DARAS: Dynamic Audio-Room Acoustic Synthesis

• DARAS is a framework that models dynamic room acoustics as time-varying impulse responses, enabling realistic spatial audio synthesis in dynamic environments.
• It integrates physics-based simulations and learned conditioning to ensure accurate temporal consistency, capturing effects like Doppler shifts and distance-dependent attenuation.
• DARAS underpins applications in XR, audio algorithm evaluation, and adaptive sound field reconstruction, bridging simulation fidelity with perceptual realism.

Searching arXiv for recent DARAS-related papers and the cited works to ground the article. Dynamic Audio-Room Acoustic Synthesis (DARAS) denotes the generation of realistic, controllable, and often dynamic room acoustics, including time-domain, multichannel audio that is consistent with the physics of sound propagation in a 3D environment where sources and/or receivers are moving. In current research usage, DARAS spans room simulation or room-impulse-response generation, dynamic propagation, spatial rendering, and adaptive scene-conditioned synthesis for applications such as training and evaluating audio algorithms, sound classification, detection, localization, beamforming, immersive XR, and real-time auralization (Götz et al., 5 Sep 2025, Barbisan et al., 21 Jan 2026, Koyama et al., 17 Mar 2025).

1. Conceptual scope and acoustic representation

Under linear time-invariant assumptions, a room is characterized by its room impulse response $h(t)$, and the output is $y(t)=x(t)*h(t)$. The RIR is commonly decomposed into direct sound, early reflections up to approximately $50$–$80$ ms, and late reverberation, which is often treated statistically. DARAS generalizes this static picture to dynamic scenes in which listener pose, moving sources, changing room configuration, and changing reproduction setups must drive a time-varying room model and a spatial renderer (Koyama et al., 17 Mar 2025).

The literature places DARAS at the intersection of room simulation and spatial audio reproduction. The survey literature organizes the rendering side into channel-based audio, object-based audio, Ambisonics and Higher-Order Ambisonics, Wave Field Synthesis, and binaural reproduction. Object-based audio is described as a natural control-level representation because a scene is a sum of objects with signals and metadata, while binaural and 6-DOF XR rendering are emphasized as primary reproduction targets for interactive systems. This suggests that DARAS is best understood not as a single algorithmic family but as an architecture in which a dynamic scene drives a room model, which in turn drives a spatial renderer and produces binaural or loudspeaker output (Koyama et al., 17 Mar 2025).

A recurring distinction in the literature is between physical accuracy and perceptual or task realism. Some systems target measurement-equivalent evaluation of downstream algorithms, others target scene-consistent immersive rendering, and others target perceptually preferred acoustics rather than geometric fidelity. This distinction matters because the conditioning variables, validation procedures, and acceptable approximations differ substantially across these use cases (Götz et al., 5 Sep 2025, Arellano et al., 16 Jul 2025, Verma et al., 2022).

2. Physical models of dynamic propagation

A central dynamic requirement is temporal consistency: changes in motion should be heard only after the corresponding propagation delay. In the DynamicSound framework, this is enforced by solving the travel-time equation

$$\|\mathbf{p}_r(t_r) - \mathbf{p}_s(t_e)\| = c \cdot (t_r - t_e),$$

where $t_e$ is emission time, $t_r$ is reception time, and $\mathbf{p}_s,\mathbf{p}_r$ are source and receiver trajectories. This directly yields finite time-of-flight, Doppler effects, and distance-dependent attenuation, and it avoids the physically incorrect behavior in which source dynamics are reflected instantaneously at the receiver (Barbisan et al., 21 Jan 2026).

DynamicSound also models geometric spreading, ISO 9613-1 air absorption, and first-order reflections from planar surfaces by image sources. For a moving source and microphone array, each microphone channel is synthesized by solving for the emission time, evaluating the source signal at a non-integer delay, applying geometric attenuation and air-absorption filtering, and summing direct and reflected contributions. The result is temporally consistent multichannel audio preserving inter-microphone time delays, inter-microphone level differences, and environment-induced spectral coloration (Barbisan et al., 21 Jan 2026).

For moving-listener measurement data, the trajectoRIR database makes the dynamic formulation explicit through a time-varying convolution model,

$$\hat{y}_{tr}(k) \approx \sum_{m} h(m, \mathbf{r}(k))\, x_{tr}(k-m),$$

where $\mathbf{r}(k)$ is the cart trajectory. The same work also formulates dynamic RIR estimation as a state-space model with a random walk in location,

$y(t)=x(t)*h(t)$0

with $y(t)=x(t)*h(t)$1. This formulation is directly compatible with DARAS systems that treat $y(t)=x(t)*h(t)$2 as a smoothly evolving latent state along a path (Damiano et al., 29 Mar 2025).

