AES Europe 2026

Digital Audio Signal Processing has long enabled precise
analysis of musical instrument behavior, supporting digital
sound synthesis. In parallel, physical modeling has evolved
into a mature synthesis; simulation technology capable
of running in real time, coupling vibro-acoustic models
with perceptual control interfaces. Over the last decade,
advances in machine learning have begun to transform both
ends of this pipeline. Instead of relying solely on
analytical DSP methods, we are increasingly able to learn
impulse; frequency responses, infer parameters,;
drive synthesis models directly from data. This broader
transition from classical DSP to *AI Audio Engineering*
brings not only new algorithms but also new workflows,
evaluation practices,; deployment contexts for musical
acoustics.

Two demonstrators illustrate this shift. *First*,
measurement-driven studies of musical instruments can
constrain model architectures; reduce parameter search
spaces. The measurement-derived priors can inform both
classical modeling; data-driven neural surrogates.
*Second*, real-time physical modeling integrated into XR
environments highlights how haptic control, perceptual
feedback,; spatial audio can create convincing virtual
instruments suitable for experimentation, pedagogy,;
performance.

These demonstrators motivate an AI Audio Engineering
workflow in which measurement, modeling, learning,;
perceptual evaluation form a continuous loop, to enable
immersive XR experiences, rapid prototyping of novel
instruments,; new modes of digital lutherie. The
approach invites collaboration across acoustics, DSP,
spatial audio,; AI Audio Engineering: an emerging
discipline that considers audio models as deployable,
maintainable,; continuously improvable artifacts
governed by data, inference, evaluation,; lifecycle
operations.