AES Europe 2026

This paper introduces a novel approach for generating a
lower layer in multichannel audio upmixing, addressing a
gap in existing methods that primarily focus on mid; top
layers. Leveraging Harmonic-Percussive Separation (HPS),
the proposed framework dynamically adjusts key parameters
(separation factor, harmonic attenuation,; phase shift)
to enhance percussive components while diffusing harmonic
elements. We compared three neural network architectures
for this task: LSTM, TCN,; Transformer. Experimental
results show comparable perceptual quality; objective
metrics across all models, with the TCN being the most
balanced; suitable for deployment on edge devices.

The mixing stage in music production involves a complex set
of interdependent technical; creative decisions aimed at
achieving a coherent; industry-level result. Intelligent
Music Production (IMP) is an emerging research area that
integrates Artificial Intelligence techniques into music
creation; post-production processes, spanning from
composition to mastering. Within this context, Answer Set
Programming (ASP), a declarative paradigm from Knowledge
Representation; Reasoning, has proven effective for
modeling; solving complex optimization problems. This
article presents frmixerr, an ASP-based intelligent system
designed to optimize the mixing process by automatically
generating balanced mixes. The system formulates mixing as
a combinatorial optimization problem; evaluates
candidate solutions against a reference spectral profile.
To assess its performance, a subjective listening test was
conducted comparing mixes generated by frmixerr with mixes
produced by human engineers with varying levels of
professional experience. The results indicate no
significant differences in perceived quality between
frmixerr mix; those created by professionals, suggesting
that ASP constitutes a viable approach for intelligent
assistance in music mixing.

The development of personal sound zone systems in recent
years show great potential for low-frequency noise control
outside of noisy spaces. These approaches show promising
applications to manage noise pollution arising from
concerts in large venues or urban festivals. However, most
of the literature considered that the created sound zones
would exist in the same room or acoustic space as the noise
source. This premise hence discards all setups where the
disturbances would occur outside of concert venues (e.g in
neighboring houses). This paper presents a first
experimental study of the behavior of sound zone methods
for indoor sound zones; outdoor noise sources. These
initial results present a good efficiency of these methods
in this edge case, opening new use cases for these
approaches.

Conventional ornithological monitoring systems rely heavily
on single-channel recorders; deep learning classifiers
to identify "what" species is present, but fail to capture
"where" it is located or how individuals interact
spatially. This limitation hinders the study of complex
ecological behaviors, such as inter-specific spacing in
dense vegetation; predator-prey dynamics. We propose a
novel, dual-mode acoustic localization system designed to
unify semantic classification; spatial tracking.
Utilizing an economically scalable 16-channel Uniform
Rectangular Array (UMA-16) interfaced with edge-computing
platforms, we implement a hybrid spatial filtering pipeline
structured to balance real-time latency constraints with
achievable angular resolution. The first stage employs a
computationally efficient, noise-robust linear scanning
technique to generate an acoustic energy map; estimate
source multiplicity. This preliminary data initializes a
second-stage, super-resolution spectral estimation
algorithm predicated on signal-noise subspace
orthogonality, allowing the noise robustness of
non-parametric beamforming methods with the precision of
parametric approaches. By integrating these spatial filters
with standard deep learning classifiers, the system
resolves overlapping vocalizations in "Cocktail Party"
scenarios; improves Signal-to-Noise Ratio (SNR) for
cryptic species detection. We address the physical
"Localization-Detection Range Disparity," demonstrating
that while detection is viable at long ranges, precise
localization is constrained by the array aperture to the
near-to-mid field. The system outputs real-time video
overlays of acoustic heatmaps for field observation;
generates autonomous volumetric territory maps in fixed
deployments, collectively providing ornithologists with a
robust capability for analyzing the spatial ecology of
avian vocalizations.

Input-output linearization is a technique for compensating
nonlinear distortion in loudspeakers. To apply it to
complex loudspeaker models, we describe an end-to-end
framework for estimating model parameters from data;
deriving the linearizing control laws using automatic
differentiation. The parameter estimation approach combines
frequency-domain linear parameter estimation with a
time-domain prediction-error method for the nonlinear
parameters. The linearization approach supports non-linear
reference systems; stabilization of the control law
using trajectory tracking. We implement the framework in
dynax, an open-source Python package based on JAX,;
validate it experimentally as a feed-forward controller on
a closed-box loudspeaker. Results demonstrate validation
errors of 1--5\,\% NRMSE; total harmonic distortion
reductions of 6--12\,dB. The framework enables researchers
; engineers to rapidly prototype; validate complex
loudspeaker models for distortion compensation without
manual symbolic derivations.