At the wave-physics level, room simulation remains governed by the acoustic wave equation,

$y(t)=x(t)*h(t)$3

which wave-based solvers such as DG-FEM solve directly. This is the regime that captures interference, diffraction, modal behavior, and phase, and it is therefore the regime against which lower-cost surrogates are often judged (Götz et al., 5 Sep 2025).

3. Simulation paradigms and room-response synthesis

The current DARAS literature spans several room-synthesis paradigms. Geometrical acoustics models sound as rays and typically uses ray tracing, beam tracing, image sources, or acoustic radiance transfer; wave-based methods solve the wave equation; and artificial reverberation methods such as Feedback Delay Networks and Scattering Delay Networks synthesize reverberant structure from compact parametric networks. These paradigms differ primarily in how they trade physical fidelity against computational cost and dynamic reparameterizability (Koyama et al., 17 Mar 2025, Sena et al., 2015, Mezza et al., 2024).

A key empirical result for DARAS trustworthiness is that not all simulators are equally valid as surrogates for measured rooms. In a direct comparison of measured and simulated RIRs for algorithm evaluation, DG-FEM yielded similar evaluation results as measurements for all three evaluated ASP/AML algorithms, whereas geometrical acoustic simulations could not replicate the measured evaluation results as reliably. The paper reports, for example, that for diffusion dereverberation with PESQ, DG-FEM achieved $y(t)=x(t)*h(t)$4 and RMSE $y(t)=x(t)*h(t)$5, while GA-RT achieved $y(t)=x(t)*h(t)$6 and RMSE $y(t)=x(t)*h(t)$7. This establishes that wave-accurate simulation can function as a trustworthy surrogate for measurement-based evaluation, whereas pure ray tracing can produce a materially different ranking of algorithm performance (Götz et al., 5 Sep 2025).

For efficient synthetic reverberation, the Scattering Delay Network offers a physically parameterized delay-network model with one scattering node per significant reflective surface, exact rendering of direct sound and first-order reflections, and progressively coarser approximations of higher-order reflections. The paper reports that the rate of energy decay is close to that obtained with the image method and consistent with the predictions of Sabine and Eyring equations, while the time evolution of the normalized echo density is also close to that of the image method, at computational complexity comparable to a feedback delay network (Sena et al., 2015).

Differentiable FDN work pushes this direction further by learning the entire parameter set, including delay-line lengths, through backpropagation. In the 2024 differentiable-FDN model, a time-invariant frequency-independent FDN is optimized against a perceptually motivated time-domain loss combining an Energy Decay Curve term and a differentiable echo-density term. In the later differentiable-FDN RIR-synthesis work, the RIR is explicitly split into early reflections $y(t)=x(t)*h(t)$8 and reverberant tail $y(t)=x(t)*h(t)$9, with

$50$0

That method reports approximately $50$1 FLOPs per sample, compared with roughly $50$2 FLOPs per time step for direct convolution and approximately $50$3 FLOPs per sample for a representative FFT-based partitioned-convolution setting, while matching clarity, definition, and center time of the target RIR closely (Mezza et al., 2024, Gerami et al., 30 Sep 2025).

Differentiable Acoustic Radiance Transfer places differentiability on a geometric-acoustics transport core. DART discretizes the time-dependent acoustic rendering equation, factors transport into geometry-dependent visibility and delay operators plus learnable material scattering operators, and optimizes material properties by gradient descent. In the reported acoustic-field-learning task it shows better generalization under a sparse measurement scenario than existing signal processing and neural network baselines, while remaining a simple, fully interpretable system. This suggests a DARAS route in which geometry-dependent transport is precomputed once, while source, receiver, and material parameters are updated within a differentiable physical model (Lee et al., 19 Sep 2025).

4. Learned conditioning, multimodal inference, and probabilistic synthesis

One major DARAS trend replaces explicit geometry or full simulation with learned conditioning. A direct example is RIR generation conditioned on acoustic parameters rather than room layout. In that framework, the conditioning vector contains $50$4 acoustic parameters: $50$5 broadband features, $50$6 band-wise features, and optionally a 20-band mel-energy profile used as EQ post-processing. The paper evaluates four models—an autoregressive transformer, MaskGIT, a flow matching model, and a classifier-based approach—and reports that the proposed models match or outperform state-of-the-art alternatives, with the MaskGIT model achieving the best performance (Arellano et al., 16 Jul 2025).

