White | The AI Music Problem | Buch | 978-1-032-95976-4 | sack.de

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

White

The AI Music Problem

Why Machine Learning Conflicts With Musical Creativity
1. Auflage 2025
ISBN: 978-1-032-95976-4
Verlag: Taylor & Francis Ltd

Why Machine Learning Conflicts With Musical Creativity

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

ISBN: 978-1-032-95976-4
Verlag: Taylor & Francis Ltd

Music poses unique and complex challenges for artificial intelligence, even as 21st-century AI grows ever more adept at generating compelling content. The AI Music Problem: Why Machine Learning Conflicts With Musical Creativity probes the challenges behind AI-generated music, 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 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 book is relevant to all those engaging with the intersection between AI and musical creativity today.

White The AI Music Problem jetzt bestellen!

Zielgruppe

Professional Practice & Development and Undergraduate Advanced

Autoren/Hrsg.

White, Christopher W.

Fachgebiete

Weitere Infos & Material

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 an Artificial Intelligence

5. Musical Structure is Hard to Learn

6. Interpreting Musical Artificial Intelligence

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

Ihre Fragen, Wünsche oder Anmerkungen
Vorname*
Nachname*
Ihre E-Mail-Adresse*
Kundennr.
Ihre Nachricht*
Lediglich mit * gekennzeichnete Felder sind Pflichtfelder.
Wenn Sie die im Kontaktformular eingegebenen Daten durch Klick auf den nachfolgenden Button übersenden, erklären Sie sich damit einverstanden, dass wir Ihr Angaben für die Beantwortung Ihrer Anfrage verwenden. Selbstverständlich werden Ihre Daten vertraulich behandelt und nicht an Dritte weitergegeben. Sie können der Verwendung Ihrer Daten jederzeit widersprechen. Das Datenhandling bei Sack Fachmedien erklären wir Ihnen in unserer Datenschutzerklärung.