A low-parameter-count machine-learning model for
classifying streaming video can enable content-aware
audio/video processing on consumer edge devices with
latency, computational,; battery constraints. In this
paper, we propose a low-compute classification technique
that uses only text metadata from the streaming file
header, enabling near-instantaneous inference without
decoding; analyzing audio or video signals as is
traditionally done. In particular, to support multilingual
platforms such as YouTube, we first apply neural machine
translation as a pre-processing step for the text metadata
; optimize a lightweight neural classifier for a
three-class audio-centric classification taxonomy (movie,
music, dialog/other). Experiments on a mixed-language
YouTube dataset achieve $\approx$90\% classification
accuracy on a test set using a combined translation; a
classification model (with only $\sim22K$ parameters),
demonstrating a globally-scalable approach for robust
classification on the edge.

Deep learning has significantly improved speech enhancement
performance in controlled laboratory conditions, yet these
advances rarely translate into robust real-world benefit
for hearing aid users. Current algorithms are trained;
evaluated in simplified acoustic scenarios, neglecting
multimodal cues, user interaction, environmental dynamics,
; the strict latency; power constraints of embedded
devices. As a result, a persistent gap remains between
algorithmic performance; everyday listening experience.
This position paper reviews recent progress in speech
enhancement, embedded Artificial Intelligence hardware,;
hearing aid systems,; argues for a shift toward
ecologically valid evaluation; hardware-aware design. We
propose virtual reality as a reproducible, multisensory
benchmarking platform enabling joint assessment of human
perception; algorithmic processing. This perspective
outlines a research roadmap toward adaptive, context-aware,
; practically deployable hearing technologies.

This work presents a perceptual model based on a complex
IIR filterbank. The filterbank with a frequency resolution
of 4 bands per Bark consists of 104 filters whose slopes
are designed to take spectral masking effects into account.
The filter outputs are used to obtain masking thresholds
with the following post processing. To obtain resonable
masking thresholds from the spreading outputs, a post
masking stage is required. Here, we propose a comodulation
dependent adaptation of the postmasking decay to model
Comodulation Masking Release (CMR) effects. This approach
explicitely considers the dip-listening effect known from
literature. The final masking thresholds are obtained by
weighting the postmasking outputs by a tonality dependent
gain, controlled using spectral flatness estimation. A
listening test compares the proposed method to an already
known approach using direct CMR based modification of the
masking threshold gains.

Sound source localization; identity tracking are
fundamental tasks in acoustic scene analysis, enabling
machines to determine what, where; when produces sound
events. While deep attractor-based networks have
demonstrated improved performance under an unknown number
of sources, maintaining continuous source tracking over
long-form audio remains challenging due to memory
limitations; permutation ambiguities across adjacent
segments. In this paper, we propose a Recursive Attractor
Network (RANet) for long-form sound source localization;
identity tracking with a variable number of sources. RANet
explicitly represents source attractors as transferable
embeddings; recursively propagates them across adjacent
audio segments using a LSTM-based model, thereby preserving
source identity continuity over time. Experimental results
on simulated datasets demonstrate that RANet achieves
robust long-form sound source localization; consistent
source identity tracking, outperforming baseline approaches
under variable; dynamic source conditions.