A second line targets one-shot or few-shot acoustic transfer. “One-Shot Acoustic Matching Of Audio Signals” replaces explicit RIR measurement with a learned acoustic signature extracted from arbitrary proxy audio in the target room, then uses a Transformer to add a residual in the log-magnitude STFT domain. In listening tests, the ground-truth target audio obtained MOS $50$7, the predicted transformed audio obtained MOS $50$8, and listeners judged the transformed output closer to the target room about $50$9 of the time. This is not a dynamic room simulator, but it demonstrates that room-consistent transformation can be conditioned on a short proxy signal rather than an explicit impulse response (Verma et al., 2022).

Few-shot Acoustic Synthesis with Multimodal Flow Matching moves closer to a general DARAS operator. FLAC models a conditional distribution over RIRs in unseen rooms given sparse acoustic context, spatial coordinates, and a panoramic depth map. It uses a VAE plus a diffusion transformer trained with a rectified flow-matching objective, and the paper states that FLAC outperforms state-of-the-art eight-shot baselines with one-shot on both the AcousticRooms and Hearing Anything Anywhere datasets. Because the model is probabilistic, it represents uncertainty under sparse context rather than producing a single deterministic response, which is particularly relevant when scene geometry or materials are only partially observed (Brunetto, 19 Mar 2026).

On-device multimodal DARAS is exemplified by SAMOSA for XR. Its pipeline fuses shoebox estimation from depth and plane detections, material segmentation from binocular RGB, room estimation over six faces with up to ten material classes per face, scene-type classification, and an RIR synthesis stage combining image-source early reflections with a spectral reverberation network. The paper reports RT60-MAE $80$0 s and EDT-MAE $80$1 s for the full system, a model size of approximately $80$2 MB, and end-to-end latency of approximately $80$3 ms. This establishes that multimodal scene-aware acoustic rendering can be executed on XR hardware under explicit mobile constraints (Xu et al., 14 Nov 2025).

A separate perceptual branch treats room acoustics as an optimization variable rather than a measured or simulated target. “Enhancing Audio Perception of Music By AI Picked Room Acoustics” ranks about $80$4 synthetic impulse responses with a learned perceptual scorer, while “One-Shot Acoustic Matching” learns an acoustic signature from arbitrary room audio. Together these works indicate that some DARAS use cases are fundamentally perceptual and content-adaptive rather than geometrically reconstructive. This suggests a bifurcation in the field between measurement-equivalent rendering and perceptually steered acoustic design (Verma et al., 2022, Verma et al., 2022).

5. Datasets, benchmarks, and validation methodology

DARAS research depends on datasets that expose either dense static fields, dynamic trajectories, or both. The trajectoRIR database is explicitly built around this need: it provides recordings using moving microphones and stationary RIRs spatially sampling room acoustics along an L-shaped trajectory in a room with reverberation time $80$5 seconds. The setup includes a dummy head with adjacent reference microphones, three first-order Ambisonics microphones, two circular arrays of $80$6 and $80$7 channels, and a $80$8-channel linear array; motion is controlled by a robotic cart traversing a $80$9 meter-long rail at $$\|\mathbf{p}_r(t_r) - \mathbf{p}_s(t_e)\| = c \cdot (t_r - t_e),$$0 m/s; and the database contains $$\|\mathbf{p}_r(t_r) - \mathbf{p}_s(t_e)\| = c \cdot (t_r - t_e),$$1 stationary RIRs plus perfect sweeps, speech, music, and stationary noise recorded during motion (Damiano et al., 29 Mar 2025).

For generative RIR augmentation, the ICASSP 2025 challenge defines a task in which sparse enrollment RIRs and geometry must be expanded into dense mono RIRs for downstream single-channel speaker distance estimation. The dataset uses $$\|\mathbf{p}_r(t_r) - \mathbf{p}_s(t_e)\| = c \cdot (t_r - t_e),$$2 rooms, including $$\|\mathbf{p}_r(t_r) - \mathbf{p}_s(t_e)\| = c \cdot (t_r - t_e),$$3 Treble rooms with $$\|\mathbf{p}_r(t_r) - \mathbf{p}_s(t_e)\| = c \cdot (t_r - t_e),$$4 simulated RIRs at $$\|\mathbf{p}_r(t_r) - \mathbf{p}_s(t_e)\| = c \cdot (t_r - t_e),$$5 kHz and $$\|\mathbf{p}_r(t_r) - \mathbf{p}_s(t_e)\| = c \cdot (t_r - t_e),$$6 GWA rooms with $$\|\mathbf{p}_r(t_r) - \mathbf{p}_s(t_e)\| = c \cdot (t_r - t_e),$$7 simulated mono RIRs at $$\|\mathbf{p}_r(t_r) - \mathbf{p}_s(t_e)\| = c \cdot (t_r - t_e),$$8 kHz, for a total of $$\|\mathbf{p}_r(t_r) - \mathbf{p}_s(t_e)\| = c \cdot (t_r - t_e),$$9 RIRs. Evaluation is split into direct RIR generation and downstream distance estimation, with Task 1 metrics given by T20 Mean Absolute Percentage Error, EDF Mean Squared Error, and DRR Mean Squared Error, and Task 2 metrics given by Mean Absolute Error and Mean Absolute Percentage Error of the speaker-distance estimator (Lin et al., 22 Jan 2025).

