RealImpact: A Dataset of Impact Sound Fields for Real Objects
Samuel Clarke Ruohan Gao Mason Wang Mark Rau Julia Xu Jui-Hsien Wang Doug L. James Jiajun Wu
Objects make unique sounds under different perturbations, environment
conditions, and poses relative to the listener. While prior works have modeled
impact sounds and sound propagation in simulation, we lack a standard dataset
of impact sound fields of real objects for audio-visual learning and
calibration of the sim-to-real gap. We present RealImpact, a large-scale
dataset of real object impact sounds recorded under controlled conditions.
RealImpact contains 150,000 recordings of impact sounds of 50 everyday objects
with detailed annotations, including their impact locations, microphone
locations, contact force profiles, material labels, and RGBD images. We make
preliminary attempts to use our dataset as a reference to current simulation
methods for estimating object impact sounds that match the real world.
Moreover, we demonstrate the usefulness of our dataset as a testbed for
acoustic and audio-visual learning via the evaluation of two benchmark tasks,
including listener location classification and visual acoustic matching.