Identifying robust headphone target curves is challenging
when preference data from untrained listeners are
interpreted without explicit perceptual structure. This
work presents a methodological framework in which deep-
learning-driven sensory-profile analysis serves as the
primary interpretive layer for listening data.
Candidate target curves are generated using an Interactive
Differential Evolution (IDE) listening experiment that
combines paired comparisons with a second- stage
absolute-rating task, enabling continuous exploration of the
perceptually relevant tuning space while reducing cognitive
load. Converged gain sets are analyzed using a Virtual
Listener Panel (VLP), a Deep Learning (DL) model trained on
large-scale expert evaluations to predict perceptual
attributes from rendered musical material. Predicted
attributes are reported as relative scores along key sensory
dimensions, including bass strength, timbral balance,;
brilliance, enabling exploration of sensory clusters,
perceptual trade-offs,; potential families of target
tunings.
Adaptive listening data from three culturally distinct
listener panels (Denmark, Japan,; Colombia; 20
participants
per site) support the DL-based interpretation. Convergence
is quantified as a reduction in population variance,
; cross-site analyses assess the similarity of clustering
structures; the consistency of relationships between
preference; sensory attributes. Overall, the framework
provides a scalable, perceptually grounded approach to
interpreting listener-preference data when developing
headphone target curves.

Sa quintina is a distinctive emergent vocal phenomenon
almost exclusively associated with the sacred polyphonic
singing tradition of Castelsardo, perceived as an
autonomous “fifth voice” arising during collective
performance by four male singers. Although widely
acknowledged in ethnomusicological literature, its
formation mechanisms remain only partially explored within
audio engineering; acoustical research.
This paper presents an early-stage, descriptive sonological
case study proposing new hypotheses on the formation;
spatial reinforcement of sa quintina. The phenomenon is
interpreted as a physically grounded, measurable outcome of
harmonic fusion; spatial interference, observable
through spectral energy distribution; coherence. It is
hypothesized to emerge from a converging set of
conditions—including non-tempered harmonic textures,
differentiated vocal emission techniques, intentional
formant tuning,; circular spatial configuration—none of
which is assumed to be strictly sufficient in isolation.
Building upon previous spectral coherence analyses, the
study introduces a Quintina Directionality Index (QDI) to
quantify the spatial dimension of the phenomenon. QDI is
defined as the ratio between spectral energy in two
frequency bands associated with sa quintina (600–750 Hz;
1200–1400 Hz); total spectral energy. The index is
evaluated as a function of direction using ambisonic
recordings in an anechoic chamber; as a function of
microphone position in a controlled field setting.
Preliminary observations suggest that sa quintina
corresponds to localized regions of enhanced spectral
coherence; energy reinforcement, supporting its
interpretation as an emergent physical phenomenon that
precedes; enables its perceptual salience, rather than a
purely auditory illusion.

This paper presents a method for extracting a center signal
from two-channel stereo signals for upmixing;
reproduction with additional center loudspeakers.
It uses a generative adversarial network with a generator
trained with multiple reconstruction losses; adversarial
losses obtained from a discriminator.
The processing is of low computationally complexity, causal
; can be configured for latencies down to one audio frame
of 46 ms length.
It is described how training data are created using only
publicly available signals; how the generation of target
data enables to control the attenuation of diffuse signals
; direct signals panned off-center.
An evaluation with listening test; computational metrics
SI-SDR; F2 measure is presented.
It shows an advantage compared to methods based on
classical signal processing in terms of computational
metrics for source separation; listeners preference.

This paper presents Part 2 of our study on personalized
timbre optimization for stereophonic sound reproduction via
earphones, following our previous work presented at the AES
International Conference on Headphone Technology in 2025.
While Part 1 established a novel auditory-model-based
framework for reproducing a listener’s natural timbre
reference; demonstrated its perceptual validity under
controlled conditions, the present study focuses on the
practical implementation; validation of this approach
for real-world use with consumer True Wireless Stereo (TWS)
earphones.

Conventional headphone; earphone personalization
techniques primarily target spatial audio reproduction or
rely on preference-based equalization, often overlooking
the accurate reproduction of natural timbre in stereophonic
content. Our approach explicitly addresses this limitation
by isolating; optimizing perceptually relevant timbral
cues while excluding spatial encoding components, thereby
improving timbral fidelity without degrading stereo imaging.

The proposed method originally consists of four stages:
high-resolution anatomical scanning of the listener’s upper
body, including the pinnae, individualized HRTF computation
using the boundary element method, selective removal of
spatial encoding components to derive a personalized
reference target response curve (PR-TRC),; perceptual
optimization using a listener-specific weighting
coefficient grounded in auditory reference fidelity rather
than preference. In this paper, each stage is simplified
; automated using smartphone-based scanning;
AI-assisted processing, enabling end users to complete the
entire personalization process via a smartphone connected
to a cloud-based server. The resulting personalized target
response curve is implemented within the computational;
memory constraints of the DSP pipeline of commercial
consumer TWS earphones.

