth of digital music repositories has rendered the manual classification of music genres infeasible. In response to this challenge, the application of machine learning algorithms for automated music genre classification has gained significant attention in recent years. This thesis explores the utilization of machine learning techniques to classify music genres efficiently and accurately. The research begins with a comprehensive introduction to the topic, providing background information on the significance of music genre classification and the limitations of existing manual classification methods. The problem statement highlights the need for automated genre classification to handle the vast amount of music data available today. The objectives of the study are outlined, focusing on developing and evaluating machine learning models for music genre classification. Chapter two presents an in-depth literature review covering various studies and approaches related to music genre classification using machine learning. The review examines different algorithms, feature extraction methods, and evaluation metrics employed in previous research projects, providing a foundation for the methodology chapter. Chapter three details the research methodology employed in this study, including data collection, preprocessing, feature extraction, model training, evaluation, and comparison. The chapter also discusses the selection of appropriate machine learning algorithms and parameter tuning strategies to achieve optimal classification performance. Chapter four presents a thorough discussion of the findings obtained from the experimental evaluation of the developed machine learning models. The chapter analyzes the performance metrics, such as accuracy, precision, recall, and F1 score, to assess the effectiveness of the classification models across different music genres. Finally, chapter five summarizes the key findings of the study, highlighting the contributions to the field of music genre classification through the application of machine learning. The conclusion reflects on the research objectives, methodologies, and results, discussing the implications of the study and suggesting potential avenues for future research in this area. Overall, this thesis contributes to the advancement of automated music genre classification using machine learning techniques, providing insights into the effectiveness and challenges of applying these methods to large-scale music databases. The findings of this research are valuable for researchers, music enthusiasts, and industry professionals seeking to enhance music organization and recommendation systems.

Thesis Overview

The project titled "Application of Machine Learning in Music Genre Classification" aims to explore the application of machine learning techniques in the field of music genre classification. Music genre classification is a fundamental task in music information retrieval systems, as it provides a way to organize and categorize music based on its stylistic attributes. Traditional methods of music genre classification rely on manual annotation and feature engineering, which can be time-consuming and subjective. In contrast, machine learning algorithms have the potential to automate this process and improve the accuracy and efficiency of music genre classification. The research will involve the collection of a large dataset of music samples spanning various genres, including rock, pop, classical, jazz, and electronic music. Feature extraction techniques will be applied to extract relevant attributes from the audio signals, such as spectral features, rhythm patterns, and timbral characteristics. These features will serve as input data for different machine learning models, including decision trees, support vector machines, and neural networks. The project will focus on evaluating the performance of different machine learning algorithms in classifying music genres accurately. Performance metrics such as accuracy, precision, recall, and F1-score will be used to assess the effectiveness of the models. Additionally, the research will investigate the impact of different feature representations and model configurations on the classification results. One of the key objectives of the project is to develop a robust and scalable music genre classification system that can accurately categorize music across a wide range of genres. The research will also explore the potential applications of machine learning in other music-related tasks, such as mood detection, artist identification, and recommendation systems. Overall, the project aims to contribute to the advancement of music information retrieval systems by leveraging the power of machine learning algorithms in music genre classification. The findings of this research have the potential to enhance the user experience in music streaming platforms, improve music recommendation services, and facilitate music discovery for users with diverse musical preferences.