  Deep learning approaches have emerged that aim to transform an audio signal
so that it sounds as if it was recorded in the same room as a reference
recording, with applications both in audio post-production and augmented
reality. In this work, we propose FiNS, a Filtered Noise Shaping network that
directly estimates the time domain room impulse response (RIR) from reverberant
speech. Our domain-inspired architecture features a time domain encoder and a
filtered noise shaping decoder that models the RIR as a summation of decaying
filtered noise signals, along with direct sound and early reflection
components. Previous methods for acoustic matching utilize either large models
to transform audio to match the target room or predict parameters for
algorithmic reverberators. Instead, blind estimation of the RIR enables
efficient and realistic transformation with a single convolution. An evaluation
demonstrates our model not only synthesizes RIRs that match parameters of the
target room, such as the $T_{60}$ and DRR, but also more accurately reproduces
perceptual characteristics of the target room, as shown in a listening test
when compared to deep learning baselines.