A subjective evaluation using the Semantic Differential
Method was conducted to assess the perceptual impact of the
simplified implementation. Twenty-four listeners evaluated
personalized target curves generated by both the original
; simplified methods, as well as two non-personalized
target curves commonly used in commercial TWS earphones.
The results show that both personalized methods
consistently outperform non-personalized conditions in
overall sound quality; listener preference. Importantly,
no statistically significant degradation in perceived
timbral naturalness was observed between the simplified;
original methods.

These findings demonstrate that auditory-model-based
personalized timbre optimization can be effectively
translated into a practical, consumer-ready technology. The
proposed approach represents a foundational contribution to
future audio personalization; has broad applicability
across headphone; earphone systems for stereophonic
sound reproduction.

While Neural Audio Codecs (NAC) have revolutionized
monaural audio compression, achieving high-fidelity
dual-channel coding at low bitrates remains a significant
challenge. Existing approaches often rely on naive
independent channel quantization, leading to phase
incoherence, or entangled latent modeling, which sacrifices
spatial precision for spectral energy. This paper proposes
a novel dual-channel coding framework based on
contentspatial disentanglement. Reframing spatial
reconstruction as an informed source separation task, our
architecture synergizes a frozen, pre-trained DAC encoder
for robust mono content preservation with a
parameter-efficient side information encoder that predicts
fine-grained time-frequency masks. To ensure precise
spatial imaging, we introduce explicit physical constraints
into the end-to-end training. Experimental results indicate
that at low bitrates of 9; 11 kbps, the proposed method
outperforms state-of-the-art dual-mono neural baselines;
industry standards in both objective spatial metrics;
subjective MUSHRA evaluations.

Audio synchronization across heterogeneous media playback
devices is essential for delivering immersive sound
experiences in applications such as speaker group play;
multi-room audio playback. Existing synchronization
techniques predominantly rely on tightly coupled network
infrastructures; often embed a media sequence;
timestamp information to the media packet at the
transmitting source end, which restrict flexibility of
selecting the transmitting source; also compromises
robustness under dynamic network conditions. This paper
proposes a network; source independent audio
synchronization framework that eliminates dependency on
embedding media sequence; timestamps. The proposed
system employs an audio fingerprinting-based media
sequencing algorithm amongst the media playback devices
without relying on the type of transmitting source; the
network availability. A novel audio synchronization
algorithm is proposed which first determines a common
sequence start information given a dynamic media stream
from the transmitting source; then communicates the
fingerprint; timestamp amongst the media playback
devices without modifying the original audio packet
structure. Experimental results demonstrate that the
proposed approach achieves a high audio-audio
synchronization of less than 10ms across media playback
devices in a no network environment, thereby extending the
scope of immersive audio application irrespective of the
transmitting source.

We present a spectral-like reformulation of 2D ambisonics,
enabling an alternative representation of the sound field
in terms of amplitudes; phases. We hypothesise that it
simplifies the representation; creative manipulation of
2D ambisonics, beyond encoded directional point sources.

In 2D high-order ambisonics (HOA) of order N, a sound field
can be represented as a 2π-periodic angular function as a
combination of circular harmonics (Y_m) weighted by the
coefficients (a_m) with m ∈ [-N, N]. This representation
can be reformulated in terms of N+1 amplitudes; N
phases, similarly to a Fourier decomposition.

A simple example of this representation is the ambisonic
encoder at an angle theta. Phases are then multiples of a
phase phi = theta/2π, as frequencies are multiples of a
fundamental in harmonic sounds. Therefore, the
amplitude-phase approach can draw on the field of sound
synthesis, between harmonic; inharmonic modelling.
Operations on ambisonic vectors in amplitude-phase also
rely on Fourier representation, namely the spectral
convolution of two vectors (element-wise products of the
amplitudes, element-wise sums of the phases). Spectral
convolution has vast potential in ambisonics, allowing to
represent all the usual spatial operations (geometric;
transformative) in a simple manner.

To test this approach, we are currently developing an
ambisonic synthesiser based on Faust functions running in
Max environment. We are evaluating the scope of this
representation, both theoretical; compositional,;
then attempt to expand this approach to 3D ambisonics.

