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REVIEW 5 major objections 10 minor 1 cited by

ForestIR: Physics-Informed Forest Sound Simulation for Array-Based Bioacoustic Remote Sensing

T0 review · 5 major / 10 minor · reviewed 2026-07-08 · glm-5.2

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Pith's one-line read Simulator matches forest acoustics and exposes what breaks localization

desk verdict Open-source forest acoustic simulator with useful engineering but unvalidated core forest module read the letter →

arxiv 2607.06299 v1 pith:XKH45NOB submitted 2026-07-07 eess.AS cs.SD

Xin Shen , Jennifer N. Kampe , Changwoo J. Lee , Braden Scherting , Panu Somervuo , Ari Lehtiö , Sandro von Brandenburg , Ossi Nokelainen
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Otso Ovaskainen David B. Dunson
This is my paper · ORCID
classification eess.AScs.SD
keywords forestforestirconditionsarrayatmosphericarray-basedbioacousticdesign
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The pith

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

The reading

ForestIR is a physics-informed, path-based acoustic simulator that generates source-microphone impulse responses under user-controlled forest structure, ground conditions, atmospheric state, and array geometry. The paper claims that by computing sound speed from temperature, humidity, and pressure rather than assuming a fixed constant, and by placing tree trunks, ground reflections, and optional canopy scatterers at explicit 3D positions, ForestIR reproduces field-measured impulse response decay (mean EDC Pearson r = 0.837) and rendered bird-call recordings (best on 3 of 4 similarity metrics) more closely than a legacy trunk-scattering simulator. The paper further demonstrates that vegetation layout and atmospheric temperature each independently affect SRP-PHAT localization error, showing that simulators lacking explicit spatial control over trees or fixed atmospheric assumptions can miss real failure modes. The central object is the impulse response assembled from delayed, frequency-shaped path contributions (direct path, image-source ground reflection, single-scattering cylinders for trunks, stochastic branch-and-leaf scatterers), each attenuated by ISO 9613-1 atmospheric absorption, which is then convolved with dry source signals and combined with controlled noise to produce synthetic multichannel array recordings.

What carries the argument

The impulse response h_{m,s}[n] assembled from four path contributions: (1) direct path with ISO 9613-1 atmospheric attenuation, (2) image-source ground reflection scaled by ground-type-specific amplitude multiplier (0.99 concrete/ice, 0.80 grass, 0.70 snow), (3) single-scattering rigid-cylinder trunk contributions with finite-height gating and precomputed per-radius-bin filter banks, and (4) optional stochastic branch-and-leaf scatterers as omnidirectional secondary sources. Propagation delay uses a moist-air speed of sound c = sqrt(gamma * R_spec * T) where R_spec and gamma vary with humidity and pressure. The full pipeline convolves these IRs with dry source waveforms and adds controlled,

What would settle it

If impulse responses simulated by ForestIR under real forest conditions (with mapped tree positions and canopy structure) fail to match field-measured forest impulse responses significantly better than the legacy trunk-scattering model, the central claim that explicit spatial control over vegetation improves simulation fidelity for localization would not hold.

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Extended reading notes

Core claim

The paper establishes that a lightweight path-based simulator with explicit 3D tree geometry, temperature-dependent sound speed, and configurable ground types can reproduce the broad decay structure of field-measured forest impulse responses well enough to serve as a practical tool for microphone-array design and localization stress-testing. The key finding is not just fidelity but controllability: when tree placement or atmospheric temperature is varied one factor at a time, SRP-PHAT localization error changes substantially, whereas a legacy simulator with implicit forest structure shows almost no sensitivity to tree count even at 100,000 trees. This means that simplified scattering models,

Load-bearing premise

The field validation is conducted entirely on a treeless frozen lake, so the trunk and canopy scattering components that distinguish ForestIR from prior simulators are never tested against measured impulse responses from an actual forest.

