The AI Music Problem
Why Machine Learning Conflicts With Musical Creativity
Seiten
2025
Routledge (Verlag)
978-1-032-95976-4 (ISBN)
Routledge (Verlag)
978-1-032-95976-4 (ISBN)
- Noch nicht erschienen (ca. Juni 2025)
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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.
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.
Christopher White is Associate Professor of Music Theory at the University of Massachusetts Amherst.
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
| Erscheint lt. Verlag | 23.6.2025 |
|---|---|
| Zusatzinfo | 12 Line drawings, black and white; 13 Halftones, black and white; 25 Illustrations, black and white |
| Verlagsort | London |
| Sprache | englisch |
| Maße | 152 x 229 mm |
| Themenwelt | Kunst / Musik / Theater ► Musik ► Musiktheorie / Musiklehre |
| Mathematik / Informatik ► Informatik | |
| ISBN-10 | 1-032-95976-2 / 1032959762 |
| ISBN-13 | 978-1-032-95976-4 / 9781032959764 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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Buch | Softcover (2021)
Philipp Reclam (Verlag)
CHF 12,90
