  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 target attributes 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 perceptually-motivated 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 FDN design.
