Thesis Abstract

Abstract
Music genre classification is an essential task in the field of music information retrieval, enabling various applications such as music recommendation systems, playlist generation, and music search engines. This thesis presents an in-depth analysis of music genre classification algorithms, aiming to enhance the accuracy and efficiency of genre classification systems. The study investigates various machine learning and deep learning techniques applied to music genre classification, evaluating their performance and exploring their strengths and limitations. The research begins with a comprehensive review of existing literature on music genre classification, highlighting the evolution of algorithms and methodologies used in this domain. Subsequently, a detailed examination of the research methodology employed in this study is provided, encompassing data collection, preprocessing techniques, feature extraction, model selection, and evaluation metrics. The methodology section outlines the experimental setup, datasets used, and the rationale behind the chosen approaches. In the discussion of findings section, the results of the experiments conducted on different classification algorithms are presented and analyzed. The performance of each algorithm is evaluated based on metrics such as accuracy, precision, recall, and F1-score. The comparative analysis sheds light on the strengths and weaknesses of each approach, offering insights into the factors influencing classification accuracy and efficiency. The conclusion and summary section encapsulate the key findings of the study, emphasizing the significance of the research outcomes in advancing the field of music genre classification. The implications of the results for real-world applications and future research directions are discussed, highlighting opportunities for further improvement and innovation in algorithm design and implementation. Overall, this thesis contributes to the ongoing discourse on music genre classification algorithms by providing a detailed analysis of state-of-the-art techniques and methodologies. The research findings offer valuable insights for researchers, practitioners, and developers working in the field of music information retrieval, paving the way for advancements in music genre classification systems and enhancing user experience in music-related applications.

Thesis Overview

The project titled "Analysis of Music Genre Classification Algorithms" aims to investigate and analyze the effectiveness of various algorithms in classifying music genres. Music genre classification is a fundamental task in music information retrieval, with applications ranging from music recommendation systems to content organization. The research will focus on evaluating the performance of different machine learning algorithms, such as Support Vector Machines, Random Forest, and Neural Networks, in accurately categorizing music tracks into predefined genres. The research will begin with a thorough review of existing literature on music genre classification algorithms to establish a foundation for the study. This literature review will explore the evolution of music genre classification techniques, highlighting the strengths and limitations of various approaches. By examining previous studies, the project aims to identify gaps in the current research and propose novel methodologies for enhancing music genre classification accuracy. Following the literature review, the research methodology will be outlined, detailing the dataset used, feature extraction techniques, model training procedures, and evaluation metrics. The project will utilize a diverse collection of music tracks spanning different genres to ensure the robustness and generalizability of the classification algorithms. Feature extraction methods such as Mel-frequency cepstral coefficients (MFCCs) and spectral features will be employed to represent the audio content effectively. The core of the project will involve implementing and evaluating a variety of machine learning algorithms for music genre classification. By comparing the performance of different models on the same dataset, the research aims to identify the most effective algorithms for accurately categorizing music genres. The project will also investigate the impact of hyperparameter tuning, feature selection, and model ensembling on classification performance. The findings of the research will be presented and discussed in detail in the results chapter, highlighting the strengths and weaknesses of each algorithm and providing insights into the factors that influence classification accuracy. The discussion will also address the challenges encountered during the study and propose recommendations for future research in the field of music genre classification. In conclusion, the project "Analysis of Music Genre Classification Algorithms" seeks to contribute to the advancement of music information retrieval by evaluating the performance of state-of-the-art machine learning algorithms in genre classification. By conducting a comprehensive analysis of different approaches and methodologies, the research aims to enhance the accuracy and efficiency of music genre classification systems, ultimately benefiting applications such as music recommendation services and content organization platforms.