In diesem Workshop wird die Rolle von Künstlichen Intelligenz, von Komposition gestützt durch Machine Learning und von Empfehlungssystemen im Prozess der Musikproduktion. Wir besprechen die Rezeption und das vorherrschende Image unter professionellen MusikproduzentInnen und -schaffenden, einschließlich der potenziellen Bedrohungen für die künstlerische Originalität. Wir kontrastieren diese Sichtweise und besprechen die Potenziale der KI-Technologie für eine Demokratisierung des Musikmachens durch den erleichterten Zugang zum Musikschaffen.
Christine Bauer (AT)
Christine Bauer is a Senior Postdoc Researcher at Johannes Kepler University (JKU) Linz, Austria.
Before joining JKU, she researched at Carnegie Mellon University in Pittsburgh, PA, USA, University of Cologne, Germany, and WU in Vienna, Austria.
Her research activities are driven by her interdisciplinary background. She holds a Doctoral degree in Social and Economic Sciences and a Diploma degree in International Business Administration, both from University of Vienna, Austria. In addition, she holds a Master degree in Business Informatics from TU Wien, Austria.
Her research centers on intelligent, algorithmically-driven systems. Thereby, she takes a human-centered approach where technology follows humans’ and the society’s needs. Currently, she lays a focus on music recommender systems. She received the prestigious Elise Richter grant funded by Austrian Science Fund (FWF) and holds several best paper awards as well as awards for her reviewing activities.
Peter Knees (AT)
Peter Knees is an Assistant Professor of the Faculty of Informatics, TU Wien, Austria. He holds a Master degree in Computer Science from TU Wien and a PhD in the same field from Johannes Kepler University (JKU) Linz, Austria. For over 15 years, he has been an active member of the Music Information Retrieval research community, reaching out to the related fields of multimedia and text information retrieval, recommender systems, and the digital arts.
His research activities center on music search engines and interfaces as well as music recommender systems, and more recently, on smart(er) tools for music creation. His work sound/tracks received a Jury Recommendation at the 14th Japan Media Arts Festival.
Richard Vogl (AT)
Richard Vogl’s works are situated at the intersection of music and artificial intelligence. In the recent past, he worked as a researcher at the Institute of Computational Perception at Johannes Kepler University Linz (project: GiantSteps) and at the Faculty of Informatics (project: SmarterJam) at TU Wien. His main interests are deep learning, signal processing, and music information research.
Hansi Raaber (AT)
Hansi Raaber grew up in Austria, he still lives there. He really likes math, music and programming, so making digital music is the perfect rabbit hole for him.