Thesis Abstract

Abstract
Music genre classification is a fundamental task in the field of music information retrieval, with various applications in recommendation systems, music streaming platforms, and content organization. This thesis presents a comprehensive study on the analysis and classification of music genres using machine learning algorithms. The main objective of this research is to develop an automated system that can accurately classify music tracks into different genres based on their audio features. To achieve this, a dataset comprising a diverse collection of music tracks is used for training and evaluation purposes. The thesis begins with an introduction that provides an overview of the research topic and its significance in the context of music information retrieval. The background of the study covers relevant literature on music genre classification and machine learning techniques used in similar research studies. The problem statement highlights the challenges associated with manual genre labeling and the need for automated classification systems. The objectives of the study outline the specific goals and aims of the research, while the limitations and scope of the study define the boundaries and constraints of the project. A detailed literature review in Chapter Two explores existing methodologies and approaches to music genre classification, discussing the strengths and limitations of different algorithms and feature extraction techniques. The chapter provides a critical analysis of prior research studies and identifies gaps in the current literature that this thesis aims to address. Chapter Three focuses on the research methodology employed in this study, including data collection, preprocessing, feature extraction, model selection, and evaluation metrics. The methodology section outlines the steps taken to build and train the classification model, as well as the criteria used to assess its performance and generalization capabilities. Chapter Four presents the findings of the research, including the experimental results of the classification model on the music dataset. The discussion section analyzes the performance metrics, model accuracy, and potential areas for improvement. The chapter also includes visualizations and interpretations of the classification results to provide insights into the effectiveness of the proposed approach. Finally, Chapter Five concludes the thesis by summarizing the key findings, discussing the implications of the research outcomes, and suggesting future directions for further research in the field of music genre classification using machine learning algorithms. The conclusion highlights the contributions of this study to the existing body of knowledge and emphasizes the importance of automated music genre classification systems in enhancing music discovery and recommendation services. In conclusion, this thesis contributes to the advancement of music genre classification research by proposing a novel approach that leverages machine learning algorithms to accurately analyze and classify music genres based on audio features. The findings of this study have practical implications for the development of intelligent music recommendation systems and content organization tools, ultimately enhancing the user experience in the digital music domain.

Thesis Overview

The project titled "Analysis and Classification of Music Genres Using Machine Learning Algorithms" focuses on utilizing machine learning algorithms to analyze and classify music genres. This research aims to explore how machine learning techniques can be applied to automatically identify and categorize different music genres based on audio features. By leveraging advanced algorithms, this study seeks to enhance the accuracy and efficiency of music genre classification, which is essential for various applications in the music industry, recommendation systems, and music streaming platforms. The research will involve collecting a diverse dataset of audio samples representing various music genres, such as rock, pop, jazz, classical, hip-hop, and electronic music. These audio samples will be preprocessed to extract relevant features, such as tempo, pitch, timbre, and rhythm, which will serve as input for the machine learning models. Different machine learning algorithms, including but not limited to support vector machines (SVM), neural networks, decision trees, and k-nearest neighbors (KNN), will be evaluated and compared to identify the most effective approach for music genre classification. The project will be structured into several key phases, including data collection, preprocessing, feature extraction, model training, evaluation, and validation. Through rigorous experimentation and analysis, the research aims to identify the optimal combination of features and algorithms that yield the highest classification accuracy for music genres. Additionally, the study will investigate the impact of different parameters and hyperparameters on the performance of the classification models. The findings of this research are expected to contribute to the field of music information retrieval and machine learning by providing insights into the effectiveness of various algorithms for music genre classification. The proposed methodology and results will be valuable for researchers, music professionals, and developers seeking to improve music recommendation systems, genre tagging, and content organization in music libraries. Overall, the project "Analysis and Classification of Music Genres Using Machine Learning Algorithms" aims to advance the state-of-the-art in music genre classification by leveraging the power of machine learning to automate and enhance the process of identifying and categorizing diverse music genres in an efficient and accurate manner.