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.

Thesis Overview

The project titled "Analysis of Music Emotion Recognition Techniques using Machine Learning" aims to explore the application of machine learning algorithms in recognizing emotions in music. Music is a powerful medium that can evoke a wide range of emotions in listeners. Understanding and analyzing these emotional cues in music can have significant implications for various fields such as music recommendation systems, music therapy, and affective computing. The research will begin with a comprehensive review of existing literature on music emotion recognition techniques and machine learning algorithms. This review will provide a foundation for understanding the current state of the art in the field and identify gaps that the research seeks to address. The study will then focus on developing and implementing machine learning models for recognizing emotions in music. Various feature extraction techniques will be explored to capture the emotional content of music, and different machine learning algorithms such as deep learning models and ensemble methods will be applied to classify and predict emotions. The research methodology will involve collecting a diverse dataset of music tracks annotated with emotional labels. The dataset will be preprocessed and feature extraction techniques will be applied to extract relevant features from the audio signals. The machine learning models will be trained and evaluated using standard metrics to assess their performance in emotion recognition tasks. The findings of the study will be discussed in detail, highlighting the effectiveness of different machine learning techniques in recognizing emotions in music. The implications of the research findings for music-related applications and future research directions will also be explored. Overall, this research aims to contribute to the growing body of knowledge in the field of music emotion recognition and demonstrate the potential of machine learning algorithms in analyzing emotional cues in music. By improving our understanding of how machines can recognize and interpret emotions in music, this study has the potential to enhance the development of intelligent music systems and applications that cater to the emotional needs of users.