Thesis Abstract

Abstract
Music genre classification is a fundamental task in music information retrieval that has attracted significant interest from researchers and practitioners. With the growing availability of digital music content, the need for automated systems to organize and categorize music based on genre has become increasingly important. Machine learning techniques have proven to be effective in addressing this challenge by enabling the development of accurate and efficient genre classification models. This thesis presents a comprehensive analysis of music genre classification using machine learning techniques, with a focus on exploring different algorithms, feature extraction methods, and evaluation metrics for genre classification tasks. The study begins with an introduction to the importance of music genre classification and its relevance in various applications such as music recommendation systems, content organization, and music similarity analysis. The background of the study provides an overview of existing research in the field of music genre classification and highlights the significance of leveraging machine learning algorithms for automated genre categorization. The problem statement identifies the challenges and limitations associated with traditional music genre classification methods and motivates the need for advanced machine learning approaches. The objectives of the study are to investigate the performance of different machine learning algorithms for music genre classification, evaluate the effectiveness of various feature extraction techniques in capturing genre-specific information, and compare the performance of different evaluation metrics in assessing the performance of genre classification models. The study also outlines the limitations and scope of the research, emphasizing the need for further exploration and refinement of genre classification techniques. The significance of the study lies in its potential to contribute to the development of more accurate and efficient music genre classification systems, which can enhance music recommendation services, improve music content organization, and facilitate music exploration for users. The structure of the thesis is outlined to provide a roadmap for the reader, detailing the organization of chapters and the flow of the research work. In the literature review chapter, a comprehensive analysis of existing literature on music genre classification and machine learning techniques is presented. The review covers a range of topics, including feature extraction methods, machine learning algorithms, evaluation metrics, and benchmark datasets commonly used in genre classification research. The chapter synthesizes key findings from previous studies and identifies gaps in the literature that warrant further investigation. The research methodology chapter details the experimental setup, data collection process, feature extraction procedures, model training and evaluation techniques, and performance metrics used to assess the effectiveness of genre classification models. The chapter also discusses the selection of benchmark datasets, preprocessing steps, and parameter tuning strategies employed to optimize the performance of machine learning algorithms. The discussion of findings chapter presents a detailed analysis of the experimental results obtained from evaluating different machine learning algorithms and feature extraction methods for music genre classification. The chapter highlights the strengths and weaknesses of each approach, compares their performance on benchmark datasets, and discusses the implications of the findings for future research and practical applications. In the conclusion and summary chapter, the key findings and contributions of the study are summarized, and recommendations for future research directions are provided. The chapter also reflects on the limitations of the study and suggests areas for further refinement and exploration in the field of music genre classification using machine learning techniques. Overall, this thesis contributes to the advancement of music genre classification research by providing a comprehensive analysis of machine learning techniques, feature extraction methods, and evaluation metrics for genre categorization tasks. The findings of the study have practical implications for the development of automated genre classification systems and can inform the design of more effective music recommendation and organization tools.

Thesis Overview

The project titled "Analysis of Music Genre Classification using Machine Learning Techniques" aims to explore and implement advanced machine learning algorithms to classify music genres more accurately and efficiently. With the exponential growth of digital music platforms and the vast amount of music available online, automated genre classification has become crucial for music recommendation systems, content organization, and user preferences analysis. The research will delve into the existing methods and techniques used for music genre classification, highlighting their strengths, limitations, and areas for improvement. By leveraging machine learning models such as deep neural networks, support vector machines, and random forests, the study seeks to enhance the accuracy and robustness of music genre classification systems. The project will involve collecting a diverse dataset of music tracks across various genres, ensuring representation from mainstream genres like pop, rock, hip-hop, electronic, jazz, and classical, as well as niche genres and sub-genres. Feature extraction techniques will be employed to analyze audio characteristics such as tempo, timbre, rhythm, and spectral features, which will serve as input to the machine learning models. Through extensive experimentation and evaluation, the research aims to compare different machine learning algorithms in terms of classification accuracy, computational efficiency, and scalability. The project will also explore the interpretability of the models, shedding light on how they make genre classification decisions and identifying potential biases or inconsistencies. Furthermore, the study will address the challenges and limitations associated with music genre classification, including issues related to data quality, class imbalance, noisy labels, and genre ambiguity. By defining clear evaluation metrics and validation procedures, the research endeavors to provide comprehensive insights into the performance and generalization capabilities of the proposed machine learning models. Overall, the project "Analysis of Music Genre Classification using Machine Learning Techniques" seeks to contribute to the field of music information retrieval and machine learning by advancing the state-of-the-art in automated music genre classification. The findings and insights derived from this research are expected to have practical implications for music streaming services, recommendation systems, and music analysis tools, ultimately enhancing the user experience and accessibility of diverse music content.