Accurate identification of audio coding artifacts is
instrumental in encoder design, audio post-processing,;
perceptual quality assessment. This paper addresses the
detection of artifacts arising from changes in the
effective bandwidth of coded audio signals caused by coarse
spectral quantization. Such bandwidth variations give rise
to two prominent artifact types: bandwidth limitation (BL)
; birdies, also referred to as spectral islands (SI).
Blind detection methods, requiring no reference signal, are
presented for both artifact types. Bandwidth limitation
is detected by analyzing variations in the zero-crossing
count across time-domain subband signals, enabling
estimation of both fixed; time-varying cutoff
frequencies. Spectral islands are identified through
analysis of the spectrogram by detecting clusters of
isolated components in the time–frequency domain,
characterized by their temporal; spectral extents. The
proposed methods are evaluated using audio material from
the ODAQ; USAC verification datasets. Results show that
the BL detection method achieves an average bandwidth
estimation error of approximately 160 Hz; demonstrates
robustness to noisy bandwidth-limited signals. In addition,
the detected birdie artifacts are perceptually validated
through listening tests, indicating an improvement in
perceived quality following detection; subsequent
suppression of the birdie artifacts.

The acoustic characterisation of indoor spaces is crucial
for a wide range of applications. While global metrics
provide convenient descriptors of a room's overall
behaviour, a more spatially detailed analysis offers deeper
insight into the spatio-temporal structure of the sound
field, albeit at a higher experimental cost. This paper
proposes a methodology that leverages the predictive
capabilities of sound field reconstruction methods to
estimate room acoustic parameters as a function of
position. The approach is experimentally evaluated in an
auditorium, where it achieves accurate estimation of
temporal; energetic room acoustic parameters across the
entire audience area. In addition, the reconstructed field
yields higher intelligibility indices compared to the raw
measurements. Overall, these results highlight the
potential of sound field reconstruction techniques as a
practical tool for room acoustic characterisation; for
supporting assistive listening technologies.

MPEG-4 SLS (scalable lossless coding) was published more
than 20 years ago. In the meantime several tools to improve
coding efficiency; flexibilities have been invented.
Currently, in MPEG WG6 (audio coding) there are two
standardization activities on lossless audio coding: Audio
Coding for Machines (ACoM); Biomedical; general
waveform signal coding (BWC).
ACoM phase 1 originally was targeted only towards lossless
storage formats for training of machine listening schemes,
but additional uses cases like “user generated content
analysis”, “live stream content analysis”,; “artistic
creation” have been added. The focus was extended to the
transmission of audio data from microphone (arrays) to
central processing units.
BWC is a joint activity with TU-R SG21. While ACoM started
with a large number of use cases; includes the
specification of a rich set of metadata BWC started with a
focus on medical data like electroencephalogram (EEG);
electrocardiogram (ECG). However, BWC can be used for audio
signals, too; medical data coding are on the list of use
cases for ACoM.
The call for proposals (CfP) for ACoM was completed in
January 2025. Two proposals, both outperforming MPEG-4 SLS,
had been submitted. Both proposals reused; optimized
core codecs from BWC. Currently, MPEG audio investigates
how the ACoM proposals can be merged into BWC. This merge
process must be completed end of April 2026.
The presentation will give details about ACoM use cases,
the ACoM CfP process, the results of the CfP; results
from the merge process.

Mixed-phase impulse response equalization can improve
magnitude; phase response, but conventional objectives
such as mean-squared error (MSE) can favor solutions that
introduce objectionable temporal artifacts, including
pre-echo; extended post-echo ringing. This paper
proposes a Spatial Equalization Quality Measure (SEQM) to
select a mixed-phase equalization filter that better
controls these artifacts while remaining computationally
simple; applicable across multiple listening positions.
SEQM combines (i) a temporal-domain metric that penalizes
energy preceding the main pulse of an impulse response;
energy persisting after it, while also accounting for the
decay rate of the post-response tail, with (ii) a spatial
aggregation rule that summarizes quality across measurement
positions. We use SEQM to select the modeling delay for
mixed-phase finite-impulse-response (FIR) equalization;
to compare mixed-phase FIR designs with minimum-phase FIR
; IIR alternatives under a common multi-position
measurement framework. Experiments using semi-anechoic
measurements across 34 spatial positions for two
loudspeakers show that SEQM consistently selects
substantially shorter delays than MSE-based selection;
yields impulse responses with reduced pre-echo; faster
post-response decay, while maintaining comparable
frequency-response equalization. These results suggest that
SEQM is a practical objective tool for designing
multi-position mixed-phase equalization filters.

