Array-Independent Spatial Audio Representations

• Array-independent spatial audio representations are frameworks that decouple spatial encoding from specific hardware geometries using invariant feature transformations and parametric models.
• They employ analytic and deep learning methods such as Ambisonics encoding, cross-attentive fusion, and geometry-agnostic tokenization to capture direction, spatial coherence, and immersive audio cues.
• These approaches enable flexible applications in localization, binaural rendering, and telepresence while addressing challenges like calibration, spatial sampling, and real-time processing.

Array-independent spatial audio representations are mathematical and algorithmic frameworks for spatial audio encoding, processing, and understanding whose feature spaces, intermediate representations, or outputs are invariant to, or robust across, microphone/speaker array geometries and directivities. The principal goal is to achieve spatial audio capture, transformation, and reproduction—such as localization, Ambisonics encoding, binaural rendering, or immersive audio-visual interaction—without coupling algorithms to specific array topologies or requiring per-device retraining, thus supporting interoperable systems, adaptation to arbitrary hardware, and generalizability across real-world scenarios.

1. Core Principles and Definitions

The central challenge in spatial audio is to represent the spatial properties of sound scenes—such as direction of arrival (DOA), spatial coherence, or source locations—independently of the underlying multichannel transducer configuration. Canonical “array-independence” can be defined as the invariance (or controlled equivariance) of the spatial representation under permutations, geometric transforms, or replacements of the array structure, so long as the physical scene remains unchanged.

Key approaches include:

• Invariant Feature Transformation: Embedding high-dimensional raw microphone signals or spatial features into a lower-dimensional, geometry-independent latent space (e.g., through neural or analytical encoders) that aligns with physical space or perceptual cues (Cohen et al., 2023, Heikkinen et al., 30 Jan 2026, Hsu et al., 2023, Wang et al., 2022, Dementyev et al., 28 Jan 2026).
• Steering and Transfer Functions: Utilizing array steering vectors, transfer functions, or array-specific meta-data to factor out geometric dependencies, allowing the model to adapt transfer characteristics to any configuration (Gayer et al., 2024, Hsu et al., 2023, Heikkinen et al., 30 Jan 2026).
• Object-Based Parametric Models: Employing a universal intermediate description—such as Ambisonics, spherical harmonics, or object-based scene graphs—decouples scene representation from particular playback or capture arrays (Jot et al., 2021, Ahrens, 2022, III, 2019).
• Learning-Based Generalization: Architectures and training paradigms explicitly designed to generalize spatial reasoning and perception across unseen geometries, e.g., through learned coordinate embeddings, self-supervised contrastive alignment, or direct modeling of interchannel phase (Dementyev et al., 28 Jan 2026, Wang et al., 2022).

2. Analytic and Deep Learning Approaches

Analytic and model-based array-independent methods leverage the physics of sound propagation and mathematical transforms:

• Ambisonics/Spherical Harmonics: The Ambisonics framework utilizes a spherical harmonic encoding of the sound field, which becomes independent of the microphone arrangement after transforming measurements into the SH domain (Ahrens, 2022, Gayer et al., 2024). For arbitrary arrays, the encoding filters are derived by solving a least-squares projection from microphone responses to SH basis functions, with performance quantified by steering matrix rank and null-space projections (Gayer et al., 2024).
• Wave Field Sampling and Huygens Arrays: Treating microphone (or speaker) arrays as spatial samplers according to Nyquist principles allows array-independent synthesis and analysis once physical sampling constraints are met. Planewave-Based Angle Panning (PBAP) and Huygens Arrays reconstruct continuous sound fields from arbitrary sampling locations (III, 2019).
• Parametric Scene Descriptions: Object-based rational parameterizations (location, orientation, directivity, room parameters) define a universal format for describing spatial scenes, which can be consumed by any renderer or array configuration (Jot et al., 2021).

