Resounding Acoustic Fields with Reciprocity
Authors: Zitong Lan
Yiduo Hao
Mingmin Zhao
Venue: NeurIPS 2025 poster ()
Keywords: Acoustic Field
Audio Learning
Reciprocity
Primary area: applications

Abstract: Achieving immersive auditory experiences in virtual environments requires flexible sound modeling that supports dynamic source positions.
In this paper, we introduce a task called resounding, which aims to estimate room impulse responses at arbitrary emitter location from a sparse set of measured emitter positions, analogous to the relighting problem in vision.
We leverage the reciprocity property and introduce Versa, a physics-inspired approach to facilitating acoustic field learning.
Our method creates physically valid samples with dense virtual emitter positions by exchanging emitter and listener poses.
We also identify challenges in deploying reciprocity due to emitter/listener gain patterns and propose a self-supervised learning approach to address them.
Results show that Versa substantially improve the performance of acoustic field learning on both simulated and real-world datasets across different metrics.
Perceptual user studies show that Versa can greatly improve the immersive spatial sound experience.

Resounding Acoustic Fields with Reciprocity
Zitong Lan
Yiduo Hao University of Pennsylvania
Mingmin Zhao
University of Pennsylvania ztlan@seas. upenn edu
University of Pennsylvania mingminzdseas. upenn. edu
y yiduohao@seas. upemn edu
httpa:/ waves. seas. www.n.upeeeee.m wwt.:pojecctt/eeeraeeee
Abstract
Achieving immersive auditory experiences in virtual environments requires flexible sound modeling that supports dynamic source positions. this paper, we intro- duce task called resounding, which aims to estimate room impulse responses at arbitrary emitter location from a sparse set of measured emitter positions, analo- gous to the relighting problem in vision. We leverage the reciprocity property and introduce Versa, a iinspird approach facilitating acoustic field learning. Our method creates physically valid samples with dense virtual emitter positions by exchanging emitter and listener poses. We also identify challenges in deploying reciprocity due to emitter/lsstnner gain patterns and propose self-supervised learn- ing approach to address them. Results show that Versa substantially improve the performance of acoustic field learning on both simulated and real-world datasets across different metrics. Perceptual user studies show that Versa can greatly im- prove the immersive spatial sound experience. Code, dataset and demo videos are available on the project website.
Introduction
The rapid development of AR/VR technology has highlighted the role of multi-sensory synthesis. where both visual and audio signal must be rendered coherently to provide immersive experiences [II] 53 64]. While significant progress has been made in modeling dynamic visual field [77 20
the acoustic counterpart remains largely limited to static scenarios. Recent works have demonstrated
impressive results in modeling acoustic fields X 34 37
59, but primarily focus on fixed sound source. This constraint limits auditory experiences where sources move through the scene, such as conversations between walking participants or footsteps echoing through corridors.
39
44
46
54
In this paper, we study a novel task of estimating acoustic fields at arbitrary emitter positions, given observations from only a sparse (i.e., fewer than 10) set of emitters. This setup is motivated by practical deployment constraints: while microphones are compact, inexpensive, and easy to place in dense arrays, speakers are bulky, power-hungry, and challenging install at scale [18]. Moreover, simultancously operating multiple speakers in the same room can introduce significant interference. We frame this task as resounding, parallel to relighting computer graphics. Just as relighting enables control over illumination in virtual scenes, resounding enables dynamic placement of sound sources while preserving physical realism. The core challenge lies in modeling how acoustic fields vary with emitter positions in way that is physically consistent and generalizable.
Modeling acoustic fields fundamentally relies on estimating impulse responses, which characterize how sound waves propagate within an environment. Existing learning-based approaches to this problem can be broadly categorized into two classes. The first class follows the neural radiance field paradigm [4] in graphics, where neural networks learn spatially continuous acoustic fields from data 34 36 39 54] However, these methods struggle with sparse emitter locations they require densely (i.e., hundreds or thousands) deployed emitters to generalize to novel emitter locations. The second class leverages differentiable ray tracing [59 26 to explicitly model acoustic propagation paths. While this approach improves generalization. it relies heavily on simplified geometry (e.g., few dozen planar surfaces), leading to significant errors in environments with complex structures.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Scene
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Scene
Loudness
Phase@1kkte
Loudness
Phase@lkkie
Loudress
Phase@1cct
Baseline
15 emiteer
Versa-SSL
Ground
truth
Figure 1: Estimated acoustic field at novel emitter position. For simulated scenes (five training emitters each), show loudness distribution (red: loud, blue: quiet) and phase patterns kHz (periodic blue-red). Purple arrows mark emitter poses. Compared the baseline (AVR (34)) Versa-ELE improves phase map, and Versa-SSL achieves accurate energy map with proper directivity.
In this paper, we introduce Versa, a physics-inspired approach that facilitates realistic resounding under sparse emitter configurations. Our method draws inspiration from reciprocity, fundamental principle in wave propagation. Reciprocity states that if the roles of an emitter and receiver are exchanged, the wave traverses the same path in reverse, and the cumulative propagation effects remain unchanged. This principle holds across various wave phenomena, including light [52], radio-frequency signals [6]. and acoustic waves [3I]. In graphics, this principle underpins powerful algorithms like bidirectional path tracing (32). Similarly, in acoustics, for any path between source and receiver, exchanging their positions preserves wave propagation characteristics 31 48 57].
Building on the principle of reciprocity, we propose Emitter Listener Exchange (Versa-ELE), a strategy that generates physically valid virtual training samples swapping the roles of emitters and listeners while enforcing the same impulse response. This technique addresses a key asymmetry in data collection: dense sampling of listener positions is relatively easier due to the compact and low- cost nature of microphones, whereas emitter deployment is far more limited due to cost, interference, and physical constraints. Versa-ELE effectively transforms densely placed microphones into dense virtual speakers. We implement Versa-ELE as data augmentation and demonstrate that it consistently improves performance across a range of acoustic field models.
While Versa-ELE is broadly effective, assumes same directional patterns between emitters and listeners ensure that the exchanged impulse responses remain unchanged. In practice, however, real-world devices can exhibit asymmetric directional gain patterns, such as an omnidirectional speaker versus highly directional shotgun microphone. To address this limitation without discarding the core insight of reciprocity, we introduce a complementary Self-Supervised Learning strategy (Versa-SSL) that incorporates reciprocity to decouple directional patterns from the acoustic propagation effects, allowing the model to learn from swapped emitter/listener pairs by enforcing consistency between their predicted outputs. This approach generalizes reciprocity to broader set of scenarios and leads to more robust and physically grounded acoustic field estimation, shown in Fig. !
constraint on the model's predictions. Our key idea
We comprehensively evaluate Versa on the resounding task using both simulated and real-world datasets. Versa-ELE is model-agnostic and be applied existing neural acoustic field models, yielding average improvements of 34% on C50 and 31 31% on STFT. On top of the AVR (34,, Versa-SSL. further improves performance by 24% C50 and 48% STFT, demonstrating its effectiveness in scenarios with asymmetric gain patterns. Perceptual study additionally confirms that Versa enhances spatial audio realism and directional consistency.
In summary, our work makes the following contributions:
We leverage reciprocity in wave propagation and propose Versa-ELE,, a simple yet effective strategy to augment sparse emitter data by generating physically valid virtual samples.
We introduce Versa-SSL, self-supervised learning framework that enforces reciprocity- consistency in model predictions and generalizes asymmetric directional gain patterns. These methods serve as a general machine learning training strategy grounded in physical reciprocity.
We implement Versa on multiple models with comprehensive evaluations. Results demonstrate Versa significantly improves acoustic field estimation and enables perceptually realistic resounding.

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2 Related Work
Room Impulse Response Modeling, Impulse response modeling has recently gained a lot of attention, driven by advances in neural acoustic field learning and immersive audio rendering for virtual environments. Early methods 40 relied audio codec and spatial interpolation, which incurred high memory costs and limited inference fidelity [18] 39]. Recent works [ 13
26
34 36
37
employ neural acoustic fields that directly map emitter/listener poses impulse response and achieve better performance. However, these methods require dense emitters to achieve resounding. add-on achieve better resounding performance these methods.
39
Our proposed Versa serves In addition, several works have investigated acoustic adaptation to novel scenes [12]14]] (38]. Although generalization unseen environments is beyond our current scope, our reciprocity-inspired approach can be naturally integrated into both their training and inference pipelines.
Virtual Experience. Novel view synthesis and realistic relighting techniques have enabled many immersive virtual experiences. Neural Radiance Fields (NcRF) [4]] and Gaussian Splatting [27] have transformed the abilitt to synthesize highly realistic novel view images from sparse inputs. More recent works, including relighting- focusee variants 63 17 20 22]. extend these methods to dynamic lighting conditions. Our resounding task, analogous to the relighting problem, aims to
enhance flexibility in audio experiences [33 34 62].
solve the Helmholtz equation directly within a neural framework to estimate acoustic fields from measured impulse responses. These methods learn implicit representation of the underlying PDE parameters without requiring an explicit mesh, but they usually require dense sampling to enforce boundary conditions. In contrast, our method incorporates the physics of acoustic propagation in the training, improving both efficiency and robustness of neural acoustic field estimation.
PINN for Acoustic Modeling, Physics-Infoe Informed Neural Networks (PINNs) 9 30 49
50
Self-Supervised Learning. SSL has by now become a staple of feature representation learning for computer vision 660 15 25 16), audio learning 19 47 2. and other multi-modas learning tasks
43 24 61]. SSL relies augmentation to create synthetic positive pairs to enforce semantic invariance in the trained model, represented by contrastive methods like SimCLR (25) and so on [ Different from prior SSL methods that learn feature representations, our work enforces consistent impulse response outputs to make the model follow signal propagation physics.
3 Method
Impulse response is the summation of the sound propagating through multiple paths, including direct path, early reflections and late reverberations. We first describe the impulse response reciprocity with single path, then extend it to multiple paths. Finally, we introduce our method to improve impulse response estimation inspired by reciprocity via exchanging emitter and listener poses.
3.1 Impulse Response and Acoustic Reciprocity
Single-Path Impulse Response. We model the acoustic impulse response with a ray-based Geometric Acoustic (GA) model similar to [34 [59] We consider first sound propagation between an emitter located at P. and listener located at pr along a single path P. This path P is defined as a sequence of points:
{po = Pe: P1, P2...... PK. PK+1 = p.). For each consecutive pair of points pı and PA+11 along the path, we define W/ as the direction from pa: to Pk+1- We define the impulse response (e:We)
h(t; P,we: wr) for this single path, which characterizes the sound received at pr when the emitter at Pe sends out pulse (i.e., a Dirac delta function), with we and wi being the orientations of emitter and listener. The response is influenced by the gain patterns of the emitter and listener as well as the effects of sound propagation along the path:
(1) where G.) and G.(-) represent the gain patterns of the emitter and listener, respectively. [[t; P) denotes the path impact function for the path P. as described below. Note that for any dry source signal s(t) emitted at p.. the listener at Py receives the reverberant signal y(t) = ((t))h((t we, wi), i.e. the convolution of the dry signal with the path-dependent impulse response h.
htt P. We, wi) = (wo::w)) l(t;; P) G((wk)
Path Impact Function. T(t:P) is time signal that represents the impulse response along a single propagation path P. It characterizes the propagation effect [51] (both attenuation and time delay) in
3