Editorial extensions

If this is right

  • Microphone-array deployments for biodiversity monitoring could be pre-optimized in simulation before field deployment, reducing costly trial-and-error in remote forest sites.
  • Localization algorithms could be stress-tested against systematic temperature and humidity sweeps to determine deployment-season windows where fixed-sound-speed assumptions remain valid.
  • Synthetic training data from ForestIR could augment machine-learning models for bird species identification or source localization that otherwise lack sufficient labeled field recordings across diverse forest conditions.
  • The explicit tree-position interface could be coupled with LiDAR-derived forest maps to produce site-specific acoustic propagation predictions for planned array installations.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The trunk-scattering module, a core differentiator from the legacy model, is never validated against field impulse responses from an actual forest. The snow-field validation exercises only direct-path, ground-reflection, and near-surface diffuse scattering components. Extending validation to forested sites with measured tree maps would be the critical next test.
  • The ground reflection model uses fixed amplitude multipliers rather than frequency-dependent impedance, which may be adequate for broadband localization but could misrepresent frequency-selective ground effects relevant to specific bird-call spectral regions.
  • The single-scattering approximation for trunks may break down in dense forests where multiple scattering between trunks becomes significant, potentially limiting applicability at high stem densities or long propagation distances through deep forest.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

5 major / 10 minor

Summary. This paper presents ForestIR, a physics-informed, path-based acoustic simulator for microphone-array bioacoustic sensing in forested environments. The simulator assembles source-microphone impulse responses from direct-path, ground-reflection, trunk-scattering, and optional branch/leaf-scattering components, with atmospheric absorption (ISO 9613-1) and a moist-air speed-of-sound model. The authors evaluate ForestIR via four experiments: (1) SRP-PHAT localization sensitivity to tree layout, (2) localization sensitivity to atmospheric temperature mismatch, (3) EDC comparison against field-measured IRs from a frozen lake (Konnevesi), and (4) bird-call similarity metrics against field recordings. Code is publicly available. The central claims are that ForestIR produces realistic IRs, is sensitive to environmental and geometric factors relevant to localization, and outperforms a legacy trunk-scattering simulator on EDC and bird-call metrics.

Significance. The simulator addresses a genuine methodological gap: existing tools either lack explicit forest geometry control or are too computationally heavy for large-scale array studies. The code is publicly available (GitHub), which is a significant strength for reproducibility. The moist-air speed-of-sound calculation (Supporting Information S1-S2) and the temperature-mismatch localization experiment (Table 2) provide a concrete, falsifiable demonstration that atmospheric state affects localization. The EDC comparison (Table 3, r=0.837 vs. 0.251 for legacy) and bird-call similarity metrics (Table 4) constitute genuine out-of-sample checks against field data, not circular validation. However, the significance is tempered by the fact that all field validation is conducted on a treeless site, so the forest-specific modules (trunk and canopy scattering) are validated only in synthetic localization experiments, not against forested field measurements.