Validation methodology is itself an important DARAS topic. The room-acoustic-surrogate study argues that the relevant question is not only whether simulated and measured RIRs look similar, but whether they produce equivalent downstream algorithm evaluations. That work therefore compares Pearson correlation and RMSE between measured-data and simulated-data performance metrics for dereverberation and speaker-distance estimation. This evaluation protocol is particularly significant for DARAS engines intended for dataset generation or algorithm benchmarking, because it tests whether synthetic acoustics preserve task-relevant cues rather than only room-acoustic descriptors (Götz et al., 5 Sep 2025).

A second validation axis emphasizes geometry-consistent evaluation. FLAC introduces AGREE, a joint acoustic-geometry embedding that supports retrieval and distributional metrics for generated RIRs. This suggests that future DARAS benchmarks may increasingly combine classical acoustic metrics such as $t_e$0, EDT, $t_e$1, $t_e$2, or DRR with geometry-aware representation-space evaluation, especially in multimodal and few-shot settings (Brunetto, 19 Mar 2026).

6. Applications, misconceptions, and open directions

DARAS applications already span training and evaluation of ASP/AML algorithms, sound classification and detection, source localization, beamforming, multi-microphone enhancement, virtual room navigation, telepresence, spatially dynamic sound field reconstruction, immersive XR, and music production or postproduction. DynamicSound is explicitly positioned as a data generator for sound classification, detection, source localization, beamforming, and multi-microphone enhancement; trajectoRIR targets sound source localization and tracking, spatially dynamic sound field reconstruction, auralization, and system identification; and SAMOSA targets XR auditory realism under on-device constraints (Barbisan et al., 21 Jan 2026, Damiano et al., 29 Mar 2025, Xu et al., 14 Nov 2025).

A common misconception is that matching geometry and materials in a geometrical-acoustics simulator is sufficient for evaluation-grade realism. The measured-versus-simulated study shows otherwise: geometrical acoustic simulations could not replicate the measured evaluation results as reliably, whereas DG-FEM did. Another misconception is that a dynamic acoustic engine must be either fully wave-based or fully learned. The literature is more mixed: DynamicSound uses a continuous-time physical propagation model with first-order reflections, differentiable FDN and SDN methods use compact recursive late-reverb structures, SAMOSA uses multimodal perception plus a hybrid analytic RIR generator, and FLAC uses probabilistic latent generation conditioned on geometry and sparse context (Götz et al., 5 Sep 2025, Barbisan et al., 21 Jan 2026, Sena et al., 2015, Xu et al., 14 Nov 2025, Brunetto, 19 Mar 2026).

Current limitations are also consistent across the literature. DynamicSound has first-order reflections only, no higher-order reflections or reverberant tails, no diffuse reverberation, simplified reflection coefficients, and no occlusion or diffraction. SAMOSA is tuned for common indoor rooms, uses a shoebox abstraction, and does not directly sense actual acoustics. The differentiable-FDN work reported time-invariant frequency-independent FDNs, so spectral coloration remained a limitation. DART is restricted by geometric-acoustics assumptions and current computational cost, while the acoustic-parameter-conditioned generators in the cited work operate on mono RIRs truncated to $t_e$3 s (Barbisan et al., 21 Jan 2026, Xu et al., 14 Nov 2025, Mezza et al., 2024, Lee et al., 19 Sep 2025, Arellano et al., 16 Jul 2025).

The forward-looking agenda is correspondingly hybrid. The survey literature emphasizes 6-DOF XR audio, personalized binaural audio, physics-informed ML for sound field estimation, adaptive RIR estimation in situ, and integrated estimation-rendering loops. DynamicSound points to higher-order reflections, diffuse reverberation, diffraction, occlusion, and binaural/HRTF-based rendering. DART points toward diffraction modeling and richer BSDF parameterizations. FLAC suggests probabilistic few-shot synthesis as a route to data-efficient room adaptation. A plausible implication is that mature DARAS systems will combine scene sensing, sparse measurement, compact physical models, and learned surrogates rather than relying on any single paradigm alone (Koyama et al., 17 Mar 2025, Barbisan et al., 21 Jan 2026, Lee et al., 19 Sep 2025, Brunetto, 19 Mar 2026).