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fip worens))
Sipeian L1nns))
Ge
flp WiiM)
flp
P
Pk
P1
Ph
Listener
Emiller
Emitter Listener Exchange
Listener
Emilter
[(:P) P()P))
Exchange poses
Pl
Pk
flp M1 v) Jipe Wks Mi-
f(Ps. (PP),) P) f(P til 16-
Emitter
Listener
Listaner
A(4 (e) o A(t; Mele
Emitter
Figure Left: Reciprocity in acoustic propagation. The path impact function I'(-) is invariant swapping the emitter and listener poses, hecause the local acoustic transfer function f(-) each point unchanged when the incident and outgoing signal directions are reversed. Right: Impact of gain patterns. Although the propagation path and path impact function remain the same, differences in emitter and listener gain patterns G produce distinct impulse responses h(t; -).
response to Dirac delta function:
1 _r) F-1 1,1, dp (1))
T(G:P)
o
The right-hand side of the equation consists of three components. dy models the attenuation due to wave propagation, where dp is the total travel distance along path This term captures the free-space propagation loss, accounting for the energy spreading as the wave travels through space. 2) The time delay is modeled by the shifted delta function d(t- dr ), where is the speed of sound. It indicates that the signal arrives only after traveling for time de along the path. 3) f(p,, 4 Wk; 2/) is acoustic transfer function modeling how the signal changes when interacting with a surface at point Pk. given the incoming direction W3-1, outgoing direction Wk, and frequency These interactions include reflection, diffraction, scattering, and transmission. The overall rreueencyddepender attenuation due v), where the product accumulates the effect of all interactions along the path. The inverse Fourier Transform _-- converts this Trequency-domati response into the time domain. Finally, the convolution operation models how the propagation-relan related terms are modulated by the frequency-deeendden interaction effects.
to environmental interactions is captured by II (Pk: Wk-1, We
Reciprocity in Single Path. Reciprocity in acoustic propagation at interaction surface (similar to BRDF in light transport (21)) implies that the transferred energy remains consistent when the signal direction reversed: fPk, Wt-1, Wk: 2) [Ps, Wk, W)).. Since each interaction along the path is reversible, the overall signal propagation along the whole path also reversible. the emitter and listener poses exchanged, the path impact function remains the same (left of Fig.
along the reversed path p' = {pK+1 Pe, PK,
Po p:
K
8 dp
I(t;P')= = dp
* F
II
f(k Wk. wk-1;
k=1
(3)
1 dp
dp
8
f(p,, wh (k-1, -1';;; [(t:P)
Therefore, when the poses the emitter and listener are exchanged, the impulse response becomes:
h(t; Pl, we) (e(w ['t;:')) (uggi
If the gain patterns of the emitter/listener are omnidirectional 3.2 i.e., G=))==(((==,, we get:
h(t:; P, wi) httt; P', w.)
(5)
Impulse Response via Multiple Paths with Reciprocity, In realistic environments, signal propaga- tion occurs along multiple paths between the emitter and the listener due to reflections, diffractions, and other interactions. Let P,, denote the n-th path. The overall impulse response h(t) is modeled as the superposition of individual path contributions: h(()) En h[(;; Pn, We, wr). Since each path- specific response h1t;; Pn, We, wi) satisfies reciprocity as shown earlier, the sum of these responses also preserves this property. Thus, the full impulse response h(t) remains reciprocal.

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Room
Acoustic Field
Gr
Acoustic
Em tter
Field
[a] Vanilla
Listener
Swag out
Emitter
Listener
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L
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Exchange poses
Acoustic Field
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Lo
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(b) Emitter Listener Exchange
[c) Impuise R Response Self-Supervised Learning
Figure Leveraging reciprocity for modeling acoustic fields. a) The vanilla method uses direct supervision with measured impulse responses. b) Eersa-EEE enforces response invariance under pose exchange create physically valid virtual samples. c) Versa-SSL aligns emitter and listener gain pattern to maintain consistency under pose exchange and to enable reciprocity-bot self-supervision.
3.2 Reciprocity Learning with Versa-ELE
We propose Emitter Listener Exchange (Versa-ELE) to leverage the reciprocity property under omnidirectional emitter/listener gain patterns. We denote one training sample with emitter and listener poses as (Pe, Pt, We, Wt, h(t)). We exchange the emitter and listener poses and let the impulse response remain identical, resulting in physically valid new sample (pt, Pey Wi, Wes h(t)). This is implemented as data augmentation as shown in Fig. ()). Versa-ELE leverages the inherent asymmetry in the acoustic field transform dense listener positions into virtual emitter locations. It effectively mitigates the sparsity of emitter positions by increasing them from few to N, where N is the number of unique listener positions in the dataset. This exchange encourages the model to understand the signal propagation path and improves its performance in resounding tasks (Fig.
Versa-ELE also extends directional emitters and listeners. Specifically, when both of them have the same gain pattern, i.e., G-(-) = Gr(-), we have: Ge(u); wy) = Gi(o: wt) and (ww Gi(wk; w.). These equations ensure that the joint effect of the emitter and listener's pattern on the impulse response remains unchanged after exchanging. With the single-path reciprocity (Eqn. 3), the final impulse responses h()) remain the same, and Versa-ELB can also be applied here.
3.3 Reciprocity Learning with Versa-SSL.
Versa-ELE works well if the emitter and listener share the same gain pattern. However, shown in Fig. rightt if they have different patterns, directly exchanging their poses would result in a different impulse response, i.e., h(t; P, Pew,, wi) ht;; P', wi we). To deal with this, our solution is to decouple and control the influence the gain pattern to leverage reciprocity for impulse response learning.
For the rest of this section, we first provide a primer on acoustic volume rendering (AVR) [34,, which we find could decouple the influence of the listener's gain pattern. We then introduce a self supervied learning framework based on this to leverage reciprocity under different gain patterns conditions.
Primer AVR and modeling gain patterns. AVR models the acoustic field with direct control over the listener gain pattern Gj, enabled by its acoustic volume rendering technique. To synthesize the impulse response received at a listener location, it integrates signals spherically from all directions:
h(t)) = ho Pes Pt, We) Gi GI w1)d
where (t: Pe, Pt, We) denotes the predicted signal received by the listener at position pt from direction w. (t; Pe, Pt, We) is obtained through acoustic volume rendering, which similar to the volume rendering in image synthesis, but considers acoustic propagation effects. We ignore the parameters Pe, Pt, We on the left side (h()) of the equation for brevity. From this equation, we manipulate the effect of the listener gain pattern by multiplying weights Gi(w; wi) to different receiving directions. It allows us to separately control the listener pattern in the final impulse response synthesis, and we find it be basis to exploit reciprocity for impulse response training.
Versa-SSI. We introduce an impulse response Self-Supervised Learning (Versa-SSI.) method that leverages the reciprocity theory by decoupling gain patterns from impulse response modeling. We
5

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model the emitter and listener gain patterns Ge, G₁ in the neural acoustic field F to output impulse response ht(t), parameterized by emitter and listener poses:
F(ees Pt, We: Wt, Ge. Gi) + h(t).
We propose to manually replace the emitter/listener pattern with a shared one, i.c. G. Under this condition, the acoustic field model should output the same impulse response consistently when emitter/listener poses are exchanged, based on previous discussion (Sec. 3.2
F(Pi, Pe. Pe.Wi, We; G, G) = F(Pe, Pi,We, wi; G, G)
(8)
With this equation defining a reciprocity- -ased consistency constraint, we formulate a suff-supervision learning with pairs created by exchanging poses (Fig. Cc). As result, it forces the neural acoustic field to output consistently after exchanging the emitter/listener poses in a self-supervised way.
Incorporating AVR into our self-supervision framework is not straightforward, as AVR's 's acoustic field does not explicitly model the emitter pattern, which can not be directly replaced for self-supervision. Instead, we exploit the ability to freely manipulate the listener pattern in Eqn. [ We propose to keep the emitter's s pattern unchanged and swap out the listener's pattern with the emitter's to enable this self-supervision. To achieve this, we first query the neural acoustic field at emitter positions and sample the emitted signals across all directions to get the emitter pattern Ge. which is used to swap the listener pattern, making both patterns the same and satisfying the condition for self-supervision.
Versa-SSL Training. We use a two-stage training pipeline for Versa-SSL. In the first stage, we let the AVR model to estimate impulse response h and fit into the ground truth h², This is guided by the audio loss (h, h²) following AVR. This first stage allows the model to capture the acoustic field of the environment. After it, we extract the emitter pattern from the trained neural acoustic field and use a set of spherical harmonic parameters to encode this pattern, i.e. Ge. In the second stage, we swap out the listener pattern with the estimated emitter pattern and force the model to predict identical impulse response pairs (h, and h2) before and after emitter/listener poses exchange. To enforce this consistency, we use self-supervision loss La-sa(h1, h2). We use both real sample and self-supervision in the second stage: L L. Ca h()) + >Cn-sa h22), where balances two losses. Moreover, since Versa-SSL does not rely valid data, can flexibly select any emitter/listener poses. However, excessive flexibility can make the model learn shortcuts. To prevent these, we sample emitter/listener poses from the dataset and gradually add noise to them Versa-SSL progresses.
Versa-SSI. Inference. During inference, we can either use the learned listener gain pattern or swap in any head-related transfer functions (HRTFs) for personalized auditory experiences for immersive auditory rendering.
3.4 Discussion on the Reciprocity
While the principle of reciprocity holds under idealized conditions [31 48
(e.g., linear, time-invariant media with symmetric reflection), real-world environments and simulated systems may deviate from these assumptions due to many factors like measurement noise, non-ideal hardware responses, or complex material properties and so on (23). Rather than assuming perfect reciprocity in the impulse responses, Versa introduces reciprocity as a structural regularization term that enforces consistency between predicted acoustic fields under exchanged emitter-listener configurations. This approach allows the model to benefit from symmetry in wave propagation, even when perfect reciprocity does not strictly hold. We provide some preliminary verification of reciprocity [57 in Sec. 4.2
Evaluation
4.1 Setup
Dataset. We use both simulated and real-world datasets to comprehensively evaluate our methods. We use MeshRIR (2) and RAF [8] as our real datasets, where MeshRIR shows similar emitter/listener gain patterns, and RAF features different emitter/listener patterns. We use the AcoustiX [34] simulator to create synthetic datasets because it provides flexibility to customize emitter and listener gain patterns in the simulation. We apply the AcoustiX simulator on three scenes from iGibson 33 and create two batches of datasets, including using the same emitter/listener gain patterns (AcoustiX- Same) and different gain patterns (AcoustiX-Dii) Diff).. There are fewer than ten training emitter positions

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Scene
Scene
Scene
PhasergJKHH
Phase
Phase@2KHz
INRAS
+ELE
+ELE
NV-NRRF
RV-NERF +ELE
Ground truth
Figure 4: Comparison of acoustic field predictions across baseline methods with and without Versa-ELE. We visualize loudness distribution and phase patterns for three simulated scenes (each with five training emitter positions and identical emilter/listener gain patterns). Purple stars mark emitter positions. Versa-ELE enhances all baseline methods' predictions, particularly in modeling energy distribution around emitters. Ground truth shown in the bottom row.
Faired
Unpoired #1
Eavironment
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C50
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un--airedd
1.74
12.0
3.69
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Kitehen
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0.29
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1.09
7.5
18.7
3.35
120
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wn-paired
1.54
16.5
24.5
2.49
104.5
Office room
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0.23
2.2
0.18
11.0
paired
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16.6
1,95
98.5
T60 28%
$509.31.00
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paired 100k
paired 10k rays
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Figure 5: Paired (left) vs. unpaired (right) impulse responses.
Table 1: Preliminary verification reciprocity.
in each room for all simulated datasets, with densely sampled emitter positions for evaluation. Please refer Appx. A.1 for more details.
Models. We compare the performance of our method with traditional audio encoding baselines using linear and nearest-neighhor interpolation [39 55 599. We implement various neural acoustic field models including NAF [39], INRAS [54], AV-NeRF [37], and AVR (34]. We apply Versa-ELE (Sec. 3.2) all these model. We only apply Versa-SSL (Sec. 3.3 to AVR as it is the only one to explicitly model the gain patterns. In addition, we also implement DiHRIR [59] as a baseline. However, since DifIRIR explicitly traces acoustic paths, Versa is excluded from it because the paths naturally remain consistent after exchanging emitter and listener positions. Please refer to Appx. for implementation details.
we measure the energy trend of impulse response by Reverberation Time (T60), Clarity (C50), and Early Decay Time (EDT). To evaluate the waveform correctness, we include envelope error (Env.), frequency domain amplitude (Amp.) error, and multi-ress rellii-rsooltion short-time Fourier transform enror (STFT).
Evaluation Metrics.
Following prior work 54

