Application of Machine Learning Algorithms for Automatic Music Composition

Thesis Abstract

**Abstract
** This thesis investigates the application of machine learning algorithms for automatic music composition, aiming to explore how artificial intelligence techniques can assist in generating musical compositions. The project delves into the integration of music theory with computational methods to develop algorithms that can compose music autonomously. The use of machine learning in music composition is becoming increasingly popular due to its ability to analyze vast amounts of musical data and generate compositions that align with established musical conventions and styles. The research begins with an introduction to the field of automatic music composition, highlighting the significance of leveraging machine learning algorithms in this domain. The background of the study provides insights into the evolution of music composition techniques and the emergence of artificial intelligence in music creation. The problem statement identifies the challenges and limitations faced in traditional music composition methods, emphasizing the need for innovative approaches to enhance the creative process. The objectives of the study include developing machine learning models capable of generating original musical compositions, evaluating the effectiveness of these algorithms in creating music that resonates with human listeners, and exploring the potential applications of automated music composition in various industries. The limitations of the study acknowledge the complexities involved in capturing the nuances of human creativity through computational means, as well as the challenges in evaluating the emotional and artistic qualities of machine-generated music. The research methodology chapter outlines the approach taken to train and evaluate machine learning models for music composition, including data collection, feature engineering, model training, and performance evaluation metrics. The literature review chapter presents a comprehensive analysis of existing studies and technologies related to automatic music composition, highlighting key advancements, challenges, and future research directions in the field. Chapter four delves into the discussion of findings, presenting the results of experiments conducted to assess the performance of machine learning algorithms in generating musical compositions. The chapter explores the quality, creativity, and stylistic coherence of the generated music, comparing it with compositions created by human musicians. The conclusion chapter summarizes the key findings of the research, discusses the implications of using machine learning in music composition, and proposes recommendations for future studies in this area. In conclusion, this thesis contributes to the growing body of research on automatic music composition by demonstrating the potential of machine learning algorithms to create original and engaging musical pieces. By bridging the gap between technology and artistry, this study opens up new possibilities for exploring the intersection of music theory and artificial intelligence, paving the way for innovative applications in music production and composition.