arxiv: 2604.08967 · v1 · submitted 2026-04-10 · 💻 cs.SD

Recognition: no theorem link

AudioGS: Spectrogram-Based Audio Gaussian Splatting for Sound Field Reconstruction

Chunhao Bi , Houqiang Zhong , Zhixin Xu , Li Song , Zhengxue Cheng

Authors on Pith no claims yet

Pith reviewed 2026-05-10 17:43 UTC · model grok-4.3

classification 💻 cs.SD

keywords audio gaussian splattingsound field reconstructionspectrogrambinaural audiospatial audiospherical harmonicsvisual-free

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The pith

Audio Gaussians reconstruct sound fields explicitly from spectrograms without visual input.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper sets out to show that a sound field can be modeled directly as a collection of Audio Gaussians derived from spectrogram data. Each Gaussian carries dual spherical-harmonic coefficients to encode directionality and one decay coefficient to handle distance falloff. Rendering for any listener pose then combines spherical-harmonic evaluation, geometry-guided attenuation, and phase correction to produce binaural waveforms. This explicit, visual-free route is claimed to capture spatial acoustic detail more reliably than implicit networks that depend on camera images. If the claim holds, it removes the need for synchronized visual data when building immersive audio scenes from sparse microphone recordings.

Core claim

AudioGS encodes the sound field as a set of Audio Gaussians, each tied to a time-frequency bin and equipped with dual spherical-harmonic coefficients together with a single decay coefficient. For a chosen listener pose the method evaluates the spherical-harmonic field to recover directionality, multiplies by geometry-guided distance attenuation and applies phase correction, then inverts the spectrogram to obtain the waveform. Experiments on the Replay-NVAS dataset show this representation reduces magnitude reconstruction error by more than 14 percent and the perceptual metric by roughly 25 percent relative to the strongest visual-guided baseline.

What carries the argument

Audio Gaussian: an explicit primitive per time-frequency bin that stores dual spherical-harmonic coefficients for angular response and a decay coefficient for radial attenuation, allowing direct splatting and rendering of the sound field.

If this is right

Binaural audio can be synthesized for arbitrary poses from sparse microphone data alone.
Complex spatial cues are recovered more accurately than by implicit neural fields conditioned on images.
The same Gaussian set supports multiple listener positions without retraining.
Reconstruction quality improves when geometry information is available even in the absence of visuals.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The explicit Gaussian form could be updated frame-by-frame to handle slowly moving sources or changing environments.
Joint optimization with visual Gaussian splatting might produce consistent audio-visual scene models from the same observations.
The decay coefficient might be extended to frequency-dependent absorption to better match real materials.

Load-bearing premise

The acoustic field can be decomposed into a sum of independent Audio Gaussians whose contributions combine accurately using only geometry and audio observations, without visual priors or extra calibration.

What would settle it

On a new recording set containing strong early reflections or non-stationary sources, if magnitude and perceptual errors no longer improve over the best visual-guided method, the explicit Gaussian decomposition would be shown insufficient.

Figures

Figures reproduced from arXiv: 2604.08967 by Chunhao Bi, Houqiang Zhong, Li Song, Zhengxue Cheng, Zhixin Xu.

**Figure 1.** Figure 1: AudioGS framework and performance. Top: Inspired by 3D Visual Gaussian Splatting, we propose AudioGS, a visual-free framework that models the sound field as a set of learnable 3D Audio Gaussians. Bottom: Quantitative comparison on the Replay-NVAS dataset. AudioGS significantly outperforms baselines in reconstruction quality (MAG) and perceptual fidelity (DPAM). AV-NeRF [2], typically employ implicit neural… view at source ↗

**Figure 2.** Figure 2: Overview of the AudioGS framework. We explicitly represent the sound field by encoding the source spectrogram into a set of Audio Gaussians, where each Gaussian corresponds to a specific time-frequency bin. For a target listener pose, the rendering pipeline separates into two streams: (1) Magnitude Modulation, which utilizes spherical harmonics and distance attenuation to model directional energy and spati… view at source ↗

**Figure 3.** Figure 3: Comparison of binaural waveform synthesis. Left: Images of source and target viewpoints provided for spatial context. Note that unlike visualdependent baselines, AudioGS does not utilize these RGB images. Right: Synthesized waveforms compared against the Ground Truth (GT). The red boxes highlight regions with distinct transient acoustic events. Baseline methods (ViGAS, AV-NeRF) often produce attenuated or… view at source ↗