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Scene
Sceme
Scene 3
Method
Amp
Env.
STET
Amp.
Eme
T60
C50
EDT
STET
Amp.
C50
EDT
NN
2.87
0.35
3.3
15.8
2.84
3.54
0.47
3.1
43.8
10.71
25.5
3.29
0.47
48.9
7,42
35.6
Lineur
2.72
0.94
6.0
15.9
3.37
20.8
3.32
1.09
5.6
69.7
15.04
37.3
3.15
1.08
6.7
62.8
11.3
51.2
DiRRIR
1.57
0.36
8.0
12.2
2.68
23.9
1.63
0.48
6.7
13.6
2.57
19.4
1.74
0.76
8.8
31.1
5.17
42.5
INRAS
1.96
0.72
2.71
24.6
1,96
0.72
3.6
12.3
2.71
24.6
93.5
7.14
50.3
w/ ELE
1.36
0.23
2.8
12.0
1.72
13.6
1.81
0.31
2.6
11.6
1.98
17.7
1.67
0.41
3.4
20.1
2.79
22.7
NAF
7.29
0.76
3.5
16.9
5.47
27.3
0.74
5.86
43.9
w/ FLE
3.27
0.66
3.5
13.2
2.2
18.2
3.27
0.66
3.1
14.1
2.17
18.2
3.37
0.58
3.3
23.7
3.31
26.5
AV-NeRF
1.69
0.85
4.6
11.0
1.98
14.1
1.87
2.35
4.8
18.7
5.26
36.6
1.85
1.17
5.2
26.2
6.09
49,3
w/ ELE
1.15
0.16
2.4
10.7
1.70
12.5
1.41
0.35
2.9
12.5
2.43
20.3
1.36
0.42
19.8
2.97
23.7
AVR
1.82
0.40
3.1
14.3
2.09
15.9
2.56
0.46
2.9
20.2
3.43
37.6
2.65
0.69
3.6
28.6
2.96
42.3
ELE
2.20
1.28
0.19
11.2
12.9
2.33
20.3
Table 2: Quantitative results on AcoustiX-Samie (5 emitter positions) when emitter/listener shares a gain pattern. We deploy Versa-ELE (i.c., w/ ELE) on existing neural acoustic fields.
4.2 Results
Preliminary Verification of Reciprocity. We begin by empirically verifying the acoustic reciprocity property. We collect impulse responses in an office room, a conference room, and a kitchen. Record- ings are performed using an onnidirectional microphone [4] and an omnidirectional loudspeaker [10], both connected via an audio interface 7 to ensure synchronization. We capture impulse responses between pairs of locations (A, B) within each environment. For every pair, we measure the impulse response emitted from A to B and the reciprocal one from B to A. According to our analysis, these two impulse responses should be identical since both the microphone and speaker are ommidirectional. To quantify reciprocity, we collect 20 such pairs in each scene and compute the distance between (i) paired impulse responses with swapped emitter/listener positions and (ii) un un-paired ones for rest of the data. As shown in Tab. [ the paired ones exhibit = =10x smaller acoustic distance compared with the un- paired ones in all real-world scenes. Examples of paired and un on-paired impulse responses are presented in Fig. [5] the waveform similarity between the paired one also confirm that reciprocity is well preserved. We also replicate this experiment in the AcoustiX simulated dataset. We vary the number of rays in the simulation to analyze how rendering granularity affects reciprocity consistency. In simulated dataset, paired ones achieve RJ x000x smaller distances with 1000k rays.
Results of Versa-ELE. We first evaluate Versa-Ele ELE on similar emitter/listener gain patterns datasets, namely the AcoustiX-Sams dataset in Tab. 2 and the real-world McshRIR dataset in Tab. 3 We find that ELE can be applied to all existing learning- improve their performance by a large margin.
impulse response estimation methods and
Fig. 4 shows a qualitative visualization of the
spatial field distributions in birds-eye view. With an unseen fixed emitter, we densely sample lis- tener locations in the entire scene and plot the loudness and phase map across three scenes. With Versa-ELE, all bascline methods (INRAS, NAF, AV-NeRF, and AVR) achieve notably en- hanced spatial field distributions that better re- flect the emitter positions. Notably, the improve- ment on the AVR model is the most signifi- cant. It produces field that best aligns with the ground truth in both loudness and phase map.
Method
STFT
Amp.
Env.
T60
C50
EDT
NN
2.46
0.42
1.33
7.6
2.34
26.9
Linear
2.65
1.22
2.76
9.9
2.76
30.9
INRAS
1.82
0.54
1.32
13.6
4.04
47.8
ELE
1.73
0.36
1.22
12.9
2.51
28.2
NAF
4.81
0.69
9.9
2.28
26.2
w/ ELE
4.43
0.65
1.14
8.6
2.17
23,5
AVR
3.78
0.61
15.6
4.97
65.2
w/ ELE
1.42
0.33
1.02
7.2
1.79
20.9
Table 3: Evaluation on Versa-ELE method on MeshRIR (7 emitter positions).
Though fL.sa-FLEE does not consider different emitter/listener gain patterns, we evaluate it on such datasets including AcoustiX-Diff in Tab. and RAF-Furnished in Tab. 5 Results show that Versa- ELE can still improve these baselines by reasonable margin, showing ELE's wide applicability to various datasets and real-world settings, especially on neural acoustic fields that cannot incorporate gain patterns modeling (NAF, INRAS, AV-NeRF). Although Versa-ELe -LLE may not accurately account for varying gain patterns, it enhances the model's 's awareness of reciprocity in the propagation path, leading to improved acoustic field modeling. Quantitatively, averaging over all models and simulated scenes, Versa-ELL ELE improves their performance by 34% at C50, and 31% at STFT.
Results of Versa-SSI. We evaluate Versa-SSL on datasets with different emitter/listener gain patterns, including the AcoustiX-Diff dataset in Tab and the RAF dataset in Tab. While we show that Versa- ELE can be applied to all the existing neural network- impulse response estimation methods and
8

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Sceme
Seene
Scene 3
Method
SIFT
Amp.
Env.
T60
C50
EDT
STFT
Amp.
Env.
T60
C50
EDT
STFT
Amp.
Env.
T60
C50
EDT
NN
2.86
2.52
0,48
4.9
77.7
116.5
2.74
0.60
9.5
17.13
Linear
2.44
0.67
9.1
70.5
18.1
253.4
1,96
0.95
8.1
77.5
22.2
238.8
2.26
0.69
11.6
61.1
17.34
216.9
DiTRIR
1.59
0.35
7.3
16.5
2.52
24.2
1.61
0.68
7.4
18.8
3.17
24.5
1.68
0.64
8.2
26.7
3.91
32.8
INRAS
5.3
6.45
2.65
1.56
3.8
67.9
44.3
4.12
1.055
4.3
6.94
67.1
w/ ELE
1.91
0.31
4.3
16.8
2,93
23.1
1.76
0.64
3.4
25.5
4.36
31.3
1.58
0.38
3.9
38.4
3,13
29.6
NAF
5.21
0.59
4.8
37.5
4,17
34.9
6.21
0.73
3.2
40.5
5.61
40.3
8.12
0.71
4.2
57.7
6.41
51.6
4.12
0.56
4.51
0.61
3.1
22.8
4.43
32.5
4.09
0.56
32.4
4.50
AWNNRRFP
1.99
1.17
6.3
18.5
4.32
43.1
2.16
1.22
3.6
16.5
3.01
24.7
2.27
1.49
6.0
35.1
5.45
49.7
w/ ELE
1.57
0.35
4.7
16.1
2.72
21.6
1.61
0.42
3,0
16.9
2.85
22.9
1.62
0.72
4.7
32.7
4.97
40.1
2.65
4.96
2.65
0.95
3.6
76.1
5.50
64.3
2.15
4.2
5.69
w/ ELE
1.83
0.34
3.9
17.3
2,83
29.2
1.79
0.48
2,5
24.4
3.79
26.8
1.62
0.48
3.8
32.1
4.36
42.5
1.39
2,09
1,26
0.27
15.4
21.6
1.31
0.27
3.5
19,4
2.07
Table 4: Results on AcoustiX-Diff (5 emitter positions) when emitter and listener have different gain patterns.
We apply Versa-ELE for all the models, and Versa-SSI. method on AVR.
Azimuth pattern 0'
Elevation pattern C'
25
T60 (%)
C50 (db)
EDT (ms)
1.0
w/o ELE
w/o ELE
ELE
315
0.8
45
20
ELE
ELE
100
w/ ELE
0.6
0.4
A
15
75
0.2
0.2
2704
90 270
0*
10
50
225
Learned pattern
135
0
2
4
6
6
0
2
6
0
2
4
6
8
Ground truth Pattern
Num. of emitter positions
Figure 6: Learned gain pattern with direction- Figure 7: Performance across different numbers of emitter integrated impulse responses.
positions.
improve their performance, Versa-SSL could further surpass Versa-ELE by 24% at C50, and 48% at STFT on the AVR model. In total, Versa-SSL improves vanilla AVR by 49% at C50, and 66% at STFT. These improvements can be largely attributed to handling the gain pattern correctly with self- supervision, which makes the model aware of reciprocity in the acoustic propagation.
Method
Amp.
T60
C50
SIFT
NN
0.75
23.4
6.5
Linear
0.99
16.9
29.1
7.6
201.9
4.28
DIARIR
0.71
22.3
28.9
4.23
154.3
1.61
Recall Fig. [ we visualize the spatial field dis- tributions when the emitter and listener have different gain patterns. With both the ELE and SSL strategies, the AVR model can produce more accurate phase maps that align with the ground-truth. However, Versa-ELE cannot accu- rately reflect the direction of the emitter in the loudness map, we force the model to learn inaccurate impulse responses after exchanging poses, which causes directionality confusion. In contrast, as Versa-SSL deals with gain pattern properly through self supervupev,,,,n, it helps to build a correct acoustic field with better directionality. We also show our estimated gain pattern on the AcoustiX- -Dff in Fig. 6 indicating that our estimation is close to the ground truth.
INRAS
0.66
8.8
19.4
5.63
149.6
3,21
w/ ELE
0.50
6.6
16.3
4.67
117.3
3.43
NAF
0.65
15.4
5.91
6.22
w/ FLE
0.63
5.9
14.7
5.20
109.5
5.46
NeRF
1.27
10.1
15.3
4.57
110.7
2.55
w/ ELLE
0.66
6.5
12.7
4.12
96.4
2.41
AVR
0.59
5.8
14.8
4.24
99.6
2.35
w/ ELE
0.51
5.2
13.9
4.14
95.8
2.16
w/ SSL
0.48
4.9
12.2
3.65
83.6
1.91
Table 5: Results on RAF LAF--uunished (4 emitters).
Perceptual user study. We conducted a user study to evaluate the subjective quality of impulse response esti- mation. In total, we generate eight audio clips from three simulated scenes involving moving listeners and speakers. We also simulate reference impulse responses as ground truth. Each estimated and reference impulse response is convolved with dry sound, and participants watch a video from the microphone's perspective for added real- ism. They are then asked to compare the generated audio to the reference in terms of volume, directional cues, and overall similarity.
ELE vs Vanilla
SSL. vs ELE.
Volume
90.0%%
88.3%
Directional
90.0%%
90.0%
Overall
93.3%
85.0%
Table 6: Results of the perceptual user study.
Our user study with 15 participants shows that 93.3% of responses preferred Versa-FELE over the vanilla model in terms of overall similarity. Additionally, 85% of responses indicated that Versa-SSL