major comments (5)
  1. §3.1, Table 1: The two realistic ForestIR tree-layout scenarios (measured Konginkangas map vs. synthetic repulsive process with 43 trees) produce nearly identical localization outcomes: mean error 0.8141 vs. 0.8145, median 0.2236 vs. 0.2236, and max error 17.9790 vs. 17.9790 (identical to four decimal places). Both scenarios use the same branch/leaf scattering configuration (5000 scatterers, same fixed scene seed), so the only difference is trunk positions. This near-perfect agreement suggests that trunk scattering contributes negligibly to the simulated IRs in these configurations. The paper's text states these results 'demonstrate that localization behavior can depend strongly on vegetation geometry,' but the data shown actually demonstrate insensitivity to trunk layout. The only scenario showing large degradation (Scenario 3, crowded layout) is an extreme degenerate case. The authors应
  2. §3.1, Table 1 (continued): The previous comment was truncated. The authors should either (a) provide an analysis of why trunk scattering is negligible relative to branch/leaf scattering in these configurations (e.g., relative energy contributions of each path type), or (b) adjust the claim that vegetation geometry strongly affects localization, since the evidence shown supports only the claim that extreme, physically implausible tree concentrations affect localization. This is load-bearing because the paper's title and motivation center on forest-specific propagation, and the trunk-scattering module (Eq. 2, term 3) is a core differentiator from the legacy model.
  3. §3.3-3.4: All field validation (EDC comparison in Table 3, bird-call similarity in Table 4) is conducted on a treeless frozen lake (Konnevesi). The trunk-scattering and canopy-scattering modules — the core forest-specific components — are never validated against field IRs from an actual forest. The paper's name ('ForestIR') and central motivation concern forested environments, yet the only field validation exercises the direct path, ground reflection, and near-surface diffuse scattering components. The branch/leaf scattering module is repurposed to represent snow piles and surface roughness (§3.3), which is an inventive use but does not validate its intended purpose. The authors should explicitly acknowledge this limitation in the Discussion (currently §4 mentions simplifying assumptions but does not state that no forested field validation exists) and clarify that forest-specific claims,
  4. §3.3-3.4 (continued): The previous comment was truncated. The authors should clarify that forest-specific claims rest on the internal consistency of the simulator's physics, not on direct field validation in forests. This is load-bearing for the paper's positioning relative to its title and abstract claims.
  5. §3.3, Table 3: The EDC comparison is based on only 7 source-receiver pairs from a single site. While the mean Pearson r=0.837 is encouraging, the sample size is small and from one environmental condition (winter snow-field). The paper should state the number of pairs and the single-site limitation more prominently in the Results section (not just implicitly in the Methods), and should temper the claim of 'realistic features' accordingly. This is not a blocking issue but affects the strength of the validation claim.
minor comments (10)
  1. §2.2, Eq. (2): The notation h^{tr}_{m,s,i} uses subscript i for tree index, but the summation is over T (the set of trees). Clarify whether T is the set of tree indices or a count.
  2. §3.1: The BirdNET clip identifier 'BirdNET 01 XC169082.wav' is used for the 121-position grid, but 30 BirdNET vocalizations are used for the single-position experiment (Table 2, Panel B). Clarify whether the single clip in Panel A is one of the 30 used in Panel B.
  3. Table 2, Panel A, T=20°C row: The mean error is 0.739 m, but in Table 1 the mean error for the same configuration (Konginkangas layout, T=20°C) is 0.8141 m. If these are the same configuration, the discrepancy should be explained; if not, the difference in setup should be noted.
  4. Figure 3: The y-axis label 'EDC (dB)' is used, but the text describes normalized EDCs. Clarify whether the dB scale is applied after normalization.
  5. §3.4, Table 4: The 'Dry source' baseline achieves the best AEI (0.06799), but the text states 'AEI was similar across methods and was slightly best for the dry-source baseline.' The word 'slightly' is subjective; consider reporting whether the difference is statistically significant.
  6. §2.2, Ground-reflected path: The ground-type parameterization uses fixed multipliers (α_g=0.99 for concrete/water/ice, 0.80 for grass, 0.70 for snow). These are described as 'simplified approximations,' but no sensitivity analysis is provided for how the EDC or localization results depend on α_g. A brief note on sensitivity would strengthen the paper.
  7. Supporting Information S4: The CLI argument --noise-level is described as 'the target ratio RMS(noise)/RMS(signal) after rendering,' but in §3.4 the noise level is set to 0.3 without specifying the units or interpretation. Clarify consistency between SI and main text.
  8. References: The ISO 9613-2:1996 reference notes it is 'Withdrawn; superseded by ISO 9613-2:2024.' Consider updating to the current standard if the simulator's implementation is compatible.
  9. §1, paragraph 3: 'Simulation provides a valuable tools' should be 'Simulation provides a valuable tool.'
  10. §1, paragraph 3: 'often multiple factors confounded by many factors at the same time' is grammatically awkward. Consider revision.

Simulated Author's Rebuttal

5 responses · 0 unresolved

We thank the referee for a careful and constructive review. The referee raises three major points: (1) the near-identical localization results for the two realistic tree layouts in Table 1 suggest trunk scattering is negligible, contradicting the claim that vegetation geometry strongly affects localization; (2) all field validation is conducted on a treeless site, so the forest-specific modules (trunk and canopy scattering) are not validated against forested field measurements; and (3) the EDC comparison uses only 7 source-receiver pairs from a single site. We agree with all three points and will revise the manuscript accordingly. Specifically, we will (a) add a per-path-type energy contribution analysis and revise the vegetation-geometry claim to accurately reflect what the data show, (b) add an explicit Discussion paragraph acknowledging that no forested field validation exists and clarifying that forest-specific claims rest on internal physical consistency rather than direct field validation, and (c) state the sample size and single-site limitation more prominently in the Results section. We believe these revisions address the referee's concerns without requiring new experiments.

read point-by-point responses

  1. Referee: §3.1, Table 1: The two realistic ForestIR tree-layout scenarios produce nearly identical localization outcomes (mean error 0.8141 vs. 0.8145, median 0.2236 vs. 0.2236, max 17.9790 vs. 17.9790). Both scenarios use the same branch/leaf scattering configuration (5000 scatterers, same fixed scene seed), so the only difference is trunk positions. This near-perfect agreement suggests trunk scattering contributes negligibly. The paper states these results 'demonstrate that localization behavior can depend strongly on vegetation geometry,' but the data show insensitivity to trunk layout. The only scenario showing large degradation is an extreme degenerate case.