High-speed 1-bit signals generated by oversampling are
widely used in audio applications as they allow simple
demodulation via low-pass filtering while preserving
in-band spectral characteristics with high accuracy.
However, conventional FIR filtering of such signals
generally requires conversion to a multi-bit representation
at a common sampling frequency, which increases
computational cost; complicates the overall processing
flow. This paper addresses the convolution of high-speed
1-bit audio signals with multi-bit FIR impulse responses
; presents a systematic formulation of a multiplier-less
convolution approach. Based on a mathematical
reinterpretation of convolution, the proposed formulation
describes how time shifting; amplitude weighting can be
expressed through structured rearranging of 1-bit samples
without arithmetic operations. This provides a theoretical
description of previously reported 1-bit convolution
methods; however, its validity has not been fully
formalized. We examine the spectral characteristics of the
proposed convolution method; compare them with those
obtained by multi-bit convolution followed by ΔΣ
modulation. Experiments are conducted by convolving 1-bit
input signals with FIR filters having multi-band frequency
responses. Spectral analysis shows that the proposed method
achieves extremely high agreement with the standard
approach within the audible band while the differences
appear primarily at much higher frequencies outside the
audible range. These results demonstrate that convolution
of high-speed 1-bit audio signals can be achieved without
multipliers, suggesting the potential for highly efficient
hardware-oriented signal processing architectures.

This paper presents a multichannel adaptive filtering
algorithm for real-time full-band adaptive transaural
reproduction on general-purpose hardware. It is based on a
multichannel frequency-domain FxLMS algorithm using an
overlap-save framework for both filtering; adaptation,
; is extended with (i) online plant identification for
fully adaptive operation, (ii) frequency-dependent
normalization for faster convergence,; (iii)
frequency-dependent regularization to stabilize adaptation.
The proposed algorithm is implemented in C language on a
standard desktop PC; evaluated on a 4x2 transaural
configuration running in real time at 48 kHz with 2048-tap
control filters. Two evaluation tests are conducted. The
first test consists of reproducing two uncorrelated
white-noise signals at the ears of a manikin using
crosstalk cancellation as the performance metric. An
average crosstalk cancellation of 32 dB over 100 Hz–20 kHz
is demonstrated. The second experiment considers binaural
signal reproduction as a more realistic use case of the
algorithm. In both cases, performance is assessed for both
a static listener; a moving listener scenario,
demonstrating the algorithm’s ability to rapidly re-adapt.

Personal sound zones aim to reproduce distinct audio
contents in separate spatial regions using loudspeaker
arrays, while minimizing acoustic interference between
zones. Although well established theoretically, their
real-time implementation remains challenging due to the
long impulse responses involved; the latency constraints
of audio processing systems.
This work presents a real-time implementation of personal
sound zones based on the pressure matching method in a
static context, i.e. transfer functions between the
loudspeakers; the zones are assumed to remain constant.
Sound zone filters are computed in the frequency domain
from experimentally measured impulse responses between an
array of 18 loudspeakers; two microphone arrays of 9
microphones defining a bright zone; a dark zone. The
system performance is then evaluated in terms of acoustic
contrast, reproduction error,; effective frequency
range. To meet real-time constraints, a fast partitioned
convolution algorithm has been used, namely the
Uniformly-Partitioned Overlap Save (UPOLS). This methods
has been implemented in C++ as an external block for the
Purr Data real-time audio environment. Experimental
results, obtained in a semi-anechoic environment,
demonstrate that it enables stable real-time multichannel
convolution with negligible numerical error compared to
offline convolution. The proposed system results in a
functional real-time sound zones demonstrator, suitable for
experimental; interactive spatial audio applications.
The codes are shared in a GitHub repository so that the
scientific community can benefit from them.

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.

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.

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.