---

outperforms Versa-ELE in sound volume and directional cues. As shown in Tab. 6 Please find more details in the Appx. B.3
4.3 Ablation studies
What is the influence of number of emitter positions? We analyze how varying the number of emitter positions affects Versa. As shown in Fig. 7 while increasing the number of emitter positions in the dataset can improve the vanilla AVR performance, with the help of Versa-ELE, we can further improve the model's 's performance by 50%. This shows that Versa consistently improves the vanilla model by large margin across different numbers of emitter positions. More ablation results on different methods with various numbers of emitter and listener positions can be found in Appx. B.
Study objoctive
Variant
Amp
Eny
T66
C50
EDT
NAF w/o Versa-EEE
0,33
5.9
13.5
2.83
50.4
Does the model learn reciprocity?
NAF VersaELEE
0.27
3.0
0.45
AVR w/o Versa-ELE
0.54
10.6
16.6
4.13
86.9
AVR w/ Versa-ELE
0.06
0.5
4.8
0.61
10.8
Comparison with
Vanilla
0.89
3,97
52.8
52.4
Contrastive loss
0.47
3.45
25,3
3,22
36.0
common SSL
Verso-SSL
0.26
3.10
16.8
1.82
22.4
Table 7: Ablation on the reciprocity verification on the trained model and self-supervision loss.
Does the model learn reciprocity with Versa? We emphasize the importance of verifying whether the neural acoustic field models trained with Versa indeed exhibit reciprocity. To this end, randomly query the trained AVR and NAF models with swapped emitter listener pairs across different scenes and measure the similarity between the predicted impulse responses. As summarized in Tab. 7 the models trained with Versa produce paired predictions that achieve substantially lower acoustic metric differences compared to those trained without Versa. These results demonstrate that Versa effectively encourages the neural acoustic field to learn and preserve reciprocity.
How effective is Versa-SSL compared with standard contrastive loss? We also evaluate standard contrastive loss by treating emitter/listener swapped pairs as positives and others as negatives. As shown in the bottom of Tab. 7 contrastive loss yields smaller gains over the Versa-SSL. We attribute this to the ambiguous notion of negatives pairs in acoustic field modeling. Unlike vision tasks with clearly separable categories, impulse responses from different spatial locations can exhibit nearly identical patterns, while responses from nearby poses may differ drastically due to occlusions or boundary effects. As result, spatial distance alone does not reliably define negative pairs.
Discussion
Conclusion. In this work, we leverage acoustic reciprocity to improve impulse response estimation, addressing the challenge of resounding under sparse emitter configurations. We introduce the Versa- ELE to augment training data which leads to substantial improvements in neural acoustic models. In addition, we propose a Versa-SSL to handle complex gain pattern asymmetries, enabling more accurate acoustic field synthesis. More broadly, our results highlight paradigm where fundamental physical laws can be translated into machine learning training strategies. improving generalization under sparse data. This principle applies beyond acoustics to light transport and RF propagation, where physical symmetries also govern wave interactions. By embedding these constraints directly into the learning process, models can extrapolate more robustly to unseen configurations and maintain physical correctness even under limited supervision. We envision that such physics-grouss grounded learning frameworks will serve foundation for future research at the intersection of simulation, perception and more across multiple sensing modalities.
Limitation and Future Works. Despite its effectiveness, Versa still requires non-sparse number of listener positions per scene, limiting its scalability in scenarios where dense microphone deployment is impractical. Moreover, our current acoustic modeling relies on ray-based geometric acoustics, which may be less accurate for low-frequency sounds whose wavelengths are comparable to or larger than scene dimensions. Future work can explore few-shot or zero-shot approaches by integrating more audio-visual correspondence and other acoustic properties beyond reciprocity, which can further reduce the need for dense sampling of listener positions.
10

---

Acknowledgments
We thank the members of the WAVES Lab at the University of Pennsylvania for their valuable feedback. We are grateful to the anonymous reviewers for their insightful comments and suggestions.
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14

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A Additional Implementation Details
A.1 Dataset Details
AcoustiX. We show the emitter locations in three different simulated scenes from the iGibson dataset [35] in Fig. 8 For each scene, there are five emitter positions in the training set, and each with around 5K emitter pairs, resulting 25K training samples. We randomly sample 10 novel emitter locations to construct the testing set, resulting in a total of 15K samples.
Scene
Scene 2
Scene
Figure 8: Visualization dataset emitter placements.
MeshRIR. We subdivide the S32-441 variant to use 7 emitter positions (3K emitter/listener pairs) as training samples, while reserving 25 emitter positions (11K total emitter/listener pairs) for testing.
RAF. We randomly select 4 emitter positions in both FurnishedRoom and EmptyRoom split, resulting total of 1.5K training samples, and we resample 6K testing samples in each setting.
Dataset setup. For similar emitter/listener gain patterns, we utilize the S32-M441 split MeshRIR dataset and sub-divide it to include seven emitter positions in the training set. We create simulated AcoustiX datasets while maintaining the emitter and listener to have the same pattern, naming AcoustiX-Sanes For different emitter/listener gain patterns, we real-world RAF Furnishedroom and Emptyroom split, while re-dividing these to include several sparse (< 10) emitter positions for training. We again AcoustiX to simulate the listener gain patterns, naming it AcoustiX- -ifff. The impulse responses are resampled to 16 kHz sampling rate for the AcoustiX and RAF datasets and 24 kHz sampling rate for the MeshRIR dataset.
three scenes but set different emitter and
A.2 Model Details
implementation. We use a STFT window size of 256 (hop size 64) to deal with 16 KHz impulse response and a STFT window size of 512 (hop size 128) to deal with 24 KHz impulse response. We let the model directly predict the amplitude of the spectrogram and use random phase to synthesize the final impulse response. We only implement the Versa-ELE method on NAF, We fully exchange the emitter and listener poses and generate new impulse response sample in the AcoustiX and MeshRIR datasets. For the RAF dataset, we only exchange the emitter and listener positions, since no listener orientation provided.
NAF. We create 3D grid features for original NAF 39
AV-NeRF. We use AV-NeRF 37 SoundSpace variant to predict the room impulse response on all the experiments. We train a vision NeRF model first and reconstruct novel-view RGB and depth images at the listener location and direction in 256x251 256 resolution. We used frozen ResNet- 18 trained on ImageNet the feature extractor. We follow a similar implementation for the Versa strategy as in NAF.
INRAS. We use the 3d positions of emitter, listener, and bounce points in the scene and the orientation of emitter and listener as network input, similar in [8]. We randomly sample 1024 points in the scene represent the scene geometry. We also follow similar implementation for the Versa strategy as in NAF.
AVR. We implement the Versa-ELE method AVR 34 by fully exchanging the emitter and listener poses and generating new impulse response sample. For Versa-SSL, to estimate the emitter gain pattern in AVR, query the network with the emitter's positions to obtain the signals transmitted from these positions in various directions. To reconstruct the gain pattern, the directionality is characterized by the average signal energy in each specific direction, which is subsequently normalized across all directions.
DiffRIR. DiffRIR (59 takes the emitter locations and listener locations to trace paths between them. Then it optimizes set of acoustic parameters along the traced path. For each scene, we use around 40 simplified meshes to represent the room geometries. We used maximum of 3-bounce ray tracing
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(S5
EDT [ms)
with WYELE
WEZLE
W 11E
35-
10
(56
(ms)
ALE WILLL
N/S ELF
NAW ELE
N/S ELE
W HI
TAV
30
WELE
25
T60 (55)
C50 (db)
EDT [ms]
155
ELE N/ELE
NYELE
ELE
R1100
no ELC
niLLE
with ULE
20-
T60 (35)
C50 (db)
EDT Ims)
STFT
(ms)
An
wit
25
Performance MeshRIR dataset.
Performance on AcoustiX dataset.
Figure 9: Performance comparison of different methods on (a) MeshRIR and (b) AroustiX-Same datasets.
with up 10 axial bounces keep the training time manageable. DiffRIR is excluded from using any of our Versa method because already traces the propagation paths, enforcing that the reciprocity property holds.
A.3 Implementation Details
For NAF, INRAS, AV-NeRF, and AVR experiments, we use the AdamW optimizer 28 with a cosine learning rate scheduler that starts from 10-3 and decays to 10-1, with a batch size of 64 for NAF, INRAS, and AV-NeRF, and a batch size of 4 for AVR. implement Versa-SSL method on AVR, we use a fourth-order spherical harmonics | to estimate the emitter gain pattern. We initiate the second stage Versa-SSL on AVR halfway through the total training epochs, gradually adding position variation to standard deviation of 0.3 m. We use loss weight 0.8.. In the DifiRIR experiments, we use an Adam W optimizer with a learning rate 10 and a batch size of 4. For AV-NeRF, we use nerfacto [55] to render RGB and depth images following [37 [8]. We train all the models NVIDIA L40s GPUs for 200 epochs.
B
Additional Experiment Results
B.1
Quantitative Results
More results on RAF dataset. We show more results on the RAF-EmptyRoom on Tab. our evaluation metries show the effectiveness for both Versa-SSL and Versa-ELE as well.
More results on different scales of the dataset. To further show that our proposed method is robust to different methods with various num- bers of emitter positions, we plot the metrics for each method Fig. and pb MeshRIR and AcoustiX- -Saee dataset. Versa can consistently improve each baseline model under different numbers of emitter settings, even with denser emitter placements.
Method
Amp.
T60
STFT
NN
1.95
11.1
15.9
7.25
174.4
5.42
Lincar
2.63
12.9
27.7
8.03
216.4
4.97
DiNRRR
1.40
4.2
24.4
3.29
122.0
1.93
INRAS
1.09
16.2
19.1
7.13
123.2
2.59
18.2
4.25
2.54
NAP
0.99
7.3
13.5
3.87
84.7
3.42
w/ ELE
0.89
6.6
13.5
4.12
84.1
3.39
In addition to emitter positions, we also show that our method is effective when training with fewer listeners. We experiment which only uses 10x less (around 400) listener positions per emitter. Our method is also ef- fective in this case with fewer listeners and im- proves baseline performance shown in the
AV-NeRF W/ELE
2.68
11.9
11.9
3.75
88.8
2.44
1.14
7.49
12.2
3.45
80.8
2.21
AcoustiX,
AVR
1.21
9.8
12.8
4.48
109.5
2.98
0.98
7.8
12.4
3.95
85.6
2.43
0.78
6.2
11.0
3.21
71.3
1.83
Table 8: Results RAF-Fumiished (4 emitters).
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Variant
STFF
Amp.
Env.
T66
C50
EDT
INRAS (5k)
12.3
STFT
Amp.
Ens.
T60
C50
EDT
less
1.72
3.8
19.5
3,4
25.9
10x less w/ ELE
1,5
0.43
3,3
16.5
2.6
17.7
NN Linear
2.6
0.83
11.4
26.1
4.4
52.8
2.5
0.84
13.4
23.6
4.2
47.7
NAF (5K)
4.7
0.69
3.7
14.1
2.7
23.3
17.6
NAF
0.79
10.4
23.7
3.6
ELE
0.67
16.5
2.5
ELE
0.54
17.9
2.3
AV-NoRF (5k)
1.7
0.85
4.6
11.0
5.0
44.5
INRAS
2.5
0,98
9.8
26.5
3.8
47.7
10x less
1.33
17.5
6.2
53.7
w/ ELE
0.76
18.9
2.3
31.6
AVR
0.82
11.1
22.9
2.9
45.4
AVR (5k)
1.8
0.4
3.1
14.3
2.1
15.9
w/ FLE
1.7
0.57
7.3
16.8
2.1
27.9
10x less
1.9
0.55
3.3
15.1
2.6
16.9
10s less w/ ELE
0.24
12.6
2.0
14.3
GWA
(a) Versa also effective with 10x fewer listeners.
Table 9: Quantitative comparison across datasets, (a) Fewer listener positions; (b) GWA dataset.
Tab 9a
With Versa, each model can predict
much better results when having 10X fewer listener samples, compared with the vanilla method with 10X more samples.
More results on GWA dataset. Furthermore, we also experiment on the GWA dataset [56] that simulates impulse response with hybrid ray-based and wave-based method. Each room contains
only 5 emitter positions, and there are 100 listener positions emitter. We demonstrate that Versa still consistently improves the performance as shown Tab. 9b
B.2 Discussion on the permutation invariant structure.
Versa is grounded in the physical principle of acoustic reciprocity. Based on the reciprocity principle, it is possible to design permutation invarrian neural networks like shared encoders for emitter and listener. Such architecture enforces symmetry unconditionally, regardless of whether impulse response remains the same with direct swapping. However, emitters and listeners can have different directional gain patterns, which breaks the impulse response invariance. Enforcing strict symmetry in such cases would mislead the model to make incorrect assumptions.
We provide results on AcoustiX-Diff with a permutation-invarian version of NAF, As shown in the Tab. 10 this structure can improve the performance on the original NAF under the sparse emitters settings. As this design treats the emitter/listenee exactly the same, it can alleviate the problem of sparse emitters. However, the permutation inviriiantn design strictly forces the model to output symmetric results and does not consider the unique features of the emitter listener including different gain patterns. In contrast, vanilla neural acoustic models that encode emitter/lstener features separately are not strictly constrained to this inappropriate symmetry and can benefit from the Versa- ELE for better performance. Finally, AVR with Versa-SSI achieves the best overall performance as correctly handles the different gain patterns.
Variant
STFT
Amp.
Env.
T60
C50
EDT
NAF
6.51
0.68
4.1
45.6
5.49
42.3
NAF + permutation
4.47
0.62
4.0
25.9
3,90
37.9
NAF + Versa-ELE
4.24
0.58
3.9
15.1
30.9
AVR + Versa-SLL
1.32
0.26
3.1
16.8
1.85
22.4
Table 10: Comparison the permutation invariant structure.
B.3 Perceptual Study
Study setup. We render eight audio-video clips using AVR and various Versa variants. Each clip includes three impulse responses: reference (simulated with AcoustiX), baseline (either vanilla or Versa-ELF)) and an improved method (either Versa-FLE or Versa-SSI.). This setup allows us to compare the vanilla method versus Versa-ELE and Versa-ELE versus Versa-SSL. We convolye each
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impulse response with dry sounds and pair it with microphone videos and trajectory animations. Participants first watch and listen to the reference clip, then evaluate the baseline and improved clips on three criteria:
Volume accuracy: Which clip conveys changes in volume more accurately?
Directional accuracy: Which audio made easier for you identify the direction of the sound source more accurately?
Overall similarity: Which clip aligns better with the reference overall?
We provide a snapshot of our user study interfaces in Fig. 10
TA
soedie cnweyed
scarnsely
Betweer Video and Wideo which one caudio wender for de aeriity the of sound urce more acoursslyy
Video
Video
Secoser Wileo and Wiee which ona caudi fedl geobetter with the tewero owerdll
Vikce
Video
Figure 10: The interface for the user study evaluations.
Result. We present the user study results in Tab. 6. averaging participants' choices across all audio clips (in total 15 participants). 90% of responses indicate that Versa-ELE better aligns with the reference impulse response terms of volume, directional accuracy. and overall similarity. Additionally, over 85% of responses favor Versa-SSL over Versa-ELE well. With these user study results, we demonstrate that our method can greatly improve the spatial audio listening experience, including sound direction and loudness.
Perceptual metric. We computed the Deep Perceptual Audio Metric (DPAM) on the audio clips rendered for user study. As shown in Tab [I] Versa-ELE reduces DPAM error by 20%, and Versa-SSL further reduces error by 36% compared with Versa-ELE on the AVR method. Versa-ELE also boosts the performance of other methods by 18%. These show method improve the perceptual similarity to ground truth. These results are consistent with the user study findings and reinforce the perceptual benefit of our method in the resounding task.
Method
DPAM
NAF+ Versa-ELE
1.53 -> 1.26
INRAS + Versa-ELL
1.42 >> 1.19
AV-NeRF+ + Versa-ELE
1.47 >> 1.15
AVR + Versa-ELE
1.44 -> 1.17 1.44 > 0.86
AVR Versa-SSL
Table 11: Results of the perceptual metric.
B.4 Details about demo videos
We render video/audio clip named demo.mp4 attached to the complementary file. We render scene 3 with three different settings.
Setting-l: emitter is directional and rotating
Setting-2: emitter is at novel position and omnidirectional
Setting-3: emitter is moving and directional
We demonstrate the effectiveness of Versa-SSL in setting-1 and Versa-ELE in setting-2 and setting-3.
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C
Social Impact
Our resounding framework promises greatly enhance immersion and presence in AR/VR appli- cations by delivering physically accurate, dynamic acoustics even when sound sources move freely through virtual spaces. However, the same technology could be misused to generate deceptive or manipulative audio-visual scenarios with "fake" acoustic environments that mislead users about their surroundings, deep-fake conversations where background sounds betray false contexts. To mitigate this, potential way is to inject an inaudible watermark into the audio to detect whether is being used for malicious purposes.
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26