Deep learning-based systems achieve array-independence by:

• Transfer Function-Based Conditioning: Neural architectures conditioned on measured or simulated array transfer functions—rather than solely on abstract geometry—robustly generalize to frequency-dependent and scattering-dominated configurations (Heikkinen et al., 30 Jan 2026).
• Cross-Attention Fusion: Separate encoders for time-frequency microphone signals and array directivity/response data are merged with attention layers, aligning observed signal content to array physical properties (Heikkinen et al., 30 Jan 2026).
• Geometry-Agnostic Tokenization: Transformers process raw multichannel STFT data, embedding microphone coordinates as learnable positional encodings and computing interchannel phase differences; this decouples spatial reasoning from array specifics and produces “spatial audio tokens” compatible with LLMs (Dementyev et al., 28 Jan 2026).

3. Feature Spaces and Representational Strategies

Array-independent spatial audio representations rely on specialized feature spaces:

• Relative Transfer Functions (RTFs): High-dimensional features computed pairwise across array elements, capturing robust spatial information such as DOA cues and reverberant signatures without explicit TDOA estimation (Cohen et al., 2023, Hsu et al., 2023).
• Spatial Coherence Representations (SCORE): Projecting RTFs onto a set of fixed plane-wave “look zones” yields a feature vector whose dimension and semantics are independent of array size or configuration (Hsu et al., 2023).
• Ambisonics Channels with Residuals: The standard set of SH channels is augmented with data-driven or analytically defined “residual channels” to capture spatial information not supported by the array’s null space, closing the gap between practical array capabilities and ideal SH encoding (Gayer et al., 2024).
• Active Intensity Vectors from FOA: Explicit direction-of-energy flow vectors derived from FOA SH signals encode spatial location in a way agnostic to the original capturing array (Wang et al., 2022).

Table: Array-Independent Feature Techniques

Method	Array Independence Mechanism	Reference
Ambisonics SH encoding	Spherical Harmonic transform	(Ahrens, 2022, Gayer et al., 2024)
FOA Intensity Vectors	First-order energy directionality	(Wang et al., 2022)
SCORE (spatial coherence)	Fixed look-zone projection	(Hsu et al., 2023)
Cross-attentive DNN	ATF-conditioned fusion	(Heikkinen et al., 30 Jan 2026)
Coordinate-patched Transformer	Sinusoidal mic embeddings	(Dementyev et al., 28 Jan 2026)
LOCA (local isometry)	Latent manifold alignment	(Cohen et al., 2023)

4. Applications and Empirical Performance

Array-independent representations have been demonstrated in a range of applications:

• Localization and Mapping: LOCA achieves mean absolute errors as small as 11.3 cm to 18.5 cm in 2D room-mapping across reverberation times, outperforming kernel-based manifold methods and TDOA-based schemes in accuracy and extrapolative generalization (Cohen et al., 2023).
• Ambisonics/Binaural Rendering: Learned ATF- and geometry-aware encoders consistently yield higher SI-SDR and spectral fidelity in complex configurations compared to static or geometry-only baselines (Heikkinen et al., 30 Jan 2026, Gayer et al., 2024). For instance, SI-SDR in a mobile-phone scattering scenario increased by 1.14 dB over static encoders, and adding residual channels reduced binaural error toward the limit set by binaural signal matching (Gayer et al., 2024).
• Audio-Visual Alignment and Perception: Self-supervised contrastive learning combining spatialized audio crops and detected video objects in 360° video substantially improved spatial audio–visual alignment tasks (e.g., +10 percentage points in alignment accuracy for FOA-IV versus log-mel features) and enhanced downstream action recognition and scene classification (Wang et al., 2022).
• Array-Invariant Telepresence: The ACIS-BAT system using the SCORE feature provides scalable binaural telepresence across unseen array geometries, with significant consistency in spatial feature extraction (ERB-SCORE cross-geometry MAC ≈ 0.98), and top performance in both magnitude-weighted IPD and ILD errors (Hsu et al., 2023).
• Multimodal LLM Integration: PhaseCoder enables LLMs to reason over spatial audio content from arbitrary microphone arrangements, with average azimuth errors of 4.3° (RSL dev), 7.4° (LOCATA), and high accuracy with 5+ microphones (Dementyev et al., 28 Jan 2026).

