Differentiable Attenuation Filters for Feedback Delay Networks
This work addresses the need for scalable and efficient filter design in audio processing, offering a differentiable solution that benefits audio engineers and machine learning practitioners, though it is incremental in improving existing FDN methods.
The paper tackles the problem of designing attenuation filters for digital audio reverberation systems using Feedback Delay Networks by introducing a novel method based on Second Order Sections of IIR filters arranged as parametric equalizers, achieving state-of-the-art performance with reduced computational cost.
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.