Analysis and Classification of Music Emotion using Machine Learning Techniques

Project Abstract

Music is a powerful form of expression that can evoke a wide range of emotions in listeners. Understanding and categorizing these emotional responses is essential for various applications in the field of music, such as personalized music recommendations, emotion-based music therapy, and enhancing user experiences in music streaming platforms. This research project focuses on the analysis and classification of music emotions using machine learning techniques. The primary objective of this study is to develop a robust system that can automatically classify music tracks based on the emotions they evoke in listeners. To achieve this objective, a comprehensive review of existing literature on music emotion analysis and machine learning techniques will be conducted in Chapter Two. This literature review will provide a solid theoretical foundation for the research and identify gaps in current research that this study aims to fill. In Chapter Three, the research methodology will be outlined, detailing the data collection process, feature extraction techniques, and the machine learning algorithms to be utilized for emotion classification. The methodology will also include the evaluation metrics and procedures to assess the performance of the classification model. Chapter Four will present a detailed discussion of the findings obtained from the experimental evaluation of the proposed system. The results will be analyzed and interpreted to provide insights into the effectiveness and limitations of the machine learning techniques employed for music emotion classification. The chapter will also discuss practical implications and potential applications of the research findings. Finally, Chapter Five will present the conclusion and summary of the research project. The key findings, contributions, and implications of the study will be summarized, along with recommendations for future research in this area. The research abstract concludes by emphasizing the significance of developing automated systems for music emotion analysis and classification using machine learning techniques and their potential impact on various music-related domains. In conclusion, this research project aims to contribute to the field of music emotion analysis by developing a robust system that can effectively classify music tracks based on the emotions they evoke. By leveraging machine learning techniques, this study seeks to enhance our understanding of music emotions and pave the way for innovative applications in the realm of music technology and user experience.

Project Overview