Bartłomiej Mróz

This paper presents a neural network for binaural rendering of first-order Ambisonics (FOA) signals, enabling immersive spatial audio over headphones without individualized HRTF measurements at inference time. The model operates in the STFT domain using Complex Ratio Masks (CRM): a shared mask pair (left/right ear) is applied via complex-valued multiplication to all four FOA channels with directional weighting, preserving both magnitude and phase. The input representation extends standard spectral features with acoustic intensity vector channels encoding sound arrival direction at each time-frequency bin. Training uses a multi-objective loss combining SI-SDR, multi-resolution spectral reconstruction, and interaural level/phase difference terms. The four-level UNet backbone with channel-spatial attention totals approximately four million parameters. Evaluation against eight baseline configurations spanning magnitude-masking variants (28M parameters), a waveform-domain Wave-U-Net (143 M), and the A2B time-domain renderer shows that the CRM variant achieves the strongest spectral and spatial fidelity in the cross-domain setting with a seven-fold parameter reduction over the magnitude-masking baseline while maintaining real-time CPU operation.

The Zylia ZM-1 (approximately 103 mm spherical array, 19 MEMS capsules, 3rd-order) and Harpex Spcmic (170 mm planar array, 84 MEMS capsules, 5th-order capable) represent two distinct geometrical approaches to higher-order Ambisonics capture. Despite widespread adoption, systematic comparison of their performance under real recording conditions is absent from published literature. We present a controlled case study based on simultaneous recordings of solo piano repertoire in a 370-seat concert hall (Aula Politechniki Gdańskiej, RT60 = 1.97 s), with both arrays mounted 17 cm apart on a single stereo bar. After peak-normalization to -0.5 dBTP, four metrics were compared: W-channel spectral response, integrated loudness (ITU-R BS.1770-5), per-order spatial energy distribution, and first-order directional ratios. The headline finding concerns spatial energy: at nominally identical 3rd-order, the ZM-1 attenuates 27.4 dB from 0th to 3rd order, while the Spcmic attenuates only 8.4 dB – a 19 dB gap that is not driven by aliasing-band content and persists across repertoire. The results indicate that nominal Ambisonics order alone does not predict per-order energy distribution in real recordings; geometry and encoding contribute substantially even when the format label is identical. Recordings are openly available under CC BY-NC-SA 4.0.

Spatial audio recording using higher-order Ambisonics offers rich directional information for medical speech capture, yet challenging hospital acoustic environments motivate preprocessing with neural denoising algorithms. This study investigates whether U-Net-based denoising of third-order ambisonic recordings improves automatic speech recognition (ASR) quality for medical applications. We developed the Medical Immersive Audio Corpus (MIAC), comprising 1,759 utterances (6.43 hours) of Polish medical speech recorded with a Zylia ZM-1 microphone in uncontrolled hospital environments, capturing 16-channel third-order Ambisonics across multiple specializations including thyroid ultrasonography, surgical procedures, and general diagnostics. We applied a U-Net architecture with dual attention mechanisms trained using the Noise2Noise paradigm to denoise the corpus, then evaluated transcription quality using ten Whisper ASR models ranging from 39 million to 1.55 billion parameters, including domain-adapted medical variants. Surprisingly, we discovered a noise reduction paradox where denoising degraded transcription quality for seven of ten models, with statistically significant increases in Word Error Rate (WER) and Character Error Rate (CER) for general-purpose base, small, and medium models. Only the domain-adapted whisper-medium-68000-abbr model showed statistically significant improvement (p = 0.0008), while large-scale models (large-v2, large-v3) exhibited robustness with negligible changes. Effect sizes remained small (Cohen’s d < 0.2) across all models. These counterintuitive findings suggest modern ASR systems implicitly utilize background noise characteristics as informative features, and that preprocessing pipelines should be reconsidered for domain-specific applications. Our results provide practical guidance for medical speech processing system design.

This paper presents a novel audio decorrelation method that integrates velvet noise with parametric modeling of vowel filters derived from recorded speech. By capturing vocal timbre, the technique extends decorrelation and artificial reverberation tools, offering new creative possibilities. Velvet noise is filtered to model vowel resonances, enabling users to imprint speech or singing characteristics onto multichannel effects. Inspired by choir acoustics, the system synthesizes distinct vowels per channel, producing speech-like textures that are convolved with input audio. The approach emphasizes immersive, voice inspired sound design, with discussion of implementation challenges, creative applications, listening tests, and directions for future research.

