Analysis of Music Emotion Recognition using Machine Learning Techniques

Thesis Abstract

Abstract
This thesis presents a comprehensive analysis of music emotion recognition utilizing machine learning techniques. The study focuses on the development and implementation of algorithms to automatically detect and classify emotions expressed in music. The research investigates the significance of recognizing emotions in music and explores the potential applications of such technology in various domains, including entertainment, healthcare, and marketing. The primary objective is to enhance the accuracy and efficiency of emotion recognition systems through the utilization of machine learning methods. Chapter 1 provides an introduction to the research topic, discussing the background of the study, the problem statement, research objectives, limitations, scope, significance, and the structure of the thesis. The chapter also includes a definition of key terms related to music emotion recognition and machine learning. Chapter 2 consists of a detailed literature review that examines existing studies and methodologies related to music emotion recognition and machine learning. The review covers topics such as feature extraction, emotion classification algorithms, datasets used in emotion recognition research, and evaluation metrics for assessing the performance of emotion recognition systems. Chapter 3 outlines the research methodology employed in this study, including the selection of datasets, feature extraction techniques, machine learning algorithms, and evaluation methods. The chapter also discusses the preprocessing steps involved in preparing the data for analysis and the experimental setup used to train and test the emotion recognition models. Chapter 4 presents an in-depth discussion of the findings obtained from the experiments conducted in this research. The chapter analyzes the performance of different machine learning algorithms in recognizing emotions in music and evaluates the effectiveness of various feature extraction methods. The results are compared and interpreted to identify the strengths and limitations of the proposed approach. Chapter 5 concludes the thesis by summarizing the key findings of the study and discussing their implications for future research in the field of music emotion recognition. The chapter also highlights the practical applications of the developed algorithms and provides recommendations for improving the accuracy and usability of emotion recognition systems. Overall, this thesis contributes to the advancement of music emotion recognition technology by demonstrating the effectiveness of machine learning techniques in accurately detecting and classifying emotions in music. The research findings have the potential to enhance the user experience in various applications, such as music recommendation systems, emotional analysis in multimedia content, and personalized music therapy interventions.