Reviews:
[{"review_id": "WSWd60FkFK", "reviewer": "Reviewer_9Edj", "summary": "This paper introduces the task called \"resounding\", which consists in regenerating a sound field for a new emitter position, based on recordings of a sparse set of emitter locations. Although this is a particular case of the well-studied task of room-impulse-response/Acoustic-transfer-function interpolation, it has been relatively less studied than interpolating between different *receiver positions*. One difficulty is that available datasets generally have less emitter positions available. To counter this, the authors propose a simple trick: use the well-known reciprocity of the wave equation to perform data augmentation by swapping the roles of the emitter and receiver. While this is straightforward in the case of omnidirectionnel emitters and receivers, the authors tackle the trickier case of directive devices by including a joint emitter-source response as an input of the neural acoustic field, and train it to learn propagation path responses that are invariant to swapping both angles of arrival/departure *and* emitter/receiver positions, via self-supervised learning. The authors show that this trick enables a boost in performance for a number of existing state-of-the-art neural acoustic field architecture.", "questions": "- My main interrogation is as written above: is this really beneficial compared to simple making the neural acoustic field invariant to permutation of the relevant input?\n\n- Eq. (8): Is G really fed twice to the neural acoustic field F? Why? Please clarify to avoid confusion.\n\n- L200: \"As a result, Versa-SSL does not generate new valid training samples with ground-truth impulse response as Versa-ELE.\" something seems wrong ith this sentence, I don't understand.\n\n- I am aware of the following work that also utilizes the reciprocity principle in the context of interpolating acoustic transfer functions (the submitted paper cite other works from these authors related to PINNs, but I am not sure those use reciprocity):  Ribeiro, J. G., Koyama, S., & Saruwatari, H. (2023). Kernel interpolation of acoustic transfer functions with adaptive kernel for directed and residual reverberations. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 1-5). IEEE.\n\n**Typos/suggestions**\n- L96: extra space before coma\n- L118: I would suggest to remove \"for simplicity\" here, which triggers confusion, and write instead \"We consider first....\", to make clear that the multipath case will be treated just after.", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "**Strengths**\n- The paper is well presented and motivated\n- Although the idea is simple, it is well formalized and executed, in particular for the case of directive devices\n- The approach offers performance boosts to a number of existing methods\n- The studied task is relevant\n\n**Weaknesses**\n- At least for the case of omnidirectional devices, I have a hard time believing that the data-augmentation trick of swapping source and receiver based on reciprocity has not been proposed before, since this is a very well known property. This makes me question the originality, although to be honest I can't pin point a reference for this. And the fact that the more interesting case of directive devices is considered somewhat counter-balances this.\n- It is relatively easy to make any neural network architecture independent of permutation of two input, for example by passing each input through the same layers, and then sum the resulting embeddings. How would such an approach compare to the proposed data augmentation strategy applied to a generic architecture? This makes me question the significance of the approach", "was_revised": true, "final_justification": "I thank the authors for their response which helped clearing my doubts on originality and significance. I am consequently improving my rating from 4 to 5.", "rebuttal": "We thank Reviewer 9Edj for the careful review and constructive suggestions. The constructive feedback has significantly contributed to the improvement of our paper. We are glad that the reviewer found our work “well presented and motivated” and “well formalized and executed”, giving us a positive rating. We address your questions and concerns as follows.\n\n---\n> **Review W1: Concern about the paper's originality on the reciprocity theory.**\n\nWe appreciate the reviewer’s point. While reciprocity is a fundamental property in the wave propagation theory. To our knowledge, no prior work has explicitly applied emitter/listener swapping as a data augmentation strategy for training neural acoustic field models. Versa-ELE is effective and novel in its systematic application and broad compatibility across models to solve the resounding task. Moreover, our key contribution lies in extending beyond the same gain pattern case with Versa-SSL, which handles directional asymmetries through a physically grounded self-supervision learning. We will clarify this distinction in the revised manuscript.\n\n---\n> **Review W2, Q1: Question on the permutation invariant structure.**\n\nOur method is grounded in the physical property of acoustic reciprocity. The impulse responses remain unchanged when emitter and listener positions are swapped, but only under certain conditions, such as omnidirectional gain patterns. This condition is important when evaluating whether architectural permutation invariance is an appropriate solution.\n\n1. **Discussion on permutation invariance architecture:** Based on the reciprocity principle, it is possible to design permutation-invariant neural networks (e.g., with shared encoders for emitter/listener poses), such architectures enforce symmetry unconditionally, regardless of whether impulse response remains the same with direct swapping. However, emitters and listeners can have different directional gain patterns, which breaks the impulse response invariance (discussed in the sec3.3). Enforcing symmetry in such cases can encourage the model to make incorrect assumptions. This is also the motivation for the development of our proposed Versa-SSL in the case of different emitter/listener gain patterns. Versa-SSL shows improved performance in these cases that simple permutation invariant networks cannot achieve. \n\n2. **The advantage of Versa**: Versa-ELE explicitly leverages reciprocity when it holds by generating physically valid virtual samples through pose exchange. This strategy is effective, model-agnostic, and compatible with any architecture, which does not require changing the model architecture. Furthermore, Versa-SSL generalizes this idea to asymmetric gain patterns by introducing self-supervised consistency constraints, without assuming symmetry. \n\n3. **Additional Experiment results**: We provide experimental results (on AcoustiX-Diff with different gain patterns) on a permutation-invariant version of NAF. As shown in the table, permutation-invariant structure can improve the performance on the original NAF model under the sparse emitters settings. As this design treats the emitter/listener exactly the same, it can alleviate the problem of sparse emitters. However, the permutation-invariant design strictly forces the model to output symmetric results and does not consider the unique features of the emitter or listener, i.e. different gain patterns. In contrast, vanilla neural acoustic models that encode emitter/listener features separately are not strictly constrained to this inappropriate symmetry and can benefit from the Versa-ELE for better performance. Finally, AVR with Versa-SSL achieves the best overall performance as it correctly handles the different gain patterns.\n\n\n|                   | STFT |  Amp | Env |  T60 |  C50  |  EDT |\n|-------------------|:----:|:----:|:---:|:----:|:-----:|:----:|\n|        NAF        | 6.51 | 0.68 | 4.1 | 45.6 |  5.49 | 42.3 |\n| NAF + permutation | 4.47 | 0.62 | 4.0 | 25.9 |  3.90 | 37.9 |\n|  NAF + Versa-ELE  | 4.24 | 0.58 | 3.9 | 15.1 |  3.67 | 30.9 |\n|  AVR + Versa-SSL  | 1.32 | 0.26 | 3.1 | 16.8 | 1.852 | 22.4 |\n\n\n---\n> **Review Q2: Confusion on equation 8: $F(P_l, P_e, w_l, w_e, G, G) = F(P_e, P_l, w_e, w_l, G, G)$**\n\n\nG is used as both the emitter gain pattern and the listener gain pattern, thus appearing twice in the equation. In the implementation, the emitter gain pattern $G_e$ is implicitly encoded in the neural acoustic field, and it is not available to be changed. Thus, we can only align the listener pattern with $G_e$, forcing the acoustic volume rendering step of AVR to use the same listener gain pattern as the implicit emitter pattern. As a result, we only alter the microphone pattern (we replace the microphone pattern with $G_e$ during the acoustic volume rendering) in the self-supervision stage. \n\n---\n> **Reivew Q3: Clarification on the L200: As a result, Versa-SSL does not generate new valid training samples with ground-truth impulse response as Versa-ELE.**\n\nSorry for the confusion in this sentence. For Versa-ELE, it is implemented as a data augmentation method to generate valid impulse response training samples. For Versa-SSL, it forces the neural acoustic field to output consistently after exchanging the emitter/listener poses in a self-supervised way. \n\n---\n> **Review Q4: Discussion of the relevant paper.**\n\nWe thank the reviewer for pointing out the work by Ribeiro et al. (ICASSP 2023). While that paper discusses reciprocity in the context of kernel interpolation and physics-informed modeling, our approach differs in both scope and methodology. No prior work has leveraged reciprocity as a systematic training strategy for the neural acoustic field. Our contribution lies in using reciprocity to improve model generalization in sparse emitter settings, and in extending this idea to asymmetric scenarios through Versa-SSL. We will update the related work section to better clarify this distinction.\n\n---\n> **Typos/Suggestions.**\n\nThank you for your detailed suggestions on the writing and text. We will incorporate them for the revised version after rebuttal."}, {"review_id": "TIWkvKfNnI", "reviewer": "Reviewer_eBxj", "summary": "The proposed work aims to estimate the acoustic field of scenes that are dynamic such that there are sparse multiple emitters in the scene and these could be relocated. To address this, the authors propose a method based on the principle of wave reciprocity Emitter Listener Exchange (Versa-ELE) which allows for swapping the roles of emitters and listeners if the path between them remains unchanged, i.e. the gain pattern between them is unchanged when the direction is being swapped. To further extend the method to cases where emitters and receivers have different directional characteristics, the authors propose Versa-SSL, a self-supervised learning strategy that encourages consistent predictions between swapped roles in conjunction with Acoustic Volume Rendering (AVR). Evaluations on simulated dataset of AcoustiX-Diff and real datasets (Mesh-RIR and RAF) indicate that incorporating ELE augmentation or SSL training with AVR  improves performance measured with multiple acoustic metrics.", "questions": "I would appreciate authors’ response to the following questions:\n\nQ1. I was not able to find the expression of the loss used for SSL (i.e. L_{a-ssl}). In general, the SSL paradigm is not fully clear. For example I am not sure if negative pairs are used as contrastive loss and how these are generated.\n\nQ2. Deep Perceptual Audio Metric (DPAM) is a useful metric calibrated with human audio perception (R6). How would Versa-ELE and -SSL perform for this metric?\n\nQ3. Related to W1-a, how would Versa-ELE and -SSL compare with BEE and can ELE approach be implemented in conjunction with BEE?