AI Music Generation Using Neural Networks

Thesis Abstract

Abstract
Music generation is a complex and creative process that has traditionally been the domain of skilled composers and musicians. With recent advancements in artificial intelligence (AI) and machine learning, there is growing interest in using neural networks to generate music automatically. This thesis explores the application of neural networks in the field of music generation, focusing on the use of AI algorithms to compose original musical pieces. The introduction provides an overview of the project, discussing the background of the study and the problem statement that motivates the research. The objective of the study is to develop a neural network model capable of generating music autonomously, with a specific focus on creating compositions that exhibit creativity and musicality. The limitations and scope of the study are also outlined, along with the significance of using AI in music generation. Chapter two presents a comprehensive literature review of existing research in the field of AI music generation. This chapter discusses various approaches and techniques used in previous studies, highlighting the strengths and limitations of different models. The literature review covers topics such as recurrent neural networks, generative adversarial networks, and deep learning architectures commonly employed in music generation tasks. Chapter three details the research methodology employed in developing the AI music generation model. The chapter outlines the data collection process, preprocessing steps, and model training procedures. It also discusses the evaluation metrics used to assess the quality of the generated music and the experimental setup for testing the neural network model. Chapter four presents a detailed discussion of the findings obtained from the experiments conducted with the AI music generation model. The chapter analyzes the generated music samples, evaluating their musical quality, creativity, and coherence. The results are compared against human-composed music to assess the effectiveness of the neural network in creating original compositions. Finally, chapter five concludes the thesis with a summary of the research findings and their implications. The chapter discusses the contributions of the study to the field of AI music generation and outlines potential future research directions. The conclusion highlights the challenges and opportunities in using neural networks for music composition and emphasizes the importance of creativity and human input in the development of AI-generated music. In conclusion, this thesis presents a comprehensive investigation into the application of neural networks for music generation. The research contributes to the growing body of knowledge in AI-generated music and provides insights into the capabilities and limitations of using AI algorithms to compose music autonomously. The findings of this study offer valuable insights for researchers, musicians, and technology enthusiasts interested in exploring the intersection of artificial intelligence and music composition.