  We present the Geometric-Wave Acoustic (GWA) dataset, a large-scale audio
dataset of about 2 million synthetic room impulse responses (IRs) and their
corresponding detailed geometric and simulation configurations. Our dataset
samples acoustic environments from over 6.8K high-quality diverse and
professionally designed houses represented as semantically labeled 3D meshes.
We also present a novel real-world acoustic materials assignment scheme based
on semantic matching that uses a sentence transformer model. We compute
high-quality impulse responses corresponding to accurate low-frequency and
high-frequency wave effects by automatically calibrating geometric acoustic
ray-tracing with a finite-difference time-domain wave solver. We demonstrate
the higher accuracy of our IRs by comparing with recorded IRs from complex
real-world environments. Moreover, we highlight the benefits of GWA on audio
deep learning tasks such as automated speech recognition, speech enhancement,
and speech separation. This dataset is the first data with accurate wave
acoustic simulations in complex scenes. Codes and data are available at
https://gamma.umd.edu/pro/sound/gwa.
