Thesis Abstract

Abstract
This thesis investigates the application of machine learning algorithms for music genre classification. The ability to automatically classify music into genres has numerous practical applications in music recommendation systems, content organization, and music streaming platforms. The project aims to explore how machine learning techniques can be utilized to accurately classify music tracks into different genres based on their audio features. Chapter 1 provides an introduction to the research topic, background information on music genre classification, a detailed problem statement, research objectives, limitations, scope, significance of the study, structure of the thesis, and definitions of key terms. The chapter sets the stage for understanding the importance and relevance of applying machine learning algorithms in the context of music genre classification. Chapter 2 presents a comprehensive literature review that examines existing studies, methodologies, and approaches related to music genre classification using machine learning algorithms. The chapter discusses various techniques, datasets, and evaluation metrics employed in previous research to classify music genres automatically. Chapter 3 outlines the research methodology employed in this study. It includes a detailed description of the dataset used, feature extraction techniques, preprocessing steps, model selection, training, and evaluation methodologies. The chapter also discusses the validation techniques and performance metrics used to assess the effectiveness of the machine learning algorithms for music genre classification. Chapter 4 presents a detailed discussion of the findings obtained from implementing different machine learning algorithms for music genre classification. The chapter analyzes the performance of various algorithms, compares results, identifies challenges, and provides insights into the effectiveness of different approaches in classifying music genres accurately. Chapter 5 serves as the conclusion and summary of the thesis. It highlights the key findings, contributions, implications of the research, limitations, and future research directions. The chapter summarizes the importance of applying machine learning algorithms for music genre classification and provides recommendations for further research in this area. In conclusion, this thesis contributes to the field of music genre classification by exploring the effectiveness of machine learning algorithms in automating the process of categorizing music tracks into genres. The research findings provide valuable insights into the potential applications of machine learning in the music industry and pave the way for further advancements in automated music genre classification systems.

Thesis Overview

The project titled "Applying Machine Learning Algorithms for Music Genre Classification" aims to explore the use of machine learning techniques in the context of music genre classification. Music genre classification is a fundamental task in music information retrieval, with applications ranging from music recommendation systems to automatic playlist generation. Traditional methods for music genre classification often rely on manual feature engineering, which can be time-consuming and may not capture the complex patterns present in music data. Machine learning algorithms offer a promising alternative by automatically learning patterns and relationships from the data. The research will begin with a comprehensive literature review to examine existing techniques and approaches in music genre classification. This review will provide a solid foundation for understanding the current state of the art and identifying gaps in the literature that can be addressed through the proposed research. The literature review will cover topics such as feature extraction, data preprocessing, model selection, and evaluation metrics used in music genre classification tasks. Following the literature review, the research will focus on developing and implementing machine learning algorithms for music genre classification. Various machine learning techniques, such as supervised learning, deep learning, and ensemble methods, will be explored and evaluated for their effectiveness in classifying music genres. The research will also investigate the impact of different feature representations, such as audio spectrograms, MFCCs, and semantic features, on the classification performance. The methodology will involve collecting a diverse dataset of music tracks spanning multiple genres and building a robust pipeline for data preprocessing, feature extraction, model training, and evaluation. The research will experiment with different machine learning models, hyperparameters, and training strategies to optimize the classification performance. Evaluation metrics such as accuracy, precision, recall, and F1 score will be used to assess the performance of the models. The findings of the research will be presented and discussed in detail, highlighting the strengths and limitations of the machine learning algorithms applied to music genre classification. The research will also discuss practical implications and potential applications of the developed models in real-world scenarios, such as music recommendation systems and content tagging platforms. In conclusion, the project "Applying Machine Learning Algorithms for Music Genre Classification" aims to advance the field of music information retrieval by leveraging machine learning techniques to automate the process of music genre classification. The research will provide valuable insights into the effectiveness of different machine learning algorithms and feature representations for classifying music genres, ultimately contributing to the development of more accurate and efficient music classification systems.