    Authors: The referee is correct. The near-identical results for the two realistic layouts do indicate that trunk scattering contributes negligibly to localization error in these configurations, and the current text overstates what the data demonstrate. We will revise the manuscript in two ways. First, we will add a quantitative analysis of per-path-type energy contributions (direct, ground-reflected, trunk-scattered, branch/leaf-scattered) for the Table 1 configurations, so readers can see the relative magnitude of trunk scattering versus other path types. We expect this analysis to confirm the referee's inference that trunk energy is small relative to branch/leaf scattering in this setup. Second, we will revise the claim from 'localization behavior can depend strongly on vegetation geometry' to a more precise statement: the data show that extreme, spatially concentrated tree placements can substantially degrade localization, while realistic trunk layouts at moderate density have limited effect on SRP-PHAT localization when branch/leaf scattering is present. We agree this distinction is important for the paper's positioning. revision: yes

  2. Referee: §3.1, Table 1 (continued): The authors should either (a) provide an analysis of why trunk scattering is negligible relative to branch/leaf scattering in these configurations, or (b) adjust the claim that vegetation geometry strongly affects localization. This is load-bearing because the paper's title and motivation center on forest-specific propagation, and the trunk-scattering module is a core differentiator from the legacy model.

    Authors: We agree this is load-bearing. We will implement option (a): we will add a per-path-type energy breakdown showing the relative contributions of direct, ground-reflected, trunk-scattered, and branch/leaf-scattered energy for the Table 1 scenarios. This will make explicit why trunk layout has minimal effect in these configurations. We will also implement option (b) by revising the claim to accurately reflect that only the extreme crowded scenario shows large degradation, while realistic layouts show insensitivity. We want to be clear that we are not claiming trunk scattering is always negligible — it may become more significant at higher tree densities, different source-receiver geometries, or when branch/leaf scattering is disabled — but the data as presented do not support a strong-sensitivity claim for realistic layouts, and we will say so. revision: yes

  3. Referee: §3.3-3.4: All field validation is conducted on a treeless frozen lake (Konnevesi). The trunk-scattering and canopy-scattering modules — the core forest-specific components — are never validated against field IRs from an actual forest. The paper's name ('ForestIR') and central motivation concern forested environments, yet the only field validation exercises the direct path, ground reflection, and near-surface diffuse scattering components. The branch/leaf scattering module is repurposed to represent snow piles and surface roughness, which does not validate its intended purpose.

    Authors: This is a fair and important limitation. We will add an explicit paragraph in the Discussion (§4) stating that no forested field validation was conducted: the Konnevesi site is treeless, so the trunk-scattering and canopy-scattering modules are validated only through synthetic localization experiments (§3.1), not against forested field measurements. We will clarify that forest-specific claims rest on the internal physical consistency of the simulator's propagation model (3D path lengths, ISO 9613-1 attenuation, single-scattering cylinder formulation following Kaneko and Gamper) rather than on direct field validation in forests. We will also note that the branch/leaf scattering module's repurposing for near-surface snow roughness demonstrates the module's flexibility but does not constitute validation of its intended canopy-scattering purpose. We acknowledge that forested field validation is a necessary future step and will state this explicitly. revision: yes

  4. Referee: §3.3-3.4 (continued): The authors should clarify that forest-specific claims rest on the internal consistency of the simulator's physics, not on direct field validation in forests. This is load-bearing for the paper's positioning relative to its title and abstract claims.

    Authors: We agree and will implement this clarification. The revised Discussion will state that forest-specific propagation claims (trunk scattering, canopy scattering) are supported by the simulator's physical formulation — 3D geometric path computation, ISO 9613-1 atmospheric absorption, single-scattering cylinder theory — and by the synthetic localization sensitivity experiment (§3.1), but not by direct comparison against forested field IRs. We will also add a qualifying sentence to the Abstract noting that field validation was conducted at an open snow-field site. We believe this is honest and necessary for proper positioning of the contribution. revision: yes

  5. Referee: §3.3, Table 3: The EDC comparison is based on only 7 source-receiver pairs from a single site. The paper should state the number of pairs and the single-site limitation more prominently in the Results section, and should temper the claim of 'realistic features' accordingly.

