用可微分滤波器优化混响系统，提升效果又降计算量
Differentiable Attenuation Filters for Feedback Delay Networks
- 用参数化等音器设计可微分滤波器，共享频率和品质因数参数
- 仅需调整增益随延迟长度变化，减少参数量并保持性能
- 适合需要梯度优化的音频生成与混响模型开发者
我们提出一种基于反馈延迟网络（FDNs）的数字音频混响系统中衰减滤波器的新设计方法。采用二阶节数字无限脉冲响应（IIR）滤波器构成参数化等音器（PEQ），实现对频率相关混响衰减速率的精细控制。与传统图形均衡器需每条延迟线配置大量滤波器不同，本方法可灵活调节滤波器数量；频率、增益和品质因数（Q）参数在各延迟线间共享，仅增益根据延迟长度调整。该设计不仅大幅减少优化参数数量，且保持完全可微，兼容梯度学习框架。结合模拟滤波器设计原理，可通过监督学习高效准确地拟合滤波器参数。方法兼具灵活性与可微性，在达到当前最优性能的同时显著降低计算开销。
原文摘要 · Abstract (English)
We introduce a novel method for designing attenuation filters in digital audio reverberation systems based on Feedback Delay Networks (FDNs). Our approach uses Second Order Sections (SOS) of Infinite Impulse Response (IIR) filters arranged as parametric equalizers (PEQ), enabling fine control over frequency-dependent reverberation decay. Unlike traditional graphic equalizer designs, which require numerous filters per delay line, we propose a scalable solution where the number of filters can be adjusted. The frequency, gain, and quality factor (Q) parameters are shared parameters across delay lines and only the gain is adjusted based on delay length. This design not only reduces the number of optimization parameters, but also remains fully differentiable and compatible with gradient-based learning frameworks. Leveraging principles of analog filter design, our method allows for efficient and accurate filter fitting using supervised learning. Our method delivers a flexible and differentiable design, achieving state-of-the-art performance while significantly reducing computational cost.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。