Making Machine Musicians

AI for Live Musical Performance
1. Auflage 2026
ISBN: 978-1-032-87742-6
Verlag: Taylor & Francis Ltd

Buch, Englisch, 298 Seiten, Format (B × H): 156 mm x 234 mm, Gewicht: 453 g

Making Machine Musicians: AI for Live Musical Performance explores the integration of artificial intelligence (AI) technologies into live music performances.

Offering a comprehensive guide to developing “machine musicians” capable of interactive improvisation and real-time collaboration with human performers, this book introduces readers to cutting-edge trends in live musical AI. Chapters combine theoretical discussions, practical tutorials, and hands-on projects, providing step-by-step instructions for building autonomous music systems, experimenting with interactive AI tools, and exploring creative applications of AI in live performance. Accompanying software and tutorials ensure accessibility for musicians of all levels, fostering both technical fluency and artistic innovation. Whether used as a textbook, a self-study guide, or a practical manual, it equips readers with the knowledge and tools to explore the emerging field of musical AI and its potential to transform the way we create and experience music.

This book will appeal to music technology professionals, educators, and novice musicians alike, this book serves as a valuable resource for anyone interested in the intersection of AI and live music.

Zielgruppe

Professional Practice & Development and Undergraduate Advanced

Autoren/Hrsg.

Palamara, Jason

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Geisteswissenschaften Musikwissenschaft Musikwissenschaft Allgemein
• Wirtschaftswissenschaften Wirtschaftssektoren & Branchen Medien-, Informations und Kommunikationswirtschaft Musikindustrie
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken

Inhaltsverzeichnis

0. Who Is This Book For? 1. A Conversation with Your Computer…  2. Defining AI Part I: Top Down  3. Understanding Algorithms and Algorithmic Music 4. Symbolic AI, Part I 5. Symbolic AI, Part II 6. Machine Decisions: Symbolic AI, Part III  7. Markov Models 8. Music and Language Part II: Bottom-Up 9. Connectionism Part I: MIDI From Audio 10. Connectionism, Part II: Turning Estimations Into Actions 11. Continuation…

Über Autor(innen)

Jason Palamara is an Assistant Professor of Music Technology in Indiana University, Indianapolis, USA. He specializes in developing AI-enabled performance technologies for music. He directs the Indianapolis-based Machine Musician Lab that pursues multiple AI music and performance initiatives. He maintained a long-term creative partnership with the late percussionist-composer and technologist Scott Deal, with whom he developed the interactive AVATAR software. AVATAR is an autonomous music system that uses machine learning to play along with live improvisation. He is also the founder/director of IU’s 30+ member DISEnsemble (Diverse Instrument Symphonic Ensemble), which counts an autonomous digital performer named the Emergency Musical Hologram (EMH) among its performers.