用可微信号处理框架实现自适应声学均衡，比传统方法更稳定。
A DDSP Framework for Adaptive Room Equalization
- 基于可微信号处理，通过自动微分还原经典Fx-LMS算法
- 频域目标使系统距离降低70%，梅尔谱距离减少13%（最差情况）
- 适合研究自适应滤波与可微信号处理融合的开发者
在时变声学环境和复杂激励信号（如音乐）下，自适应房间均衡仍具挑战性，传统滤波-x最小均方（Fx-LMS）方法因结构僵化而表现不佳。本文提出一种模块化可微数字信号处理（DDSP）框架，用于闭环自适应房间均衡，可通过自动微分恢复Fx-LMS作为特例。该框架支持可替换的均衡结构、响应估计方法、损失函数和优化器。在时变实测房间冲击响应实验中，频域目标比时域目标更具稳定性。相较于未均衡状态，系统距离降低70%，梅尔谱距离减少13%（最差情况）。进一步分析了在线响应估计精度与帧长对响应速度与收敛稳定性的权衡影响。整体上，该框架为探索经典自适应滤波与基于DDSP的优化协同提供了统一开源基础。
原文摘要 · Abstract (English)
Adaptive room equalization remains challenging under time-varying acoustic conditions and complex excitation signals, such as music. In these scenarios, classical filtered-x least mean squares (Fx-LMS) methods falter due to their rigid formulation. We present a modular differentiable digital signal processing (DDSP) framework for closed-loop adaptive room equalization that recovers Fx-LMS as a special case through automatic differentiation. The framework supports interchangeable EQ structures, response estimation methods, loss functions, and optimizers. Experiments with time-varying measured room impulse responses show that frequency-domain objectives provide more stable adaptation than time-domain objectives in the considered scenarios. Relative to the non-equalized response, system distance is reduced by 70% and mel-spectral distance by 13% (worst-case scenario). We further examine how online room response estimation accuracy and frame length affect the trade-off between responsiveness and convergence stability. Overall, the framework provides a unified open-source basis for exploring synergies between classical adaptive filtering and DDSP-based optimization.
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