Analysis and Creation of Music using Artificial Intelligence Techniques

Thesis Abstract

Abstract
The integration of artificial intelligence (AI) techniques in music analysis and creation has revolutionized the music industry by enabling novel approaches to understanding musical patterns, creating new compositions, and personalizing music experiences. This thesis explores the utilization of AI in the analysis and creation of music, aiming to enhance the efficiency and creativity of music production processes. The study investigates the application of machine learning algorithms, deep learning models, and neural networks to analyze music data, extract meaningful patterns, and generate music compositions autonomously. Chapter One provides an introduction to the research topic, presenting the background of the study, problem statement, objectives, limitations, scope, significance, and structure of the thesis. The chapter also defines key terms related to the research topic to establish a common understanding of the concepts discussed throughout the thesis. Chapter Two presents a comprehensive literature review that examines existing research and developments in the field of AI-driven music analysis and creation. The review covers topics such as music information retrieval, music generation models, AI-based music recommendation systems, and the impact of AI on the music industry. Chapter Three details the research methodology employed in this study, including the selection of datasets, the implementation of AI algorithms, and the evaluation metrics used to assess the performance of the models. The chapter outlines the data preprocessing steps, model training procedures, and validation techniques used to ensure the accuracy and reliability of the results obtained. Chapter Four presents a detailed discussion of the findings derived from the implementation of AI techniques in music analysis and creation. The chapter explores the effectiveness of different AI models in analyzing music features, generating music compositions, and predicting user preferences based on listening patterns. Chapter Five concludes the thesis by summarizing the key findings, discussing the implications of the research results, and proposing future directions for further research in the field of AI-driven music analysis and creation. The chapter also highlights the practical applications of AI in the music industry and the potential benefits of integrating AI technologies into music production processes. Overall, this thesis contributes to the growing body of knowledge on the application of artificial intelligence techniques in music analysis and creation, offering insights into the transformative potential of AI for advancing the field of music technology and enhancing the music listening experience for audiences worldwide.