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Table of Contents:

Chapter 1: Introduction
1.1 Background of the Study
1.2 Statement of the Problem
1.3 Objectives of the Study
1.4 Research Questions
1.5 Significance of the Study
1.6 Scope and Limitations of the Study
1.7 Organization of the Thesis

Chapter 2: Literature Review
2.1 Overview of Music Theory
2.2 Introduction to Topological Data Analysis
2.3 Previous Studies on Topological Data Analysis in Music Theory
2.4 Gaps in Existing Literature

Chapter 3: Research Methodology
3.1 Research Design
3.2 Data Collection Methods
3.3 Data Analysis Techniques
3.4 Research Ethics

Chapter 4: Discussion of Findings
4.1 Analysis of Topological Data in Music Theory
4.2 Comparison with Traditional Music Analysis Methods
4.3 Interpretation of Results
4.4 Implications for Music Theory

Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Contributions to Music Theory
5.3 Recommendations for Future Research
5.4 Conclusion

Brief Overview of Thesis:

The thesis on “Topological Data Analysis in Music Theory” explores the application of topological data analysis (TDA) in analyzing music compositions. The study aims to investigate how TDA can be used to uncover hidden patterns and structures in musical data that traditional music theory methods may overlook. The research methodology involves collecting music data from various sources, applying TDA techniques to analyze the data, and interpreting the results to gain insights into the underlying musical structures.

The literature review examines existing studies on TDA and music theory, highlighting the gaps in current research and the potential for TDA to enhance our understanding of music composition. The discussion of findings presents the analysis of music data using TDA, comparing the results with traditional music analysis methods, and discussing the implications for music theory.

Overall, the thesis contributes to the field of music theory by demonstrating the utility of TDA in uncovering complex relationships and structures in music compositions. The conclusions summarize the key findings and offer recommendations for future research in this exciting and innovative area of study.

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