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Max Graf

Max Graf

PhD Researcher in AI & Music, developing new XR musical instruments

London, UK
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Joined December 2024
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Summary

Max Graf is a dedicated PhD researcher at Queen Mary University of London, specializing in the interdisciplinary field of AI and Music. His core research, under the PERFORM-AI project, focuses on creating innovative AI-driven mixed reality applications for musical production and performance. He aims to make music creation more accessible by leveraging 3D space, gestures, and advanced human-computer interaction in XR environments, effectively bridging the gap between traditional instruments and immersive digital experiences. ac+2
A significant innovation from Max's research is 'Netz,' an award-winning XR musical instrument. This AI-powered, hand-controlled software blends real and virtual environments, allowing users to produce sounds directly from a head-mounted display. Netz interprets subtle hand movements and gestures as expressive musical controls, showcasing his expertise in developing cutting-edge interfaces for musical expression. He has also explored multimodal hand tracking using vision and sEMG data to improve accuracy in XR musical instruments. netzxr+4
Max is an active contributor to the academic community, with publications presented at major conferences like AES AI & The Musician, CMMR, VRST, and NIME. His work often co-authored with his supervisors Mathieu Barthet and Andrew McPherson, delves into topics such as integrating interactive machine learning with XR, combining vision and EMG-based hand tracking, and exploring ergonomics of mixed reality musical interfaces. maxgraf+2

Work

Education

Projects

Writing

When XR Meets AI: Integrating Interactive Machine Learning with an XR Musical Instrument

January 1, 2024

This paper explores the integration of artificial intelligence with Extended Reality (XR) musical instruments. It discusses how AI can enhance the capabilities of XR instruments, providing more intuitive and responsive musical interaction.

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Combining Vision and EMG-Based Hand Tracking for Extended Reality Musical Instruments

January 1, 2023

This paper proposes a multimodal hand tracking system that combines vision-based hand tracking with surface electromyography (sEMG) data for finger joint angle estimation in Extended Reality Musical Instruments (XRMIs). It aims to improve tracking accuracy for gestures prone to self-occlusion.

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Reducing Sensing Errors in a Mixed Reality Musical Instrument

January 1, 2023

This paper describes the design and evaluation of Netz, a novel mixed reality musical instrument, focusing on addressing and reducing sensing errors inherent in current XR tracking technologies to improve user experience and control.

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Mixed Reality Musical Interface: Exploring Ergonomics and Adaptive Hand Pose Recognition for Gestural Control

January 1, 2022

This paper investigates the ergonomics of mixed reality musical interfaces and adaptive hand pose recognition techniques for gestural control, aiming to create more natural and intuitive interactions for musicians in XR environments.

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