We present the Geometric-Wave Acoustic (GWA) dataset, a large-scale audio dataset of over 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.

@inproceedings{tang2022gwa,
    title={GWA: A Large Geometric-Wave Acoustic Dataset for Audio Processing},
    author={Zhenyu Tang and Rohith Aralikatti and Anton Ratnarajah and and Dinesh Manocha},
    url = {https://doi.org/10.1145/3528233.3530731},
    booktitle = {Special Interest Group on Computer Graphics and Interactive Techniques Conference Proceedings (SIGGRAPH '22 Conference Proceedings)},
    year={2022}
}