AES Europe 2026

We present Binaspect, an open-source Python library for
binaural audio analysis, visualization,; feature
generation. Binaspect generates interpretable “azimuth
maps” by calculating modified interaural time; level
difference spectrograms,; clustering those
time-frequency (TF) bins into stable time-azimuth histogram
representations. This allows multiple active sources to
appear as distinct azimuthal clusters, while degradations
manifest as broadened, diffused, or shifted distributions.
Crucially, Binaspect operates blindly on audio, requiring
no prior knowledge of head models. These visualizations
enable researchers; engineers to observe how binaural
cues are degraded by codec; renderer design choices,
among other downstream processes. We demonstrate the tool
on bitrate ladders, ambisonic rendering,; VBAP source
positioning, where degradations are clearly revealed. In
addition to their diagnostic value, the proposed
representations can be exported as structured features
suitable for training machine learning models in quality
prediction, spatial audio classification,; other
binaural tasks. Binaspect is released under an open-source
license with full reproducibility scripts at: (link removed
for blind review)

The recently finalized ISO international standard (IS) on
MPEG-I immersive audio enables interactive
six-degrees-of-freedom (6DoF) audio rendering for a
multitude of virtual-reality; augmented-reality (VR/AR)
acoustic scenarios; applications with comprehensive
modeling of room acoustics; intricate acoustic
phenomena, including e.g. occlusion, reflection,
transmission; diffraction caused by sound obstacles,
Doppler effect,; dynamic environment changes triggered
by user interactivity. This paper describes concept,
methodology; results of the final verification test of
this standard. In the verification test, the perceptual
quality of the renderer was assessed in an interactive
listening test using different in-; outdoor acoustic
scenes, testing the above-mentioned features of the
standard. More than 50 listeners participated in the test
distributed across six labs using the ITU‑R BS.2132 [1]
multi‑stimulus method on a 100‑point scale for three
conditions (IS, mid-; low anchor) in 10 VR scenes plus
two repetitions. The results of several anchor processing
configurations are presented. The selected mid; low
anchors have demonstrated stable quality across diverse
scenes with progressive timbre; spatial degradations.
The listening test results show a clear separation of the
conditions (IS > mid > low); the low anchor was stable
(around 16 points median value) while the mid anchor varied
by scene (around 47 points). The IS is rated with a median
of 84 points among all labs, which is the “excellent”
region of the scale. The individual scenes are rated
differently. The quartile range for some scenes can exhibit
20 points. The median value for the IS of the different
labs varied, some are a bit more critical than others.

This paper presents a comparative analysis of two immersive
recording techniques for classical music: the PCMA-3D
(Perspective Control Microphone Array); the Decca
Cuboid. While the Decca Cuboid relies primarily on
time-of-arrival differences to generate spatial
impressions, the PCMA-3D utilises intensity differences;
separates ambience from direct sound. A recording session
was conducted in a concert hall using a classical guitar
soloist; two distinct folk music ensembles to capture
performances simultaneously with both arrays. Subjective
evaluation was performed using a MUSHRA listening test with
18 participants, assessing parameters such as sensation of
space, localisation precision,; sound quality.
Statistical analysis reveals that while both systems
provide high-quality immersive experiences, the PCMA-3D
scored significantly higher in the sensation of space (p