  A common bane of artificial reverberation algorithms is spectral coloration,
typically manifesting as metallic ringing, leading to a degradation in the
perceived sound quality. This paper presents an optimization framework where a
differentiable feedback delay network is used to learn a set of parameters to
reduce coloration iteratively. The parameters under optimization include the
feedback matrix, as well as the input and output gains. The optimization
objective is twofold: to maximize spectral flatness through a spectral loss
while maintaining temporal density by penalizing sparseness in the parameter
values. A favorable narrower distribution of modal excitation is achieved while
maintaining the desired impulse response density. In a subjective assessment,
the new method proves effective in reducing perceptual coloration of late
reverberation. The proposed method achieves computational savings compared to
the baseline while preserving its performance. The effectiveness of this work
is demonstrated through two application scenarios where natural-sounding
synthetic impulse responses are obtained via the introduction of attenuation
filters and an optimizable scattering feedback matrix.
