Analysis of Music Emotion Recognition Techniques Using Artificial Intelligence

Thesis Abstract

Abstract
The advent of artificial intelligence has revolutionized various industries, including the field of music analysis. This thesis explores the application of artificial intelligence in the realm of music emotion recognition, aiming to enhance the understanding of how machines can interpret and classify emotional content in music. The study delves into the development and evaluation of algorithms and techniques that enable machines to recognize and categorize emotional cues in music tracks. Through a comprehensive literature review, the research highlights existing methodologies and approaches in music emotion recognition, emphasizing the role of artificial intelligence in advancing the field. Chapter One sets the stage for the research by providing an introduction to the topic, presenting the background of the study, identifying the problem statement, outlining the objectives of the study, discussing the limitations and scope of the research, elucidating the significance of the study, and presenting the structure of the thesis. This chapter also defines key terms and concepts essential for understanding the subsequent chapters. Chapter Two conducts an in-depth literature review on music emotion recognition and artificial intelligence, analyzing ten critical studies that have contributed significantly to the field. The review explores various algorithms, techniques, and models employed in music emotion recognition, providing insights into the evolution and current state of the research landscape. Chapter Three focuses on the research methodology employed in this study, detailing the research design, data collection methods, data analysis techniques, and evaluation criteria. It also discusses the selection of datasets, preprocessing steps, feature extraction methods, and the implementation of machine learning algorithms for music emotion recognition. Chapter Four presents a detailed discussion of the findings derived from the experimentation and analysis conducted in the study. It evaluates the performance of different artificial intelligence models in recognizing and categorizing emotions in music, discussing the strengths, weaknesses, and implications of the results obtained. Finally, Chapter Five concludes the thesis by summarizing the key findings, discussing the implications of the research outcomes, and offering recommendations for future research directions. The study underscores the potential of artificial intelligence in enhancing music emotion recognition, paving the way for innovative applications in music analysis and emotional intelligence technologies. In conclusion, this thesis contributes to the growing body of knowledge on music emotion recognition by showcasing the capabilities of artificial intelligence in interpreting emotional content in music. The research findings provide valuable insights for researchers, practitioners, and developers seeking to leverage AI technologies for enhancing music analysis and emotion recognition systems.