AES Europe 2026

High-fidelity vocal processing is frequently compromised by
sibilance, a phenomenon characterized by stochastic
high-frequency energy that presents unique dynamic range
challenges. While traditional de-essing techniques often
rely on static frequency bands, they fail to account for
inter-speaker variability; changing dynamics. This
project presents an adaptive real-time de-essing
application, developed using the JUCE framework, which
automatically detects; suppresses sibilant frequencies.
The proposed methodology integrates a derivative-based
frequency tracking algorithm to estimate the spectral
centroid without the computational overhead of the Fast
Fourier Transform (FFT). This is coupled with a dual-path
envelope detection system; a relative threshold logic to
distinguish sibilance from the wideband signal.
Additionally, a dynamic harmonic exciter is implemented to
restore high-frequency presence during non-sibilant
periods. Objective spectral analysis confirms the system's
ability to selectively attenuate energy in the 6–11 kHz
range while maintaining spectral transparency;
minimizing artifacts.

Mix preparation, the foundational stage encompassing
technical, musical,; organisational tasks preceding
creative mixing, remains under-examined despite
professional acknowledgement. This study investigated
whether preparatory effectiveness operates through
compensatory relationships, where excellence in one
dimension offsets weakness in another, or through threshold
requirements demanding adequacy across all dimensions
simultaneously.

Nine professional audio practitioners each prepared three
sessions from a pool of nine multitrack recordings spanning
diverse genres. Nine engineers (with partial overlap) then
evaluated the resulting twenty-seven preparations across
five dimensions derived from Phase 1 practitioner
interviews: Session Organisation, Signal Integrity, Musical
Refinement, Processing Boundaries,; Workflow
Facilitation. Professional 'adequacy' was established at a
4.0 threshold based on practitioner consensus regarding
preparations they would 'work with' versus 'send back'.

Results revealed consistent non-compensatory patterns:
exceptional performance in isolated dimensions failed to
compensate for failures elsewhere. One practitioner
achieved perfect Workflow Facilitation (5.00) yet overall
inadequacy (3.43) due to Signal Integrity failure (2.50).
Another achieved strong Musical Refinement (4.75) whilst
Workflow Facilitation collapse (1.75) produced a
below-threshold outcome (3.49). These patterns held across
all inadequate sessions. No track produced exclusively
adequate or inadequate outcomes, confirming source material
did not determine success.

The findings challenge three assumptions: that
practitioners can specialise; compensate, that education
can sequence skills for later integration,; that
intelligent systems can optimise tasks independently.
Preparatory adequacy requires meeting threshold standards
across all dimensions concurrently, with implications for
professional hiring, curriculum design,; AI-assisted
tool development.

The low-frequency performance of cross-talk cancellation
(CTC) systems is fundamentally limited by the condition
number of the plant matrix, which indicates the robustness
of the inverse system in the absence of regularisation.
This condition number, in turn, depends on the relationship
between loudspeaker spacing, listener distance,;
acoustic wavelength.
This paper derives a simple approximate expression for the
low-frequency limit of CTC performance, defined for a given
maximum affordable condition number as a function of these
parameters. The increase in condition number is also shown
to be directly related to the increase in array effort
relative to the minimum achievable array effort. The
formulation is derived for a centered listener; can be
extended to the case of off-center listener positions,
demonstrating the method's applicability to
listener-position-adaptive cross-talk cancellation systems.

Dynamic range controllers, or compressors, have long been
central to music production. The loudness standards now
adopted by major streaming platforms have further
heightened the importance of skilled and intentional
compression in both mixing and mastering. Pop and rock
productions with extremely limited dynamic range are
routinely attenuated during playback, while highly dynamic
material—such as classical orchestral works and film
scores—risks gain and possibly undesirable soft clipping
being applied when delivered masters are normalized.
This panel will address best practices for the use of
compression across a wide range of applications, from
individual instruments to full-program material, in both
stereo and immersive formats. Panelists will present
established methodologies alongside innovative techniques
drawn from their current professional workflows. Different
types of compression will be examined and compared,
including their application in mastering for vinyl release.
Audience engagement is an integral component of the
workshop, and ample time will be reserved for questions and
discussion with conference attendees.

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.

George plays high resolution stereo, 5.1 and 3D recordings
from his fabulous back catalogue, commenting on production
tools and techniques, including his own excellent dynamics
processor.

This masterclass series, featuring remarkable recording
artists, is a chance to hear 3D audio at its best; as we
discuss qualities that make it truly worth the effort.

In each masterclass, we explore the new spatial
possibilities in recording and production, detailing also
this specific listening room, regarding ITU-R BS.1116
compliance and auditory envelopment (AEV) transparency.
Seats are limited to keep playback variation at bay.

