Applications of Artificial Intelligence in Music

Professor Introduction

Q. W | Ph.D. in Linguistics

Home Institute：University of Rochester

Project Description

This course aims to explore the applications of artificial intelligence (AI) in the realm of digital music, focusing on recommendation systems and natural language processing (NLP). These fields are at the intersection of statistics and computer science and are crucial research areas within AI with broad applications. AI has significant applications in digital music, which is stored in digital format and can be created, edited, and played using music editing software, offering flexibility and speed that traditional records lack. Through analyzing individual music preferences and online behavior, AI can recommend music effectively.

Project Keywords

Project Outline

Part 1: Introduction to AI in Music
• Overview of fundamental concepts in AI and its applications in music.
• Key principles and historical context of digital music and AI technologies.
• Applications and relevance to current computer science and music research.

Part 2: Theoretical Frameworks
• Exploration of theories related to AI, machine learning, and music recommendation systems.
• Examination of computational and statistical methods (e.g., collaborative filtering, content-based filtering).
• Discussion on the role of AI in enhancing music experiences.

Part 3:  Literature Review
• In-depth reading and analysis of existing literature on AI applications in music.
• Identification of major viewpoints and debates in current research.
• Discussion on research gaps and the significance of this study.

Part 4:  Research Methods
• Introduction to qualitative and quantitative research methods used in AI and music studies.
• Techniques for data analysis, machine learning algorithms, and NLP.
• Practical exercises to develop research skills.

Part 5:  Recommendation Systems in Music
• Detailed exploration of music recommendation systems and their algorithms.
• Examination of collaborative filtering, content-based filtering, and hybrid methods.
• Comparative analysis of different recommendation systems.

Part 6:  Natural Language Processing in Music
• Analysis of how NLP techniques are used in music applications (e.g., lyric analysis, genre classification).
• Discussion on the challenges and advancements in NLP for music.
• Real-world examples and case studies.

Part 7: Empirical Results and Discussion
• Presentation and interpretation of research findings.
• Discussion of the significance and impact of findings.
• Identification of research limitations and suggestions for improvement.

Part 8: AI and Digital Music Creation
• Exploration of AI tools for music composition and editing.
• Discussion on the ethical and creative implications of AI-generated music.
• Practical examples of AI in music production.

Part 9:  Future Research Directions
• Discussion of current trends and challenges in AI applications in music.
• Identification of open problems and future research directions.
• Encouragement for student-led research projects.

Part 10:  Conclusion and Summary
• Summary of the main findings and conclusions.
• Discussion of contributions to computer science and music research.
• Recommendations for future research and practice.

Part 11: Research Paper and Presentation
• Guidance on writing a well-structured research paper.
• Tips for effective academic writing.
• Instructions on preparing and delivering an engaging oral presentation.

Suitable for

High School Students:  
• Interested in computer science, linguistics, and applied mathematics, preparing for advanced studies or competitions.
• Basic knowledge of programming and statistical principles.

University Students:
• Majoring in computer science, linguistics, applied mathematics, statistics, or related fields, seeking to deepen understanding and engage in research.
• Familiar with basic AI theories and research methodologies.