  We present a novel approach to improve the performance of learning-based
speech dereverberation using accurate synthetic datasets. Our approach is
designed to recover the reverb-free signal from a reverberant speech signal. We
show that accurately simulating the low-frequency components of Room Impulse
Responses (RIRs) is important to achieving good dereverberation. We use the GWA
dataset that consists of synthetic RIRs generated in a hybrid fashion: an
accurate wave-based solver is used to simulate the lower frequencies and
geometric ray tracing methods simulate the higher frequencies. We demonstrate
that speech dereverberation models trained on hybrid synthetic RIRs outperform
models trained on RIRs generated by prior geometric ray tracing methods on four
real-world RIR datasets.
