Analysis of Music Emotion Recognition Using Machine Learning Techniques

Thesis Abstract

Abstract
Music has the remarkable ability to convey emotions and evoke strong feelings in listeners. Understanding and analyzing these emotional cues in music can significantly impact various fields, including music recommendation systems, mental health therapy, and human-computer interaction. This thesis focuses on the application of machine learning techniques to analyze music emotion recognition. The objective is to develop a system that can accurately identify and classify emotions expressed in music, paving the way for enhanced user experiences and personalized music recommendations. Chapter one provides an introduction to the study, discussing the background of music emotion recognition, the problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of key terms. Chapter two presents a comprehensive literature review encompassing ten key aspects related to music emotion recognition, machine learning techniques, and previous studies in this domain. Chapter three outlines the research methodology, detailing the data collection process, feature extraction methods, machine learning algorithms employed, evaluation metrics, and experimental setup. This chapter also covers data preprocessing, model training, validation techniques, and performance evaluation criteria. Furthermore, it discusses the ethical considerations and potential biases associated with the research. In chapter four, the findings of the study are elaborately discussed, including the results of the machine learning models in classifying music emotions, the accuracy rates achieved, the impact of different feature sets on classification performance, and the comparison of various algorithms. Additionally, this chapter explores the interpretability of the models and the implications of the results for real-world applications. Finally, chapter five presents the conclusion and summary of the thesis, encapsulating the key findings, contributions, limitations, and future research directions. The study highlights the potential of machine learning techniques in enhancing music emotion recognition capabilities and emphasizes the importance of considering user preferences and context in developing personalized music systems. In conclusion, this thesis contributes to the evolving field of music emotion recognition by leveraging machine learning techniques to analyze and classify emotions in music. The findings offer valuable insights into the effectiveness of different algorithms and feature sets in capturing emotional nuances in music. Ultimately, this research aims to advance the development of intelligent systems that can understand and respond to human emotions expressed through music, promoting enhanced user experiences and personalized interactions.