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.

我们提出了一种基于可微分的反馈延迟网络 (FDN) 的参数寻找方法，通过反向传播同时学习每个延迟网络参数，以使其输出呈现出测量的房间脉冲响应的感知特性。通过实验证明，该方法能够产生时间不变和频率无关的 FDN，能够与所需的声学特性紧密匹配，并且优于基于遗传算法和分析滤波器设计的现有方法。