\n\nR6. Manocha, P., Finkelstein, A., Zhang, R., Bryan, N. J., Mysore, G. J., & Jin, Z. (2020). A differentiable perceptual audio metric learned from just noticeable differences. arXiv preprint arXiv:2001.04460.", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\nS1. The paper addresses an important problem of dynamic scenes acoustic field reconstruction, which many approaches in acoustic field research are unable to fully address at the onset. \n\nS2. The proposed ELE approach makes use of wave propagation reversibility and proposes an augmentation approach to switch the roles of emitters and listeners, requiring that the paths from emitters to listeners and backward will be preserved. Such augmentation can be combined with existing approaches for acoustic field reconstruction.\n\nS3. The work proposes a self learning (SSL) approach for further precision / realistic cases. where the acoustic field prediction is constrained to be identical under switching the role of the emitter and listener creating positive pairs for SSL training.\n\nS4. Results with several acoustic field reconstructions baselines show improvement in multiple metrics.\n\nWeaknesses:\n\nW1. The related work is not up to date and in general I found the related work section to be too scarce. In particular, two major concerns\n\nThe authors omitted work that addressed dynamic acoustic field reconstruction. e.g. R1 where acoustic field reconstruction for such scenes was considered with the use of sparse stationary receivers placed in the scenes, making the paths from dynamic emitters to listeners to be emitters -> receivers-> listeners. \n\nSeveral recent acoustic field approaches were not included in related work and results, e.g. INRAS++ (R2), AVCloud (R3), NeRAF (R4), etc.\n\nR1. Chen, M., Su, K., & Shlizerman, E. (2023). Be everywhere-hear everything (bee): Audio scene reconstruction by sparse audio-visual samples. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 7853-7862).\n\nR2. Chen, Z., Gebru, I. D., Richardt, C., Kumar, A., Laney, W., Owens, A., & Richard, A. (2024). Real acoustic fields: An audio-visual room acoustics dataset and benchmark. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 21886-21896).\n\nR3. Chen, M., & Shlizerman, E. (2024). AV-Cloud: Spatial Audio Rendering Through Audio-Visual Cloud Splatting. Advances in Neural Information Processing Systems, 37, 141021-141044.\n\nR4. Brunetto, A., Hornauer, S., & Moutarde, F. (2024). NeRAF: 3D Scene Infused Neural Radiance and Acoustic Fields. arXiv preprint arXiv:2405.18213.\n\nW2. The results were not applied to same datasets as the baselines, i.e. SoundSpaces, on Replica, Matterport3D, and on a moderate number of scenes.", "was_revised": true, "final_justification": "The work proposes a new reciprocity-inspired learning approach for acoustic field estimation of a scene. In particular, the proposed loss (reciprocity contrastive loss) can be combined with various acoustic field estimation methods and enhance their accuracy. Experiments that the authors conducted in the manuscript and additional results on effectiveness of the reciprocal contrastive loss that the authors stated in the rebuttal and discussion of generalization, indicate an effective and useful method for improving acoustic field estimation and serve as a foundation for further extensions. I am supportive of publication of the work, given that the authors incorporate the revisions presented in the rebuttal in the revised version of the paper, and raising my score for this paper to 4.", "rebuttal": "We thank Reviewer eBxj for the thoughtful review. The constructive feedback has significantly contributed to the improvement of our paper. We appreciate the reviewer for recognizing that our work deals with a problem that prior methods “are unable to fully address at the onset”, “can be combined with existing approaches”, and “show improvement in multiple metrics”. We address your questions and concerns one by one as follows.\n\n---\n> **Review W1 part1. Discussion on the related paper BEE.**\n\nWe thank the reviewer for pointing out this relevant line of work on dynamic acoustic field reconstruction. We agree that BEE[R1] is an important contribution in the broader space of learned acoustic field modeling. While we did not include this work in our original related work section, we will add it in the revised manuscript and clarify how our setting differs as follows. \n\n1. **Training Setup**: BEE trains on a large-scale, multi-scene corpus with dense emitter-listener pair sampling across a variety of environments. It also leverages audio-visual correspondence as a strong learning signal. After training on the large-scale dataset, it can leverage the sparse listener samples in the novel scene to reconstruct the acoustic field. In contrast, our method focuses on per-scene optimization, only trained on a sparse set of emitter positions, and does not consider cross-scene generalization. Our goal is to reconstruct a physically consistent acoustic field within a single environment, given limited emitter setups.\n\n2. **Task definition and output**: BEE targets novel scene generalization, which involves predicting audio waveforms or spectrograms for novel listener positions in the environment given sparse receiver samples. In contrast, our work tackles the resounding task in the same acoustic scene, which focuses on estimating the impulse response at an arbitrary emitter/listener location within a scene. This allows for more flexible downstream rendering.\n\nWe will revise the related work section to include and discuss our differentiation from BEE and similar approaches. \n\n---\n> **Review W1 part2: Discussion on the related paper AVCloud, NeRAF, INRAS++**\n\nThank you also for pointing out recent advances in acoustic field modeling. We now include two other open-source papers, including AV-Cloud (R3) and NeRAF (R4), as additional baselines in our experiments. Both the AV-Cloud and NeRAF leverage 3D visual information for acoustic prediction.\n\nWe experimented with the AcoustiX-Same dataset and applied our Versa-ELE method on top of AV-Cloud and NeRAF. We find that Versa-ELE improves both of their performance by a large amount, further supporting the generality of our approach to various acoustic field approaches. \n\nThe INRAS in our baseline model is already implemented as INRAS++ (R2), extending the 3d grid sampling and extra loss components. The other baseline NAF and AVNeRF implementations also follow the RAF paper (R2). \n\nWe will cite all of these works (R1–R4) and clarify the baseline settings in the revised version, and expand our related work section to better reflect recent progress in the field. \n\n|                   | STFT |  Amp | Env |  T60  |  C50 |  EDT |\n|-------------------|:----:|:----:|:---:|:-----:|:----:|:----:|\n|       NeRAF       | 2.08 | 0.50 | 4.6 |   20.5  |  4.54 | 33.2 |\n| NeRAF + Versa-ELE | 1.82 | 0.35 | 4.0 |  16.9 | 3.08 | 23.4 |\n|      AVCloud      | 1.87 | 0.48 | 3.7 |  16.6 | 3.66 | 32.3 |\n| AVCloud+Versa-ELE | 1.26 | 0.32 | 2.7 |  13.2 | 2.07 | 15.8 |\n|        AVR        | 2.34 | 0.51 | 3.2 | 21.0 | 2.82 | 31.9 |\n|     AVR + ELE     | 1.20 | 0.25 | 2.1 |  13.5 | 1.75 | 15.1 |\n\n---\n> **Review W2 part1: Evaluation dataset, “Not applied to the same dataset as the baselines, like SoundSpaces”.**\n\nThank you for raising the dataset issue. For the SoundSpaces dataset, we also noted some time-of-flight errors on the simulated impulse response as prior paper (AVR), which makes it not ideal to test on some models like DiffRIR and AVR. As a result, we use other simulation engines (AcoustiX and GWA) and datasets, plus real datasets (MeshRIR and RAF), with diverse settings to evaluate the model’s performance.\n\n---\n> **Review W2 part2: Concern on the number of evaluation scenes.**\n\nAs pointed out by the Reviewer sQL3 in the strengths section, we used various simulated datasets, including ray-based, wave and ray hybrid simulation datasets, and also real datasets to test our method. Our evaluation includes various simulated scenes (3 for AcoustiX, 3 for GWA) and real scenes (MeshRIR, 2 RAF scenes), with a total of 9 scenes. Our experiment also involves various setups for gain patterns, resulting in more than 15 different scene+gain pattern combinations for evaluations. We also incorporate a diverse number of emitters/listeners in each scene to evaluate the robustness of our method. Through these diverse evaluations, we have verified the effectiveness of our Versa framework in various models that purely rely on neural acoustic feature (NAF), scene geometries (INRAS), acoustic propagation phenomenon (AVR), visual-acoustic correspondence (AV-NeRF, AVCloud, NeRAF).\n\n---\n> **Review Q1: Confusion on the self-supervision loss.**\n\nWe do not involve negative pairs for self-supervised learning as common contrastive loss. For our self-supervision loss, let $h_1 = F(P_l, P_e, w_l, w_e, G, G)$ and $h_2 = F(P_e, P_l, w_e, w_l, G, G)$. The SSL loss $L_{a-ssl}$ only forces $h_1$ and $h_2$ to be identical. For the simplest version, we can directly optimize on the L1 loss between the waveform $h_1$ and $h_2$, i.e., $L_{a-ssl} = |h_1 - h_2|$. In our implementation, we follow a similar strategy to calculate the L1 distance between $h_1$ and $h_2$ in time and spectrogram domain as in the previous framework.\n\n---\n> **Review Q2: Include DPAM metric for evaluations.**\n\nThank you for your suggestion! To evaluate perceptual quality, we computed the Deep Perceptual Audio Metric (DPAM)  on the audio clips rendered for user study and demo. These clips were generated by convolving predicted RIRs with dry sound and comparing them to ground-truth renderings.\n\nVersa-ELE reduces DPAM error by 20%, and Versa-SSL further reduces error by 36% compared with Versa-ELE on the AVR method. Versa-ELE also boosts the performance of other methods by 18%.  These show our method can improve the perceptual similarity to ground truth. These results are consistent with the user study findings and reinforce the perceptual benefit of our method in the resounding task. We will include this new metric and its analysis in the revised version.\n\n|        Method       |  DPAM score  |\n|:-------------------:|:------------:|\n|   NAF + Versa-ELE   | 1.53 -> 1.26 |\n|  INRAS + Versa-ELE  | 1.42 -> 1.19 |\n| AV-NeRF + Versa-ELE | 1.47 -> 1.15 |\n|   AVR + Versa-ELE   | 1.44 -> 1.17 |\n|   AVR + Versa-SSL   | 1.44 -> 0.86 |\n\n---\n> **Review Q3: Evaluation of Versa on BEE.**\n\nWe thank the reviewer for the follow-up question regarding the comparison to BEE and the potential for combining Versa-ELE with that framework. We address this question as follows:\n\n1. **Different Settings and Objectives**: Echoing our response to W1-part1, BEE and our work target distinct problem settings. BEE is designed for cross-scene generalization: it is trained on a large-scale, multi-scene dataset with dense emitter-listener pair sampling, and it leverages audio-visual correspondence to model the acoustic field. After large-scale training, it can be adapted to novel scenes using sparse listener observations. Versa is specifically designed to improve generalization of neural acoustic fields within a single scene, leveraging reciprocity as a physics prior for improved modeling of wave propagation. We also contain only a few emitter locations training dataset, instead of densely sampled emitter-listener pairs in lots of scenes as in BEE. \n\n2. **Evaluation on other related models**: While we do not compare directly to BEE, we evaluate Versa on other recent open-source neural acoustic field methods that also use audio-visual correspondence, including AVCloud and NeRAF/AV-NeRF. These methods operate within a single scene and use visual priors for field reconstruction.  Our results demonstrate that Versa is compatible with such architectures and consistently improves performance."}, {"review_id": "r3KzljU3P7", "reviewer": "Reviewer_tDBR", "summary": "The paper presents Versa, a physics-inspired approach to learning acoustic fields, and shows how this approach can be used to improve on existing methods for \"resounding\" tasks analogous to \"relighting\" in computer graphics.", "questions": "I watched and listened to the demo in the supplementary materials (the video was labeled \"CVPR_25\" – I suggest relabeling it ;)) which was helpful, but as a non-expert, I didn't know what to listen for to judge the quality and really understand the comparison with baselines.  