Analysis of Music Emotion Recognition Techniques using Machine Learning

Thesis Abstract

Abstract
This thesis presents a comprehensive study on the analysis of music emotion recognition techniques utilizing machine learning algorithms. Music is a powerful medium that has the ability to evoke various emotions in listeners. Understanding and recognizing these emotional cues in music can have significant implications in areas such as music recommendation systems, entertainment industry, and mental health applications. Machine learning techniques have shown promise in automatically detecting emotions in music, offering a scalable and efficient solution to this challenging task. The research begins with an exploration of the background of music emotion recognition and the existing challenges in accurately detecting emotions in music. The problem statement highlights the need for advanced algorithms to effectively capture the subtle nuances of emotions expressed in music. The objectives of the study include developing and evaluating machine learning models for emotion recognition in music, aiming to improve accuracy and efficiency in this domain. The limitations of the study are acknowledged, such as the availability of labeled datasets and the subjective nature of emotional perception in music. The scope of the study focuses on analyzing various machine learning algorithms, including deep learning models, for their effectiveness in music emotion recognition tasks. The significance of the research lies in its potential to enhance user experience in music applications and contribute to the advancement of emotion recognition technology. The structure of the thesis is outlined, detailing the organization of chapters and the flow of content. Chapter One provides an introduction to the research topic, setting the stage for the subsequent chapters. Chapter Two reviews the existing literature on music emotion recognition, exploring different methodologies and approaches used in previous studies. Chapter Three details the research methodology, including data collection, preprocessing techniques, feature extraction, model selection, and evaluation metrics. The chapter also discusses the experimental setup and procedures used to train and test the machine learning models on music datasets. Chapter Four presents a comprehensive discussion of the findings, highlighting the performance of different machine learning algorithms in recognizing emotions in music. The results are analyzed, and insights are drawn regarding the strengths and limitations of each approach. Finally, Chapter Five concludes the thesis by summarizing the key findings, discussing the implications of the research, and suggesting future directions for further exploration in the field of music emotion recognition using machine learning. Overall, this thesis contributes to the growing body of knowledge in music emotion recognition and demonstrates the potential of machine learning techniques in enhancing our understanding of emotional cues in music.