The AI Music Problem

Why Machine Learning Conflicts With Musical Creativity
1. Auflage 2025
ISBN: 978-1-032-95975-7
Verlag: Taylor & Francis Ltd

Buch, Englisch, 198 Seiten, Format (B × H): 152 mm x 229 mm

As artificial intelligence has become increasingly sophisticated at producing many forms of media, it continues to be a challenge for AI to reliably and independently write convincing, enjoyable music without human guidance. The AI Music Problem: Why Machine Learning Conflicts With Musical Creativity asks why producing and constructing music is difficult for AI, with an investigation that straddles the technical, the musical, and the aesthetic. Bringing together the perspectives of the humanities and computer science, the author shows how the difficulties that music poses for AI connect to larger questions about music, artistic expression, and the increasing ubiquity of artificial intelligence.

Taking a wide view of the current landscape of machine learning and Large Language Models, The AI Music Problem offers a resource for students, researchers, and the public to understand the broader issues surrounding musical AI on both technical and artistic levels. The author breaks down music theory and computer science concepts with clear and accessible explanations, synthesizing both the technical with more holistic and human-centric analyses. Enabling readers of all backgrounds to understand how contemporary AI models work, and why music is often a mismatch for those processes, this insightful book is relevant to all those engaging with the intersection between AI and musical creativity today.

Zielgruppe

Professional Practice & Development and Undergraduate Advanced

Autoren/Hrsg.

White, Christopher W.

Fachgebiete

• Geisteswissenschaften Musikwissenschaft Musikwissenschaft Allgemein Musiktheorie, Musikästhetik, Kompositionslehre
• Mathematik | Informatik EDV | Informatik Professionelle Anwendung Digitale Musik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken

Inhaltsverzeichnis

1. The Problems Facing Musical Artificial Intelligence
2. History, Engineering, and Motivations Behind Generative Musical AI
3. Collecting Examples for Musical Datasets
4. Representing Music to Artificial Intelligence
5. Musical Structure is Hard to Learn
6. Interpreting Musical Artificial Intelligence

Über Autor(innen)

Christopher W. White is Associate Professor of Music Theory at the University of Massachusetts Amherst.