Analysis of Music Emotion Recognition using Machine Learning Techniques

Thesis Abstract

Abstract
Music is a powerful medium that can evoke a wide range of emotions in listeners. Understanding and analyzing the emotional content of music have significant implications for various applications, including music recommendation systems, mood-based playlists, and emotional therapy. This research project focuses on the analysis of music emotion recognition using machine learning techniques. The primary objective is to develop a system that can automatically recognize and classify emotions in music tracks. The thesis begins with an introduction that provides background information on the importance of music emotion recognition and the challenges associated with manual annotation of emotional content in music. The problem statement highlights the need for automated techniques to analyze music emotions efficiently. The objectives of the study include developing a robust machine learning model for emotion recognition and evaluating its performance using a diverse dataset of music tracks. The literature review in Chapter Two covers ten key aspects related to music emotion recognition, including existing methodologies, datasets, feature extraction techniques, and evaluation metrics. This comprehensive review provides a solid foundation for the research methodology outlined in Chapter Three. The research methodology includes data collection, preprocessing, feature extraction, model training, and evaluation processes. Various machine learning algorithms such as deep learning, support vector machines, and random forests are explored for emotion classification tasks. Chapter Four presents an elaborate discussion of the findings obtained from the experimental evaluation of the proposed music emotion recognition system. The results showcase the effectiveness of the machine learning model in accurately identifying and categorizing emotions in music tracks. The discussion also addresses the limitations of the study, including dataset biases, feature selection challenges, and model interpretability issues. Finally, Chapter Five offers a comprehensive conclusion and summary of the project thesis. The significance of the study lies in its potential to enhance music recommendation systems, personalized playlists, and emotional analysis tools in various domains. The research contributes to the growing field of affective computing and demonstrates the feasibility of using machine learning techniques for music emotion recognition. Future research directions and potential improvements for the proposed system are also discussed. In conclusion, the "Analysis of Music Emotion Recognition using Machine Learning Techniques" thesis presents a novel approach to automatically analyze and classify emotions in music. The integration of machine learning algorithms with music processing techniques opens up new opportunities for understanding the emotional impact of music on listeners. This research project contributes valuable insights to the field of music information retrieval and lays the groundwork for further advancements in music emotion recognition technologies.