AES Europe 2026

This tutorial lecture is intended to present the basic
concepts of Physical Modelling (PM) sound synthesis. The
motivation behind the presentation is to increase the
awareness of the audio community of the existence of this
synthesis method, as an alternative to the much more
widespread subtractive synthesis approach.

Physical modelling synthesis allows for the creation of
realistic acoustic instrument timbres, as well as
avant-garde sound design constructs, in a way that is much
more approachable and intuitive than FM synthesis.

The lecture is intended for the beginner adept of sound
design, as well as the general audio public, so no prior
knowledge is required beyond a basic, intuitive
understanding of sound. Auditory examples will be presented
using a software modular synthesiser environment, as well
as dedicated physical modelling virtual instruments/effects
plugins within a Digital Audio Workstation application.

The presented concepts are applicable to the field of sound
design for music, film, gaming, and audiovisual arts, as
well as to teaching acoustics and musical instrument
studies in the classroom.

Topics covered:
-PM synthesis premise
-Comparison to subtractive synthesis
-Concept of a resonant model (Karplus–Strong oscillator)
-Simple and complex resonators, resonator coupling,
exciters, modifiers, pickups, and instrument body resonance
-Typical PM synthesiser architecture and workflow
-Signal processors/effects utilising physical modelling
(e.g. modal reverb)
-Dedicated virtual instruments for realistic acoustic and
electroacoustic instrument synthesis

All sound and workflow examples will be presented in a
technology-neutral way (using a variety of physical
modelling plugins from different vendors).

Dialogue intelligibility is a fundamental aspect of audio
post-production. Ensuring speech clarity in complex sound
mixes remains challenging across different playback
systems. Selective auditory attention plays a central role
in how listeners track dialogue in busy mixes, so small
changes in spectral or spatial structure can influence
perceived clarity in unexpected ways. This study
investigates the effectiveness of psychoacoustically
informed techniques, equalisation; spatialisation, in
reducing auditory masking; improving the clarity of
dialogue. The listening test was completed on participants’
own playback systems, which reflects typical domestic
viewing conditions; aligns the study with real-world
listening environments. The techniques were tested
individually; in combination to assess their impact.
Results show that equalisation was more effective than
spatialisation in reducing masking, while their combination
produced a significant improvement in intelligibility,
clarity,; reduced interference. The effectiveness of
these methods varied between the two groups of clips,
suggesting that their application should be adapted to the
specific acoustic context of each scene.

Sound plays a critical role in virtual reality (VR),
shaping attention, narrative comprehension, emotional
engagement,; experiential plausibility under conditions
of embodiment; user agency. Although a growing body of
research addresses VR audio techniques, perceptual effects,
; sound taxonomies, existing approaches remain fragmented
; largely descriptive. In particular, they do not provide
a unifying, VR-specific account of how sound meaning;
emotional intent are operationally linked to user agency
; non-linear narrative progression. This paper presents a
narrative review of selected literature spanning game audio
frameworks, immersive sound design, narrative theory,;
plausibility-related research in games; VR. Through
synthesis of these perspectives, the review identifies a
conceptual gap in current research, namely the absence of a
VR-specific, agency-coupled sound design framework for
structuring sound meaning; emotional intent in support
of experiential plausibility as users actively shape events
in interactive VR environments.

This paper presents a systematic literature review on
inverse synthesis; sound matching, which focus on
predicting synthesizer parameters to recreate a target
audio waveform. Automating this process using machine
learning is impeded by distinct technical challenges: many
to one mappings where different parameter settings produce
the exact same sound, the non-differentiability of
commercial black box synthesizers, a scarcity of musically
structured training data,; a lack of standardized
perceptual metrics. Existing approaches are categorized
into non-differentiable synthesizer methods, utilizing
evolutionary algorithms; deep learning, incorporating
techniques to bypass gradient limitations such as neural
proxies or generative models. In contrast, differentiable
synthesizer methods, enable the integration of audio loss
functions into training pipelines via custom signal
processing environments. The analysis identifies a critical
reliance on spectral representations for evaluating
perceptual similarity, given that parameter based metrics
frequently fail to align with human hearing. The findings
indicate that while deep learning has reduced inference
times, the field lacks a unified production solution.
Future progress requires the establishment of standardized
benchmarks to evaluate models, the implementation of novel
advancements in generative models not yet applied to this
problem,; the development of hybrid architectures to
simultaneously address these distinct technical challenges.