5. Limitations, Constraints, and Interoperability

Despite generalization, array-independent methods face constraints:

• SNR and Array Aperture: For analytic SH encodings, array geometry sets a physical bound on the maximum representable spatial order (e.g., (N_a+1)^2 ≤ M microphones); null-space projections quantify irrecoverable spatial information in a given array (Gayer et al., 2024).
• Local Sampling Assumptions: Some unsupervised manifold learning methods require local burst sampling in array position space and may be challenged by high-reverberation or nonstationarity (Cohen et al., 2023).
• Real-Time and Bandwidth Tradeoffs: Wavefield/sampling methods are limited by spatial Nyquist constraints, while parametric scene models must maintain adequate rendering parameterization across diverse playback systems (III, 2019, Jot et al., 2021).
• Calibration and Anchoring: Embeddings recovering only relative spatial structure (e.g., up to rigid transform) require a small set of physical anchors or multimodal calibration for true absolute localization (Cohen et al., 2023).

Object-based parametric representations uniquely support interoperability, allowing distribution of spatial content as perceptually meaningful object graphs rather than device-specific multichannel audio, enabling seamless rendering on any arbitrary transducer array and consistent audio-visual congruence across applications (Jot et al., 2021).

6. Future Directions and Extension Strategies

Ongoing research pursues several extensions to current array-independent spatial audio frameworks:

• Online/Adaptive Learning: Moving from offline or static array methods to adaptive real-time learning for dynamic environments, moving sources, or variable reverberation (Cohen et al., 2023).
• 3D/6DOF Encodings: Generalizing 2D frameworks (e.g., LOCA) to full 3D spatial representations and supporting listener navigation/freedom (Cohen et al., 2023, Jot et al., 2021).
• Multimodal and Sensor Fusion: Integrating visual, inertial, or other sensory inputs to resolve global rotation/translation ambiguities and enhance spatial scene understanding (Cohen et al., 2023, Wang et al., 2022).
• Enhanced Directivity Modeling: Leveraging measured, frequency-dependent array transfer functions for more faithful reproduction of complex housing/body scattering and practical device-specific directivities (Heikkinen et al., 30 Jan 2026, Gayer et al., 2024).
• Tokenization for Large-Scale Models: Synthesizing array-invariant embeddings or tokens consumable by general multimodal transformers or LLMs, unlocking cross-modal spatial reasoning (Dementyev et al., 28 Jan 2026).

A plausible implication is that as array-independent representations become standard in audio scene encoding, cloud-deployed or edge-executed immersive experiences will remain robust and high-fidelity even as hardware platforms diversify, with future research focusing on scaling to broader sensor modalities and increasingly abstract spatial representations.

• (Cohen et al., 2023) Unsupervised Acoustic Scene Mapping Based on Acoustic Features and Dimensionality Reduction
• (Heikkinen et al., 30 Jan 2026) Beyond Omnidirectional: Neural Ambisonics Encoding for Arbitrary Microphone Directivity Patterns using Cross-Attention
• (Hsu et al., 2023) Learning-based Array Configuration-Independent Binaural Audio Telepresence with Scalable Signal Enhancement and Ambience Preservation
• (Wang et al., 2022) Self-supervised Learning of Audio Representations from Audio-Visual Data using Spatial Alignment
• (Jot et al., 2021) Rendering Spatial Sound for Interoperable Experiences in the Audio Metaverse
• (Gayer et al., 2024) Ambisonics Encoding For Arbitrary Microphone Arrays Incorporating Residual Channels For Binaural Reproduction
• (Dementyev et al., 28 Jan 2026) PhaseCoder: Microphone Geometry-Agnostic Spatial Audio Understanding for Multimodal LLMs
• (III, 2019) A Spatial Sampling Approach to Wave Field Synthesis: PBAP and Huygens Arrays
• (Ahrens, 2022) Ambisonic Encoding of Signals From Equatorial Microphone Arrays