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.

翻译：人工混响算法的常见缺陷是频谱染色，通常表现为金属性混响，导致感知音质下降。本文提出一种优化框架，通过可微分的反馈延迟网络迭代学习参数集以降低染色效应。优化参数包括反馈矩阵、输入增益和输出增益。优化目标具有双重性：通过频谱损失最大化频谱平坦度，同时通过惩罚参数稀疏性维持时间密度。在保持所需脉冲响应密度的前提下，实现了更优的模态激励窄分布。主观评估表明，新方法能有效减少晚期混响的感知染色。与基线方法相比，所提方法在保持性能的同时实现了计算量节省。通过两个应用场景验证了本工作的有效性：通过引入衰减滤波器和可优化散射反馈矩阵，获得了自然感合成的脉冲响应。