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Deep learning approaches have emerged that aim to transform an audio signalso that it sounds as if it was recorded in the same room as a referencerecording, with applications both in audio post-production and augmentedreality. In this work, we propose FiNS, a Filtered Noise Shaping network thatdirectly estimates the time domain room impulse response (RIR) from reverberantspeech. Our domain-inspired architecture features a time domain encoder and afiltered noise shaping decoder that models the RIR as a summation of decayingfiltered noise signals, along with direct sound and early reflectioncomponents. Previous methods for acoustic matching utilize either large modelsto transform audio to match the target room or predict parameters foralgorithmic reverberators. Instead, blind estimation of the RIR enablesefficient and realistic transformation with a single convolution. An evaluationdemonstrates our model not only synthesizes RIRs that match parameters of thetarget room, such as the $T_{60}$ and DRR, but also more accurately reproducesperceptual characteristics of the target room, as shown in a listening testwhen compared to deep learning baselines.

Filtered Noise Shaping for Time Domain Room Impulse Response Estimation From Reverberant Speech