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.

Thesis Overview

The project titled "Analysis of Music Emotion Recognition Using Machine Learning Techniques" aims to explore the application of machine learning algorithms in recognizing and analyzing emotions in music. Emotions play a crucial role in how music is perceived and experienced by listeners, making it a fascinating area of study. By leveraging machine learning techniques, this research seeks to develop a system that can automatically detect and classify emotions in music, thereby enhancing our understanding of the emotional content embedded in musical pieces. The research will begin with a comprehensive review of existing literature on music emotion recognition, machine learning algorithms, and their applications in music analysis. This review will provide a solid foundation for understanding the current state of research in the field and identifying gaps that this study aims to address. The methodology chapter will detail the approach taken to collect and analyze music data, preprocess the audio files, extract relevant features that capture emotional cues in music, and train machine learning models for emotion recognition. Various machine learning algorithms such as deep learning models, support vector machines, and random forests will be explored and evaluated for their effectiveness in classifying emotions in music. The findings chapter will present the results of the experiments conducted, including the accuracy of emotion recognition models, the impact of different features on classification performance, and any insights gained from the analysis of emotional patterns in music. The discussion section will delve into the implications of the findings, the limitations of the study, and potential future research directions in the field of music emotion recognition. In conclusion, this research project holds the promise of advancing our understanding of how machine learning techniques can be leveraged to recognize and analyze emotions in music. By developing a robust system for music emotion recognition, this study aims to contribute to the field of music information retrieval and provide valuable insights into the intricate relationship between music and emotions.