    Authors: We agree. We will add an explicit statement in §3.3 that the EDC comparison uses 7 source-receiver pairs from a single site (Konnevesi, winter snow-field conditions), and will note this limitation prominently at the point where the EDC results are discussed, not only in the Methods. We will also temper the language around 'realistic features' to acknowledge that the validation is preliminary in scope — encouraging but based on a small sample from one environmental condition. The claim will be revised to reflect that ForestIR reproduces broad decay characteristics in this specific winter snow-field setting, rather than making a general claim of realism across conditions. revision: yes

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: the simulator is built from externally specified physical parameters and validated against independent field measurements; one minor self-citation to the authors' own prior simulator design is descriptive rather than load-bearing.

full rationale

ForestIR's impulse response model (Eq. 2) is constructed from externally specified physical inputs: source/microphone positions, tree geometry (from CSV or synthetic sampling), atmospheric state (temperature, humidity, pressure via Eq. 3 and Supporting Information S1), ground-type amplitude multipliers (fixed values from ISO 9613-2 and published absorption coefficients), and ISO 9613-1 atmospheric attenuation. No parameter is fitted to the validation data (Konnevesi field recordings) and then 'predicted' back. The EDC Pearson correlation (Table 3, r = 0.837) and bird-call similarity metrics (Table 4) are genuine out-of-sample comparisons against field measurements not used in model construction. The scatterer count (5000) and vertical band (z in [0, 0.5] m) used in the snow-field validation are configuration choices, not fitted parameters — the paper explicitly presents a sensitivity analysis (Figure 5) over scatterer counts (3000, 5000, 8000) rather than claiming a fitted optimum. The legacy model comparison (Kaneko and Gamper 2021, 2022) is an external baseline, not a self-citation. The only self-referential element is that ForestIR's trunk-scattering module 'follows the single-scattering-cylinder (SSC) philosophy' of Kaneko and Gamper, but this is a descriptive attribution of a modeling choice, not a load-bearing claim whose truth depends on the authors' own prior unverified result. The near-identical localization errors for Scenarios 1 and 2 in Table 1 (mean 0.8141 vs 0.8145) raise a correctness concern about whether trunk scattering is acoustically inert, but this is not a circularity issue — it is an empirical observation about the simulator's sensitivity, not a case where the output is defined in terms of the input by construction.

Assumptions & free parameters 6 free parameters · 5 assumptions · 1 invented entities

The free parameters are mostly physically motivated (ground multipliers from literature, scatterer counts from scene configuration) but the snow-field validation involves tuning scatterer count and vertical band to match measured decay, which is a form of parameter fitting. The axioms are standard domain assumptions for path-based acoustic simulation. The repurposed near-surface scatterer entity is the most ad hoc element.

