Teaser Image

Abstract

Rigid-body impact sound synthesis methods often omit the ground sound. In this paper we analyze an idealized ground-sound model based on an elastodynamic halfspace, and use it to identify scenarios wherein ground sound is perceptually relevant versus when it is masked by the impacting object’s modal sound or transient acceleration noise. Our analytical model gives a smooth, closed-form expression for ground surface acceleration, which we can then use in the Rayleigh integral or in an “acoustic shader” for a finite-difference time-domain wave simulation. We find that when modal sound is inaudible, ground sound is audible in scenarios where a dense object impacts a soft ground and scenarios where the impact point has a low elevation angle to the listening point.

Paper and Links

*Erratum: The original version of the paper stated an exponent incorrectly in the caption of Figure 9. It has been corrected in this version.

Citation

bibtex
Ante Qu and Doug L. James, “On the impact of ground sound,” in Proceedings of the 22nd International Conference on Digital Audio Effects (DAFx-19), Birmingham, UK, Sept. 2–5 2019.

Supplemental Material

Ideal Steel Ball, Wood Floor Impact Sound (Figure 8):

Wavesolved Granite Rock, Soil Ground Sound (Figure 7)

Wavesolved 13 Steel Ball Bearings, Soil Ground Sound (Figure 6)

Wavesolved 13 Steel Ball Bearings, Concrete Ground Sound (Figure 5)

Wavesolved 13 Steel Ball Bearings, Wood Ground Sound (Supplementary)

Wavesolved 13 Plastic Dice, Wax Floor Sound (Supplementary)*

*Implementation caveats: For the wax, we used ν=0.25 instead of 0.37 to avoid numerical issues. Only for this example we manually removed DC offsets in postprocessing which arise from the implementation-specific boundary conditions necessary to handle vibrating objects leaving the domain; in all other examples we provided the output as-is from the listening point.

Acknowledgements

We thank the anonymous reviewers for their feedback. We acknowledge support from the National Science Foundation (NSF) under grant DGE-1656518, the Toyota Research Institute (TRI), and Google Cloud Platform compute resources. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NSF, the TRI, any other Toyota entity, or others.