Thesis Abstract

Abstract
This thesis presents a comprehensive study on the analysis and classification of music genres utilizing machine learning techniques. The aim of this research is to develop an automated system that can accurately identify and categorize music genres based on audio features. With the exponential growth of digital music content, there is a pressing need for efficient methods to organize and classify music for various applications such as recommendation systems, music streaming platforms, and music information retrieval. The research begins with an exploration of the background of music genre classification and the existing methods used in the field. The problem statement emphasizes the challenges in accurately categorizing music genres due to the subjective nature of genre definitions and the complexity of musical features. The objectives of the study include developing a machine learning model that can effectively analyze audio signals and classify music genres with high accuracy. The limitations of the study are acknowledged, including the potential challenges in extracting meaningful features from audio signals, the subjectivity of genre labels, and the computational complexity of machine learning algorithms. The scope of the study is defined to focus on popular music genres and to evaluate the performance of various machine learning algorithms in genre classification tasks. The significance of this research lies in its potential to enhance music recommendation systems, improve music organization, and facilitate music exploration for users. The structure of the thesis is outlined to include the introduction, literature review, research methodology, discussion of findings, and conclusion. The literature review delves into existing studies on music genre classification, machine learning algorithms for audio analysis, and feature extraction techniques. The research methodology section details the data collection process, feature extraction methods, model training procedures, and evaluation metrics used in the study. The findings of this research demonstrate the effectiveness of machine learning techniques in accurately classifying music genres. The discussion elaborates on the performance of different algorithms, the impact of feature selection on classification accuracy, and the implications of the results for real-world applications. In conclusion, this thesis contributes to the field of music information retrieval by providing insights into the application of machine learning for music genre classification. The study highlights the potential of automated systems to enhance music organization and user experience in the digital music domain.

Thesis Overview

The project titled "Analysis and Classification of Music Genres Using Machine Learning Techniques" aims to explore and utilize machine learning algorithms to analyze and classify music genres. Music classification is a fundamental task in the field of music information retrieval, with applications ranging from music recommendation systems to music genre identification in digital libraries. Machine learning techniques have shown great promise in automating this process and improving accuracy compared to traditional methods. The research will begin with a comprehensive review of existing literature on music genre classification, machine learning algorithms, and their applications in music analysis. This review will provide a strong theoretical foundation for the project and help identify gaps in current research that can be addressed. The methodology for the project will involve collecting a diverse dataset of music tracks spanning different genres. Feature extraction techniques will be applied to capture relevant characteristics of the audio signals, such as tempo, pitch, and timbre. These features will then be used as inputs to various machine learning models, such as support vector machines, neural networks, and decision trees, to train and evaluate the classification performance. The findings from this research will be presented and discussed in detail in the results chapter. The accuracy, precision, recall, and F1-score metrics will be used to evaluate the performance of the different machine learning models in classifying music genres. The discussion will also explore the strengths and limitations of the proposed approach, as well as potential areas for future research and improvement. In conclusion, this project aims to contribute to the field of music genre classification by leveraging the power of machine learning techniques. By developing a robust framework for analyzing and categorizing music genres automatically, this research has the potential to enhance music recommendation systems, music search engines, and other applications that rely on accurate genre information.