Having some helpful pointers (listen for this, compare this) woud help the NeurIPS community better appreciate the value of this paper. \n\nAll the demos involved music, and all music in a pretty limited span of pop genres.  How does the approach generalize to other kinds of music, and other kinds of sounds.  What about speech? \n\nHow does the approach interact with physical models one has (built or learned) for material that surfaces in the environment are made out of (e.g., wooden floor, stone floor, plaster walls, carpet, etc.).  I expect these would change the acoustics of an environment substantially?  Is there a way the approach extends to include models of or knowledge about the acoustically relevant physics of the environment? \n\nGiven the paper's limited accessibility outside of its specialty area, and limited appeal to the broader NeurIPS community, could the authors draw out a broader message or lesson of interest that readers could take home, beyond just that this seems to work pretty well?", "limitations": "yes", "rating": 4, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "I have to start off by stating that this paper is far from my expertise.  At a high level, the problem seems interesting, and relevant to a range of computer graphics and interactive (VR, game) design settings.  The combination of physical modeling sophistication with machine learning sophistication is a strength of the paper.  The improvements over baselines appear substantial, but it's hard for me to judge the quality or significance as a nonexpert.  \n\nI don't see technical weaknesses but I have low confidence in my ability to detect these given that I'm not an expert in this paper's topic.  As a NeurIPS submission, I do view it as a weakness of the paper that it has limited accessibility and appeal to the broader NeurIPS community outside of those interested in machine learning for audio representations.   I'm not sure why the paper isn't better suited for SIGGRAPH or a computational audio or acoustics venue.", "was_revised": true, "final_justification": "The rebuttal helpfully addressed my concerns and I think their proposed paper changes will make the paper stronger.  I still feel unsure whether NeurIPS is the right venue for this work so I'll maintain my score of borderline accept.  This is mostly about my own lack of familiarity with this area of AI.  The \"accept\" criterion say: \"Technically solid paper, with high impact on at least one sub-area of AI or moderate-to-high impact on more than one area of AI\".  I'm just enough of an expert to judge that this work meets those criteria.", "rebuttal": "We thank Reviewer tDBR for the constructive suggestions. The feedback has significantly contributed to the improvement of our paper to make it better accessible to the broader community. We appreciate the reviewer for recognizing that “the combination of physical modeling sophistication with machine learning sophistication is a strength”, and “the improvements over baselines appear substantial”, giving us a positive rating. We address your concerns as follows.\n\n\n---\n> **Reivew W1: Concern about the scope and accessibility.**\n\nWe understand the reviewer’s concern about accessibility to the broader NeurIPS community. While the application domain is audio and acoustics, our contribution is fundamentally about machine learning under a structured physical prior. Specifically, we propose reciprocity-grounded Versa as a learning strategy. We show the effectiveness of Versa in many existing work on audio field reconstruction that are published in the NeurIPS community (NAF, INRAS, AVR, and AV-NeRF). We also provide theoretical grounding and empirical evidence for the proposed method for learning the dynamic audio field. \n\nWe hope the broader NeurIPS audience finds value in how our method bridges wave propagation and ML training pipelines. We will highlight these broader themes and revise the introduction and discussion to explicitly call out these cross-domain insights. \n\n---\n> **Reivew Q1: Adding pointer in the demo.**\n\nThanks for the advice, we will add extra pointers to highlight the comparisons and point-by-point comparison for the audio after releasing our work on the project webpage.\n\n---\n> **Reivew Q2: Question about the rendered sound, how about the rendering speech?**\n\nWe appreciate this thoughtful question. Our method fundamentally models impulse responses, which characterizes how sound waves propagate in the environment and heard by human ears. Since the impulse responses are independent of the input dry sound, it can be convolved with any sound source like music, speech and ambient noise. As a result, the resounding capability generalizes naturally across genres and source types. \n\nWhile we used music in the demos for its perceptual richness and clarity in spatial cues, our method works equally well with speech and other sounds. We will include additional speech-based examples in the supplementary material and project website to illustrate this.\n\n---\n> **Reivew Q3: How does the approach interact with the physical model?**\n\nThe physical characteristics of surfaces like sound reflections directly influence the room’s impulse response. Integrating explicit material priors, either from semantic scene understanding or estimated material parameters is a promising direction for future work. This could further enable data efficiency by grounding the acoustic model in known physical properties. Given the material’s properties, one can use a ray tracing method to render the impulse response, where the reciprocity property is already considered.\n\nVersa is complementary to these efforts. While material-based models focus on surface-level interactions, our method emphasizes the broader wave propagation paths, enforcing physical consistency across emitter-listener pairs. It leverages reciprocity to capture the cumulative effects of symmetric path interactions along surfaces for better acoustic field reconstruction. Besides, some of our baseline models (AV-NeRF, INRAS, NeRAF) include relevant physics of the environment from vision or 3D inputs. Experiment results show that Versa improves their performance substantially as well. \n\n---\n> **Reivew Q4: Broader take home message.**\n\nWe thank the reviewer for encouraging us to better articulate the broader message. At a high level, our work demonstrates how physical priors like reciprocity can be converted into effective training strategy, especially when training samples are sparse. Within the audio domain, we show the effectiveness in many works published in the NeurIPS community. \n\nBeyond the audio domain, this principle extends beyond acoustic signal to vision (light transport), radio frequency (RF) signal and so on. Similar ideas can guide neural rendering models to enforce multi-view consistency. For RF signals, electromagnetic reciprocity holds in many indoor environments. This can also be exploited to simulate virtual transmitters/receivers and reduce measurement overhead in RF channel modeling. \n\nOur key takeaway is the potential of incorporating physical principles into the training of neural networks. This approach not only reduces the 'black box' nature of the models but also enhances their generalization capabilities, often requiring less training data."}, {"review_id": "rVdgYLY7M6", "reviewer": "Reviewer_sQL3", "summary": "This paper proposes training techniques to improve the performance of NNs for solving acoustic field learning problems under a particular situation where the source positions are sparse (which they call a resounding task).\n- The basic idea is to train the NNs with an auxiliary loss term when learning an acoustic field so that its output closely follows to satisfy Rayleigh's reciprocity theorem.\n- Two approaches are proposed: one that simply preserves the output when the source-receiver positions are switched (Versa-ELE), and one that decouples from the directivity pattern and trains to preserve the reciprocal relationship (Versa-SSL).\n- The authors show that the Versa technique improves quantitative and qualitative evaluation performance on a variety of existing studies, including NAF, INRAS, AV-NeRF, and AVR.", "questions": "- Concerns about the relationship between Versa and the reciprocity\n\n    - Versa is essentially a training technique that guides the NN output to remain similar when the source-receiver positions are switched. As the authors associate this technique with the ‘acoustic reciprocity principle’, this reviewer hopes that the paper will present results that show how this technique relates to reciprocity in practice. Just so there is no misleading the reader.\n\n        1. The reviewer believes that the Rayleigh reciprocity theorem is built upon many assumptions, such as linear time-invariance (LTI), symmetric reflection, etc., and it seems that AcoustiX (and probably GWA too) might easily meet such assumptions, but would MeshRIR and RAF also do as well?\n        2. Would there be any results on how the IRs are preserved in terms of the position swaps for various datasets?\n        3. Could you provide any samples of how well the output IRs match when the source-receivers are swapped? (Or other results that can support the claim that NNs learn reciprocity through Versa?) \n\n- Questions on the perceptual user study (L303 and Appendix B.2-3)\n    1. How were the subjects recruited for the listening evaluation?\n    2. Was IRB approval for this not required? (I see that the final checklist says N/A.)\n    3. Did the subjects have sufficient experience and background in listening and evaluating spatial acoustics?\n    4. I wonder if 15 subjects are a large enough number for a listening test to show a statistical significance.\n\n**Minor comments.**\n- The results in Table 6: the caption says it’s for RAF-Furnished, but the text says it’s RAF-EmptyRoom (L531).\n- What do you mean by mentioning “Impulse response modeling has gained lots of attention due to its application in virtual experiences” in L81?", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 2, "originality": 3, "strengths_and_weaknesses": "**Strengths.**\n1. The main strength and contribution of this paper is that the authors redefine a specific challenge where the emitter locations are sparse and propose a simple technique that can alleviate the difficulty.\n2. The authors applied their proposed technique to 4 prior studies and reported performance on 3+ datasets, showing consistent performance gains for different configurations.\n3. It is worth noting that the datasets studied in the manuscript (including the appendix) cover diverse types of acoustic observations such as ray-based simulation, ray-and wave-based hybrid simulation, and real recording measurements.\n\n**Weaknesses.**\n1. In order for this study to be more convincing, it needs to be shown how the situation the authors envision — sparse emitter positions — is indeed a major challenge.\n    - Given the IR obtained from computer simulations such as AcoustiX or SoundSpaces, it seems unlikely that emitter positions would have to be sparse, so I don't see much utility in the resounding task, and by extension Versa.\n    - In a real-world setting where IR is measured, being able to predict the acoustic field with the least source positions is an advantage, but then, does reciprocity (or something to support Versa's validity to be associated with a reciprocity) hold in all real recordings covered in this study?\n2. This reviewer is concerned that the method proposed in this paper is not sufficiently presented to be called reciprocity in practice.\n    - The reciprocity principle is a fundamental concept, but it involves many assumptions.\n    - It does not appear to be sufficiently demonstrated how well the datasets covered in this study meet those assumptions, and therefore how valid it is to apply this approach.