Visualisierungskolloquium SPECIAL: Prof. Dr. Meinard Müller

Abstract:

Music information retrieval (MIR) is a dynamic research field at the intersection of engineering and the humanities, connecting disciplines such as signal processing, machine learning, musicology, and digital humanities. In this presentation, we explore learning in MIR from both technological and educational perspectives, using music as a tangible application domain. Our focus is on integrating deep learning with traditional engineering approaches to develop explainable hybrid models. By collaborating with domain experts and utilizing specialized music corpora, we demonstrate how computational tools can advance musicological research while uncovering data biases and confounding factors in modern technologies. Furthermore, we emphasize how music can facilitate interactive learning in technical disciplines, promoting innovation at the crossroads of technology and education.

Bio:

Meinard Müller received the Diploma degree (1997) in mathematics and the Ph.D. degree (2001) in computer science from the University of Bonn, Germany. After his postdoctoral studies (2001-2003) in Japan and his habilitation (2003-2007) in multimedia retrieval in Bonn, he worked as a senior researcher at Saarland University and the Max-Planck Institut für Informatik (2007-2012). Since 2012, he has held a professorship for Semantic Audio Signal Processing at the International Audio Laboratories Erlangen, a joint institute of the Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) and the Fraunhofer Institute for Integrated Circuits IIS. His research interests include music processing, music information retrieval, audio signal processing, and motion processing. He wrote a monograph titled "Information Retrieval for Music and Motion" (Springer 2007) and a textbook titled "Fundamentals of Music Processing" (Springer 2015). In 2020, he was elevated to IEEE Fellow for contributions to music signal processing.