Thesis Abstract

Abstract
This research project focuses on the analysis of music emotion recognition using deep learning techniques. Music emotion recognition is a complex and challenging task due to the subjective nature of emotions and the intricate patterns present in music. Deep learning has emerged as a powerful tool for processing and analyzing complex data, making it a promising approach for addressing the challenges of music emotion recognition. This study aims to investigate the effectiveness of deep learning techniques in recognizing and classifying emotions in music. Chapter One provides an introduction to the research topic, presenting the background of the study, defining the problem statement, outlining the objectives, discussing the limitations and scope of the study, highlighting the significance of the research, and presenting the structure of the thesis. The chapter also includes a definition of key terms related to music emotion recognition and deep learning. Chapter Two presents a comprehensive literature review on music emotion recognition and deep learning techniques. The chapter explores existing research studies, methodologies, algorithms, and datasets related to music emotion recognition and deep learning in the context of music analysis. Chapter Three outlines the research methodology employed in this study. The chapter describes the data collection process, preprocessing steps, feature extraction techniques, deep learning models utilized for emotion recognition, evaluation metrics, and experimental design. It also discusses the tools and software used for data analysis and model development. Chapter Four presents a detailed discussion of the findings obtained from the experiments conducted in this research. The chapter analyzes the performance of different deep learning models in recognizing and classifying emotions in music. It also discusses the impact of various factors such as dataset size, feature selection, and model architecture on the effectiveness of emotion recognition. Chapter Five concludes the thesis by summarizing the key findings of the study, discussing the implications of the research results, and suggesting future directions for research in the field of music emotion recognition using deep learning techniques. The chapter also provides recommendations for improving the accuracy and efficiency of emotion recognition systems in music applications. In conclusion, this research contributes to the growing body of knowledge on music emotion recognition by investigating the application of deep learning techniques. The study demonstrates the potential of deep learning models in effectively recognizing and classifying emotions in music, paving the way for further advancements in the field of music analysis and emotion recognition.

Thesis Overview

The project titled "Analysis of Music Emotion Recognition using Deep Learning Techniques" aims to investigate and analyze the application of deep learning techniques in recognizing and understanding emotions conveyed through music. Emotions play a significant role in music perception and appreciation, and the ability to accurately recognize these emotions can lead to various practical applications such as personalized music recommendations, emotional music composition, and mood-based playlist generation. The research will delve into the field of music information retrieval (MIR) and the intersection of deep learning and emotion recognition. Deep learning has shown remarkable success in various domains, including computer vision, natural language processing, and audio analysis. By applying deep learning techniques to music emotion recognition, this project seeks to enhance the accuracy and efficiency of emotion detection in musical content. The research overview will include a comprehensive literature review to explore existing methodologies, datasets, and models related to music emotion recognition and deep learning. This review will provide a foundational understanding of the current state-of-the-art techniques and identify gaps and opportunities for further research. Furthermore, the project will outline a detailed research methodology encompassing data collection, preprocessing, feature extraction, model training, and evaluation. Various deep learning architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer models will be explored and adapted to the specific task of music emotion recognition. The project will also address challenges and limitations such as dataset availability, annotation quality, and model generalization across different music genres and cultural contexts. By defining the scope of the study and setting clear objectives, the research aims to provide valuable insights into the potential of deep learning for music emotion recognition. The significance of this research lies in its contribution to the evolving field of MIR and the broader impact on applications related to music analysis and recommendation systems. By advancing the state-of-the-art in music emotion recognition, this project seeks to enhance user experiences, promote emotional engagement with music, and facilitate innovative applications in the music industry and beyond. In conclusion, the research overview of "Analysis of Music Emotion Recognition using Deep Learning Techniques" highlights the importance of understanding and recognizing emotions in music through advanced computational methods. By leveraging deep learning techniques, this project aims to advance the field of music emotion recognition and pave the way for future developments in emotion-aware music technologies.