\n\n    (Please refer to the Question section below for some questions regarding this connection.)", "was_revised": true, "final_justification": "I appreciate the authors' detailed responses and their willingness to improve the manuscript with clearer statements. \nConsidering the authors' explanation that Versa aims to tackle the real-world scenario, my concern was then any demonstrations or references seem to be necessary that can support the feasibility of this approach, which assumes reciprocity as an underlying principle in the measured wave field. The authors expanded on this and showed their intention to avoid potential misunderstandings and overclaims. The amount of impact that this paper can bring to the NeurIPS community is considered moderate, as it is essentially a simple technique based on very well-known principles (both the reciprocity itself and the training technique). For these reasons, I am adjusting my score from 3 to 4.", "rebuttal": "We thank Reviewer sQL3 for the careful review. We appreciate the reviewer for recognizing that our method shows “consistent performance gains for different configurations”, and our used datasets “cover diverse types of acoustic observations”. We address your concerns as follows. \n\n---\n> **Review W1 part1: Question on Versa on Simulated dataset.**\n\nIn the simulated environment, one can freely simulate the IRs at any position. The reason why we incorporate the simulated dataset in this paper is to demonstrate the effectiveness of our method in various dataset settings. We answer the rest of the questions (W1 part2, W2 and Q1) about whether reciprocity holds in the real world environment as follows. \n\n---\n> **Review Q1(1), W2, W1 part2: Whether requirements for reciprocity are met in the real dataset?**\n\nThe Rayleigh reciprocity theorem assumes linear, time-invariant (LTI) systems and symmetric propagation media. In a simulated environment, these could be easily satisfied through ray-based or wave-based methods. In real environment, most room acoustic environments used for impulse response measurements (e.g., MeshRIR, RAF) meet these criteria:\n1. The environment is static during measurement and there is no moving object or human in the scene, making it time-invariant. (As also noted in the dataset collection procedure in the MeshRIR and RAF paper)\n2. All common materials and structures (e.g., drywall, wood, concrete, furniture, carpeted floors) exhibit symmetric acoustic reflection behaviors. Only specially-engineered materials like metamaterials with time-varying impedance, active acoustic diodes, or isolators exhibit non-symmetric reflections. However, these materials are mostly found only in laboratories or engineered acoustic environments, and are not presented in daily life, especially in room acoustic measurement scenes. \n\nBased on the above analysis, real datasets and real-life scenarios would meet the requirements for reciprocity. We also show some RIR measurements in real-world datasets to prove reciprocity in the following answers. \n\n---\n> **Review Q1(2), W2, W1 part2: Preservation of IRs after position swaps in real dataset.**\n\nIt is easy to verify the reciprocity in the simulated dataset. For this question, we focus on proving reciprocity on the real dataset.\n\n**Discussion on MeshRIR and RAF:**\n\nMeshRIR has different areas for emitter/listener positions, and thus does not include RIR pairs that have the same emitter/listener positions after swapping. RAF has significantly different emitter and receiver gain patterns, nor do they have listener orientation measurements. Thus, there isn’t any RIR pair that have the same emitter/listener positions and orientations after swapping. As a result, we could not empirically evaluate the impulse response invariance for these two datasets based on their current data points. However, experimental results in our original paper show consistent improvements of Versa on them, indicating reciprocity constraints do hold for them. \n\n**Discussion on our collected real dataset as a proof:**\n\nThough we can not get the pairs of IRs after switching the position of the listener/emitter in the MeshRIR and RAF dataset (these samples are not included in the dataset), we collect our own real dataset as proof.\n\n**Dataset collection procedure:** We conducted an experiment in 3 various indoor environments (an office room(9m x 8m, a conference room (13m x 7m), a kitchen (5m x 9m)) using a microphone (ADMP401 MEMS microphone) and a speaker (Bose SoundLink Revolve II speaker) that are both omnidirectional. They are wired connected to an audio interface (BELA board), which can synchronize between the playing and recording. We follow the data collection in DiffRIR and play a 10-second chirp signal that spans 50Hz to 20KHz to collect RIR samples. We collected the impulse responses emitted from position A to B and from position B to A. Under the reciprocity principle, these pairs of impulse responses should be identical. Positions A and B are randomly chosen to represent the diverse acoustic fingerprint in an environment. We collect 20 such RIR pairs in each environment, placing them in a diverse combination of speaker and microphone positions.  \n\n**Results:** We measure the similarities within pairs of RIRs that have swapped emitter/listener positions and the cross-paired RIRs. As shown in the table, in each of the scenes, the paired RIRs have much lower acoustic metrics (some metrics are x10 smaller) compared with cross-paired RIRs. This confirms that the RIRs are preserved in terms of the position swaps for various real-world environments. \n\n|                               |  Amp |  Env |  T60 |  C50 |  EDT  |\n|-------------------------------|:----:|:----:|:----:|:----:|:-----:|\n|     Kitchen cross-paired RIRs     | 1.74 | 12.0 | 11.5 | 3.69 |  89.1 |\n|          Kitchen paired RIRs         | 0.24 |  1.8 |  2.1 | 0.29 |  9.8  |\n| Conference room cross-paired RIRs | 1.09 |  7.5 | 18.7 | 3.35 |  120  |\n|  Conference room paired RIRs  | 0.22 |  1.0 |  3.3 | 0.23 |  11.9 |\n|   Office room cross-paired RIRs   | 1.54 | 16.5 | 24.5 | 2.49 | 104.5 |\n|    Office room paired RIRs    | 0.23 |  2.0 |  2.2 | 0.18 |  11.0 |\n\nWe will also incorporate these supplementary results in the revised version to show the reciprocity for the real-world environment.\n\n---\n> **Review Q1(3), W2, W1 part2: Provide evidence of NN have learn reciprocity with Versa.**\n\nWe acknowledge the importance of validating whether the neural acoustic field models trained with Versa indeed exhibit reciprocity. We included a similar experiment in Q1(2). We randomly query the trained AVR and NAF models on swapped emitter/listener pairs at each scene and evaluate the predicted impulse response similarity. As shown in the table, the predicted RIRs pairs with Versa training exhibit much lower acoustic metrics (showing higher similarities for paired predictions after position swap) compared with the neural acoustic field without the Versa training. These show that the NN models learn reciprocity through the Versa strategy. In addition, we promise to include more waveform visualizations of the model-predicted RIRs in the final manuscript. \n\n|                               |  Amp |  Env |  T60 |  C50 |  EDT |\n|-------------------------------|:----:|:----:|:----:|:----:|:----:|\n| AVR w/o Versa-ELE | 0.54 | 10.6 | 16.6 | 4.13 | 86.9 |\n|  AVR w/ Versa-ELE  | 0.06 |  0.5 |  4.8 | 0.61 | 10.8 |\n|       NAF w/o Versa-ELE       | 0.33 |  5.9 | 13.5 | 2.83 | 50.4 |\n|        NAF w/ Versa-ELE       | 0.27 |  3.0 |  5.4 | 0.45 |  8.4 |\n\n\n---\n> **Review Q2: Questions on the perceptual user study.**\n\n- **IRB and Recruitment:** We have consulted with our host institution's IRB office regarding the ethical requirements of our audio listening evaluation. Based on the nature of the study that involves only anonymous adult participants, no collection of personal or identifiable data, and no procedures involving risk or discomfort,  the study was formally deemed exempt from IRB review and granted a waiver with “non-human subject determination”. We will clarify this waiver status in the revised paper to avoid confusion.\n\n- **Background:**  Though the participants were not expert acousticians, we ensured they had sufficient familiarity with spatial audio experiences. We included a brief session before the evaluation to inform them about the process of the audio evaluations.\n\n- **Sample size:** Recent related works like [R1] use 12 users for their perceptual study. We understand your concern and further extend our user study from 15 users to 26 users with an updated result shown below. Our method still demonstrates superior perceptual preference across all aspects. \n\n|  | Versa-ELE vs Vanilla | Versa-SSL vs ELE |\n|:-----------:|:--------------------:|:----------------:|\n|    Volume   |  88.5% | 86.5%  |\n| Directional |  88.5% | 88.5%  |\n|   Overall   | 92.3%  | 80.8% |\n\n\n- **Additional evaluations:** We also incorporate an objective, reference-based audio similarity metric Deep Perceptual Audio Metric (DPAM) as suggested by reviewer eBxj. This metric can evaluate the audio similarity close to human perception. We evaluate the similarities between the rendered sound (IR convolve with music) to the ground truth. As shown in the table, Versa-ELE reduces DPAM error by 20%, and Versa-SSL further reduces error by 36% compared with Versa-ELE on the AVR method. Versa-ELE also boosts the performance of other methods by 18%.  These show our method has stronger perceptual similarity to ground truth. These results are consistent with the user study findings and reinforce the perceptual benefit of our method in the resounding task. \n\n| Method | DPAM score |\n|:-------:|:------------:|\n|   NAF + Versa-ELE   | 1.53 -> 1.26 |\n|  INRAS + Versa-ELE  | 1.42 -> 1.19 |\n| AV-NeRF + Versa-ELE | 1.47 -> 1.15 |\n|   AVR + Versa-ELE   | 1.44 -> 1.17 |\n|   AVR + Versa-SSL   | 1.44 -> 0.86 |\n\nWe will revise the manuscript to clarify the study design, IRB waiver and add the new evaluation metric to enhance the robustness of our perceptual analysis.\n\n[R1] Tong, G., Leung, J. C. H., Peng, X., Shi, H., Zheng, L., Wang, S., ... & Chakravarthula, P. (2025). Multimodal Neural Acoustic Fields for Immersive Mixed Reality. IEEE Transactions on Visualization and Computer Graphics.\n\n---\n> **Minor comments:**\n\n**Typos:** Thanks for pointing out the typos and we will fix them in the revised version.\n\n**What does L81 mean?** It highlights the growing importance of RIR modeling in immersive audio rendering for virtual and augmented reality applications. RIR characterizes how sound propagates through an environment, which is essential for generating spatially and temporally realistic audio cues. These cues greatly enhance user immersion in virtual environments, aligning with the motivation behind our work on neural acoustic fields and the resounding task."}]

Author rebuttal:

Meta-review: The submission initially received mixed ratings, with two reviewers leaning toward acceptance and two toward rejection. Reviewers found the problem of estimating the acoustic field at novel emitter positions (resounding) interesting, and they noted that the proposed solution is simple, grounded in established principles, and demonstrates improved performance across multiple datasets compared to baselines.

However, concerns were raised regarding the clarity of some technical aspects, missing related work, and the relevance of the proposed task. The authors rebuttal effectively addressed these issues, and most reviewers stated that their concerns were largely resolved. Following the rebuttal, all reviewers recommended acceptance.

The AC concurs with the reviewers consensus and recommends acceptance of the submission. The authors are encouraged to incorporate their rebuttal and the reviewers’ feedback into the final version.
Decision: Accept (poster)