AES Europe 2026

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.