**Figure 4.** Figure 4: MUSHRA listening test on Replay-NVAS dataset. Mean Basic Audio Quality (BAQ) ratings (0–100) from 12 participants on six representative novel-view samples. Error bars denote the standard error of the mean (SEM). Lowest 20% magnitude Lowest 50% magnitude Overall [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗

**Figure 5.** Figure 5: The spatial distribution of Audio Gaussians filtered by STFT mag [PITH_FULL_IMAGE:figures/full_fig_p006_5.png] view at source ↗

read the original abstract

Spatial audio is fundamental to immersive virtual experiences, yet synthesizing high-fidelity binaural audio from sparse observations remains a significant challenge. Existing methods typically rely on implicit neural representations conditioned on visual priors, which often struggle to capture fine-grained acoustic structures. Inspired by 3D Gaussian Splatting (3DGS), we introduce AudioGS, a novel visual-free framework that explicitly encodes the sound field as a set of Audio Gaussians based on spectrograms. AudioGS associates each time-frequency bin with an Audio Gaussian equipped with dual Spherical Harmonic (SH) coefficients and a decay coefficient. For a target pose, we render binaural audio by evaluating the SH field to capture directionality, incorporating geometry-guided distance attenuation and phase correction, and reconstructing the waveform. Experiments on the Replay-NVAS dataset demonstrate that AudioGS successfully captures complex spatial cues and outperforms state-of-the-art visual-dependent baselines. Specifically, AudioGS reduces the magnitude reconstruction error (MAG) by over 14% and reduces the perceptual quality metric (DPAM) by approximately 25% compared to the best performing visual-guided method.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper proposes AudioGS, a visual-free explicit representation for sound field reconstruction that encodes spectrogram-based audio data as a collection of Audio Gaussians, each equipped with dual spherical-harmonic coefficients for directionality and a single decay coefficient. For a target pose, binaural audio is rendered by evaluating the SH field, applying geometry-guided distance attenuation and phase correction, and reconstructing the waveform. Experiments on the Replay-NVAS dataset report that AudioGS outperforms state-of-the-art visual-dependent baselines, reducing magnitude reconstruction error (MAG) by over 14% and the perceptual metric (DPAM) by approximately 25%.

Significance. If the visual-free claim is substantiated and the reported gains hold under rigorous controls, AudioGS would represent a meaningful advance in explicit, interpretable modeling of spatial audio, analogous to 3D Gaussian Splatting but adapted to spectrogram data. The dual-SH plus scalar decay design offers a compact, potentially editable alternative to implicit neural fields, with possible benefits for real-time binaural rendering and reduced reliance on visual priors.

major comments (3)

[Abstract and §3] Abstract and §3 (method): The central claim that AudioGS is 'visual-free' and achieves the stated 14% MAG / 25% DPAM gains rests on geometry-guided distance attenuation and phase correction. It is unclear whether room layout, source/receiver positions, or calibration data are obtained without visual sensors, SfM, or manual steps; if any such priors are used, the comparison to visual-dependent baselines becomes invalid and the performance numbers cannot be interpreted as evidence for a purely audio-driven method.
[§3.1] §3.1 (Audio Gaussian definition): The model associates each time-frequency bin with a single scalar decay coefficient per Gaussian. No derivation, ablation, or comparison to per-frequency or direction-dependent absorption models is provided, yet this scalar is load-bearing for capturing reverberation; the reported gains may therefore reflect dataset-specific simplicity rather than general representational power.
[§4] §4 (experiments): The abstract reports concrete percentage improvements without error bars, statistical significance tests, or ablation studies on the Replay-NVAS dataset. This absence prevents assessment of whether the 14% MAG and 25% DPAM reductions are robust or sensitive to hyper-parameters, making the outperformance claim difficult to evaluate as evidence for the proposed representation.

minor comments (2)

[§3] Notation for dual SH coefficients and the rendering equation should be introduced with explicit variable definitions and a small worked example to improve readability.
[Abstract] The abstract uses 'over 14%' and 'approximately 25%'; replace with precise values and reference the corresponding table or figure in the main text.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the thoughtful and constructive comments on our manuscript. We address each of the major comments point by point below, providing clarifications and indicating the revisions we plan to make.

read point-by-point responses

Referee: [Abstract and §3] Abstract and §3 (method): The central claim that AudioGS is 'visual-free' and achieves the stated 14% MAG / 25% DPAM gains rests on geometry-guided distance attenuation and phase correction. It is unclear whether room layout, source/receiver positions, or calibration data are obtained without visual sensors, SfM, or manual steps; if any such priors are used, the comparison to visual-dependent baselines becomes invalid and the performance numbers cannot be interpreted as evidence for a purely audio-driven method.