free parameters (6)
  • α_g (ground reflection multiplier) = 0.99 (concrete/water/ice), 0.80 (grass), 0.70 (snow)
    Fixed amplitude multipliers per ground type, chosen from literature ranges but not fitted to the validation data. Used in §2.2 Ground-reflected path.
  • Number of near-surface scatterers (snow-field validation) = 5000
    Chosen to approximate snow piles and surface roughness in §3.3. Sensitivity analysis (Fig. 5) shows decay depends on this count, indicating it is a tunable parameter.
  • Scatterer vertical band (snow-field validation) = z∈[0, 0.5] m
    User-specified vertical range for near-surface scatterers in the snow-field validation, chosen to represent snow-surface roughness.
  • Trunk diameter (localization experiments) = 0.5 m
    Fixed trunk diameter for all trees in the localization sensitivity experiment (§3.1), stated as a simplification.
  • Branch/leaf scatterer count (localization experiments) = 5000
    Number of canopy scatterers in the tree-layout experiment (§3.1), sampled over z∈[2,5] m.
  • noise_level (bird-call validation) = 0.3
    RMS noise/signal ratio used in §3.4 bird-call validation, chosen to match field recording conditions.
assumptions (5)
  • domain assumption Linear time-invariant (LTI) forward model between source and microphone (Eq. 1)
    Invoked in §2.2. Standard in acoustic simulation but an approximation for outdoor time-varying environments (wind, temperature gradients).
  • domain assumption Single-scattering approximation for tree trunks (rigid cylinders)
    §2.2 Trunk scattering. Multiple scattering is ignored; the authors acknowledge this limitation in §4.
  • domain assumption Branch and leaf elements are approximately omnidirectional secondary scatterers
    §2.2 Optional branch and leaf scattering. Simplifies complex foliage scattering to impulse-shaped scattering filters.
  • domain assumption Ground reflection can be approximated by a frequency-independent amplitude multiplier rather than full impedance model
    §2.2 Ground-reflected path. The authors explicitly state this is a simplification.
  • standard math ISO 9613-1 atmospheric absorption is valid for forest propagation distances
    §2.2. ISO 9613-1 is a published standard for outdoor sound attenuation; its applicability to forest distances is a standard assumption.
invented entities (1)
  • Near-surface scatterers repurposed from branch/leaf module to represent snow piles and surface roughness
    purpose: Approximate unmodeled snow-surface diffuse scattering in the Konnevesi validation
    The branch/leaf scattering module is repurposed for snow-field validation (§3.3) by placing scatterers at z∈[0,0.5] m. This is a modeling choice without independent physical validation that such scatterers accurately represent snow-surface acoustics. The sensitivity analysis (Fig. 5) shows the decay depends on scatterer count, confirming this is a tunable approximation.

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Pith. "Pith review of ForestIR: Physics-Informed Forest Sound Simulation for Array-Based Bioacoustic Remote Sensing." pith.science (2026). https://pith.science/paper/XKH45NOB

@misc{pith2026260706299,
  author       = {Pith},
  title        = {Pith review of: ForestIR: Physics-Informed Forest Sound Simulation for Array-Based Bioacoustic Remote Sensing},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/XKH45NOB}},
  note         = {Machine review of arXiv:2607.06299}
}
read the original abstract

Microphone array-based passive acoustic monitoring is increasingly used for biodiversity sensing in forests. However, design and evaluation of array systems and configurations remains difficult since field recordings are costly, difficult to reproduce, and provide limited control over forest and atmospheric conditions. We present ForestIR, a physics-informed and reproducible simulation framework that links forest and environmental conditions to microphone-array recordings for bioacoustic remote sensing. Through a more realistic sound propagation method and a systematic control over array design and environmental factors, ForestIR provides a practical simulation framework for optimizing array-based monitoring systems, especially for sound source localization purposes. ForestIR generates source-microphone impulse responses (IRs) under user-controlled forest and atmospheric conditions, and renders synthetic array recordings by convolving test signals with controlled background noise. We evaluate and demonstrate realistic features of ForestIR through experiments based on localization sensitivity to forest layout and atmospheric conditions, and also comparison between simulated IRs with sine-sweep IR measurements from a field experiment. ForestIR provides a practical way to test how forest and ground conditions, atmospheric state, and array geometry affect bioacoustic localization, and can support microphone-array design, robustness testing, and synthetic-data generation for passive acoustic monitoring.

Figures

Figures reproduced from arXiv: 2607.06299 by the authors.

Figure 1
Figure 1. Overview of the ForestIR acoustic propagation model. For each bird source [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Konginkangas tree-position map used as the measured-tree-layout input in Scenario 1. [PITH_FULL_IMAGE:figures/full_fig_p009_2.png] view at source ↗
Figure 3
Figure 3. Normalized energy decay curves (EDCs) for seven matched measured and simulated [PITH_FULL_IMAGE:figures/full_fig_p012_3.png] view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: Baseline comparison of normalized EDCs at three representative source–receiver dis [PITH_FULL_IMAGE:figures/full_fig_p013_4.png]
Figure 5
Figure 5. Figure 5: Sensitivity of ForestIR EDCs to the number of near-surface scatterers used to approx [PITH_FULL_IMAGE:figures/full_fig_p014_5.png]
Figure 6
Figure 6. Figure 6: Clarity C50 as a function of source–receiver distance for measured and ForestIR￾simulated IRs. C50 quantifies the ratio of early energy arriving within the first 50 ms to later energy arriving after 50 ms. Both measured and simulated IRs show decreasing C50 with increa…

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Forward citations

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Pith tools

Reviewed July 8, 2026 · model on record in the stance chip above.