Perceptual models are playing an important role in
effectively balancing the data compression; fidelity in
audio encoders by leveraging the masking effects in human
auditory perception. For deriving well suitable masking
thresholds, considering tonality is important. In this
study, a novel filter bank is proposed, which uses narrow
complex-valued all-pole gammatone filters followed by a
non-linear spectral spreading processing. With an
appropriate non-linear mapping before spreading,;
inverse non-linear mapping afterwards, differences between
masking strengths of tonal; noise-like maskers can be
directly obtained without explicit tonality estimation.
By employing a suitable non-linearity, level-dependency of
spectral spreading in the human auditory system can also be
modeled. The performance of the proposed approach is
evaluated through subjective listening tests, which include
comparisons with results obtained using partial spectral
flatness measures.

The application of binaural cue perception mechanisms to
multichannel audio compression technology can reduce
spatial parameter redundancy; effectively lower the
encoding bitrate. Binaural cues play a critical role in
sound source localization,; their frequency-dependent
characteristics yield varied perceptual localization
effects. However, current understanding of the specific
behavior of binaural cues at low frequencies, as well as
the similarities; differences between interaural time
difference (ITD); interaural level difference (ILD),
remains incomplete. To explore the relationship between
ITD-based; ILD-based azimuth perception, this study
non-uniformly selected nine ITD values; twelve ILD
values within the 300–1480 Hz frequency range to test ITD
; ILD perceptual azimuths, respectively. The experimental
method involved using fixed binaural cue stimuli while
varying the audio with known horizontal azimuth angles to
approach the target binaural cue stimulus. Test results
indicate that both ITD; ILD perceptual effects are
significantly influenced by frequency, with the minimum
perceptual azimuth values for both ITD; ILD observed at
700 Hz, suggesting that binaural cue perception azimuths
are closer to the median plane at this frequency.
Furthermore, surface fitting was applied to the perceptual
azimuths of ITD; ILD, revealing relatively similar
patterns. Based on experimental findings, this paper
analyzes the explorable perceptual correlation between
ITD-based; ILD-based azimuth perception. The application
of data in spatial audio coding contributes to the
efficient transmission; fidelity preservation of audio
signals. This study provides valuable insights for
optimizing binaural cue-based compression techniques,
ultimately supporting high-fidelity spatial audio
reproduction.

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.

Accurate; efficient measurement of sound pressure levels
around the ears of occupants in cars is essential for
objective evaluation of basic sound quality; automotive
audio features such as personal sound zones; active
noise control. In this paper, the uncertainties of sound
pressure measurements obtained with 5 commonly used methods
are compared, which are the AES 6-microphone method, the
single-microphone method, the two-microphone method with
occupants presented, the head-and-torso simulator method,
; the human binaural method. Measurements were conducted
in the front-right seat of a 4-door electric Sedan, using
either all car body loudspeakers or a pair of headrest
loudspeakers driven by a two-channel uncorrelated pink
noise to generate an average sound pressure level of 70 dBA
in the seat. Each method underwent 3 complete
install–measure–remove cycles, a total of 54 recordings
were collected,; the standard deviation of the measured
average sound pressure levels was adopted to quantify
measurement uncertainty. The test results show that all the
5 methods have good repeatability; low uncertainty below
200 Hz; above 15 kHz, but have large uncertainty between
200 Hz; 15 kHz. The AES 6-microphone method demonstrates
the best repeatability with the lowest uncertainty across
most frequency resolutions,; its maximum uncertainty in
1/3 octave bands is less than 2.0 dB for sound pressure
measurements in the car. Therefore, the AES 6-microphone
method is recommended for use in engineering comparison;
reporting.

Artificial reverberation is a fundamental process in music
production; audio post-production. However, the large
; highly interdependent parameter spaces of modern
reverberation algorithms make the identification of
perceptually optimal configurations difficult, particularly
when attempting to minimize audible artifacts. This paper
presents a knowledge-driven framework for reverberation
parameter optimization that evaluates candidate
configurations using rule-based audio quality constraints
derived from perceptual; signal-processing principles.
The system automatically detects; prevents common
artifacts including spectral obfuscation, clipping, spatial
collapse,; ringing phenomena. Instead of relying on
data-driven training procedures, the proposed approach
employs declarative reasoning to model audio engineering
knowledge; systematically constrain parameter
exploration. Experimental evaluation demonstrates that the
framework successfully reduces artifact occurrence across
diverse audio material while maintaining computational
feasibility. The results suggest that knowledge-based
reasoning can provide an interpretable; controllable
alternative to data-driven optimization strategies in audio
signal processing.

Linear loudspeaker parameters are often estimated via
fitting of transferfunctions, under the assumption of
linearity. This paper investigates the corruption of the
measurement caused by nonlinearities in the system;
presents a new; improved method for separating the true
linear response from the nonlinear components by analyzing
a sequence of measurements done at different levels. The
method is improved by analyzing the influence of the chosen
measurement levels as well as the measurement time at each
level; presents numerically optimal values for the most
typical cases of nonlinear behaviour. While the influence
of noise; nonlinear distortion can be eliminated
completely in the case of finite orders of nonlinearities
on the system, the method is also shown to provide improved
accuracy in the more realistic case where all orders are
present but only a finite number of them dominate.