Authors: We appreciate the referee pointing out the need for clarity on the visual-free aspect. AudioGS does not use any visual sensors, SfM, or image-based methods. The geometry-guided distance attenuation and phase correction are based on the known 3D positions of the audio sources and receivers, which are provided as part of the Replay-NVAS dataset metadata without requiring visual input. The room layout is approximated using acoustic propagation models rather than visual reconstruction. In contrast, the visual-dependent baselines explicitly leverage image features or visual scene understanding. We will revise Section 3 to explicitly state the input assumptions and highlight this distinction to avoid any ambiguity. revision: yes
Referee: [§3.1] §3.1 (Audio Gaussian definition): The model associates each time-frequency bin with a single scalar decay coefficient per Gaussian. No derivation, ablation, or comparison to per-frequency or direction-dependent absorption models is provided, yet this scalar is load-bearing for capturing reverberation; the reported gains may therefore reflect dataset-specific simplicity rather than general representational power.

Authors: The single scalar decay coefficient is chosen to balance representational power with model compactness, allowing the dual SH coefficients to focus on directional information while the decay captures the overall reverberation envelope. This is motivated by standard acoustic models where frequency-independent decay approximates late reverberation in many environments. Although no ablation was included in the initial submission, the superior performance on both magnitude and perceptual metrics indicates its effectiveness. In the revised version, we will include a derivation sketch in §3.1 and an ablation study comparing the scalar decay to a per-frequency variant on the Replay-NVAS dataset. revision: partial
Referee: [§4] §4 (experiments): The abstract reports concrete percentage improvements without error bars, statistical significance tests, or ablation studies on the Replay-NVAS dataset. This absence prevents assessment of whether the 14% MAG and 25% DPAM reductions are robust or sensitive to hyper-parameters, making the outperformance claim difficult to evaluate as evidence for the proposed representation.

Authors: We agree that additional statistical analysis would strengthen the experimental section. The percentage improvements are computed as averages over the test set, and our internal evaluations showed consistent gains with low variance. To address this concern, we will add error bars to the reported metrics in the revised manuscript, include results from statistical significance testing, and expand the ablation studies to cover hyper-parameter sensitivity (e.g., number of Gaussians, SH degree). These will be presented in Section 4 and the supplementary material. revision: yes

Circularity Check

0 steps flagged

No significant circularity in derivation chain

full rationale

The paper introduces AudioGS as an explicit representation of the sound field via Audio Gaussians equipped with dual SH coefficients and a decay coefficient, followed by a rendering procedure that applies geometry-guided distance attenuation and phase correction to produce binaural audio. Performance metrics (MAG reduction >14%, DPAM ~25%) are reported from external experiments on the Replay-NVAS dataset against visual-dependent baselines, without any equations or claims that reduce these outcomes to quantities defined by the fitted parameters themselves. No self-citations are invoked as load-bearing for uniqueness or ansatz choices, and the method does not rename known results or smuggle assumptions via prior work. The derivation remains self-contained against the stated external benchmarks.

Axiom & Free-Parameter Ledger

1 free parameters · 2 axioms · 1 invented entities

The central claim rests on the new Audio Gaussian representation and on the assumption that spherical harmonics plus a decay term suffice to capture directionality and attenuation from sparse spectrogram data.

free parameters (1)

Audio Gaussian coefficients (SH and decay)
Each Gaussian's dual spherical-harmonic coefficients and decay value are optimized from the input spectrograms; these are the primary fitted quantities.

axioms (2)

domain assumption Spherical harmonics can represent the directional component of a sound field at each time-frequency bin
Invoked when the method evaluates the SH field to capture directionality for a target pose.
domain assumption Geometry-guided distance attenuation and phase correction can be applied without visual input
Used in the rendering step to produce the final waveform.

invented entities (1)

Audio Gaussian no independent evidence
purpose: Explicit encoding of the sound field as a set of elements each tied to a time-frequency bin
New primitive introduced by the paper; no independent evidence outside the current work is provided.

pith-pipeline@v0.9.0 · 5496 in / 1489 out tokens · 77190 ms · 2026-05-10T17:43:23.054765+00:00 · methodology

discussion (0)

Reference graph

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