Data-Driven Room Acoustic Modeling Via Differentiable Feedback Delay
  Networks With Learnable Delay Lines
Alessandro Ilic Mezza Riccardo Giampiccolo Enzo De Sena Alberto Bernardini
Over the past few decades, extensive research has been devoted to the design
of artificial reverberation algorithms aimed at emulating the room acoustics of
physical environments. Despite significant advancements, automatic parameter
tuning of delay-network models remains an open challenge. We introduce a novel
method for finding the parameters of a Feedback Delay Network (FDN) such that
its output renders the perceptual qualities of a measured room impulse
response. The proposed approach involves the implementation of a differentiable
FDN with trainable delay lines, which, for the first time, allows us to
simultaneously learn each and every delay-network parameter via
backpropagation. The iterative optimization process seeks to minimize a
time-domain loss function incorporating differentiable terms accounting for
energy decay and echo density. Through experimental validation, we show that
the proposed method yields time-invariant frequency-independent FDNs capable of
closely matching the desired acoustical characteristics, and outperforms
existing methods based on genetic algorithms and analytical filter design.