Multiple immersive audio formats compete for adoption in streaming, cinema, and virtual reality, yet systematic objective comparisons across formats remain limited. This study proposes a two-model evaluation framework using BAM-Q (perceptual quality) and BINAQUAL (localization similarity) applied to binaural renderings of ten format variants derived from a seventh-order Ambisonics (7OA) recording of a professional choral production. Formats include first- through third-order Ambisonics encodings (IAMF, Two Big Ears, YouTube/Resonance Audio), channel-based commercial formats (Dolby Atmos, Auro3D, Sony 360 Reality Audio), and variants re-encoded through a 42-point geodesic spherical loudspeaker array (42pIKO) to assess rendering chain interactions. Primary analysis uses two composite perceptual metrics to avoid double-counting binaural cues internal to BAM-Q, with interaural level and time differences reported separately as diagnostic sub-metrics. The correlation between BAM-Q and BINAQUAL is moderate when the 7OA reference is included (r = 0.756) but collapses without it (r = -0.120), indicating that the two models capture independent degradation dimensions among processed formats. BINAQUAL localization similarity scores are severely compressed (0.0439-0.1490) relative to published validation benchmarks, reflecting the cross-format comparison paradigm rather than the within-codec conditions under which the metric was developed. Within-category analysis reveals that metric discrimination varies by format family: localization similarity differentiates Ambisonics variants effectively (CV = 0.68) but poorly among channel/object-based formats (CV = 0.18). These findings identify both the utility and the boundary conditions of current objective models for immersive audio format evaluation, motivating future subjective validation.

The ventriloquist effect, understood as perceptual displacement of sound toward a spatially discrepant visual stimulus, has been extensively studied in traditional laboratory settings, but its manifestation in immersive virtual reality with naturalistic movement remains poorly understood. We investigated audiovisual spatial binding in a six-degrees-of-freedom (6DoF) VR environment using fifth-order Ambisonics spatial audio. Thirty-one participants localized sounds presented with visual cues displaced by 15-70 cm. Results revealed a robust ventriloquist effect: participants’ localization responses were shifted approximately 35% toward the visual cue on average, with substantial individual differences (range: 11-73% visual capture). Sound type did not modulate the effect, but exploratory analyses revealed that participants who moved more during trials achieved better localization accuracy, suggesting that head movement in 6DoF VR serves as a beneficial exploration strategy. We introduce ASCOE (Auditory Spatial & Cognitive Observation Environment), an open-source VR research platform for studying audiovisual perception, and provide complete analysis scripts for reproducibility. These findings extend classic ventriloquist research to ecologically valid immersive contexts and have implications for spatial audio design in VR applications.

This paper investigates spatial energy distribution differences among spherical microphone arrays used for Higher-Order Ambisonics (HOA) recording. A co-located comparison of three arrays, the Zylia ZM-1 (19 MEMS capsules, 3rd-order), Harpex Spcmic (84 capsules, 5th-order), and Saramonic SR-VRMIC (4 electret capsules, 1st-order), reveals that the MEMS-based ZM-1 exhibits 27.4 dB of energy rolloff from 0th to 3rd Ambisonics order, compared to 8.4 dB for the Spcmic at the same order. This finding, obtained from simultaneous recordings of identical musical content from a shared capture position, suggests that nominal Ambisonics order alone does not characterize spatial resolution capability and that array design substantially affects higher-order component energy. The comparison experiment draws on a seven-year HOA recording corpus comprising 24 sessions (April 2019-January 2026), approximately 164 GB and over 17 hours of B-format content captured across diverse acoustic environments (concert halls, churches, outdoor locations) and musical content (solo piano, choir, chamber music, ambient soundscapes). We present the corpus methodology, measured acoustic parameters for the primary recording venue (RT60 = 1.97 s), LUFS-I quality metrics across all 75 rendered files, and spectral and directional analysis of the array comparison. The corpus and its documentation are made openly available to support further research in spatial audio quality assessment, compression algorithms, and immersive audio production.

In this article spatial audio recording techniques are compared: scene-based audio and object-based audio. The study involved mixing recordings from a higher-order ambisonic microphone and support microphones, ambisonically encoded on a virtual sphere. The recordings were combined in different spatial resolution variations by manipulating the ambisonic order. A MUSHRA-like test was conducted, taking into consideration the room divergence effect. The experiment used binaural rendering with headtracking. The results were analyzed using linear mixed models, providing insights into spatial audio recording techniques.