Download Differentiable Attenuation Filters for Feedback Delay Networks 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.
Download Gradient Descent Optimization of Room Impulse Responses with Parameter-Efficient Differentiable Feedback Delay Networks Artificial reverberation can be produced either by convolving a signal with a measured room impulse response (RIR) or by synthesizing it with a parametric algorithm such as a Feedback Delay Network (FDN). The former reproduces a captured space faithfully but is costly to run and offers no control over its acoustic properties, while the latter is efficient and editable but hard to match to a specific room. In this paper we bridge the two by fitting a fully differentiable FDN to a measured RIR through gradient descent. The proposed network uses sixteen delay lines at a sampling rate of 48 kHz and trains all of its components jointly, including the delay lengths, the feedback matrix, the early-reflection taps, and a set of attenuation filters that control the frequency-dependent decay.
Download Transformer-Based Plate Parameter Estimation with Differentiable and Particle-Swarm Refinement We present two Transformer-based methods for Task A of the 1st DAFx Parameter Estimation Challenge, which requires estimating six effective physical parameters of a synthetic plate-reverb model from its impulse response (IR). Method A1 combines an Audio Spectrogram Transformer encoder and Transformer regressor with differentiable IR refinement. Method A2 uses the same encoder to condition a continuous normalizing flow and refines sampled candidates using particle swarm optimisation (PSO) and gradient polishing. Both methods preserve the absolute IR scale to recover surface density. On a synthetic holdout set of 100 IRs, both refinement procedures reduce waveform and parameter errors by more than three orders of magnitude relative to the unrefined neural outputs. The PSO-based pipeline achieves the lowest errors, indicating near-perfect recovery in this matched synthetic setting.
Download Count-Density Networks for Modal Plate Parameter Estimation We describe two submissions to Task B of the 1st DAFx Parameter Estimation Challenge, which estimates an unknown number of modal frequency, decay, and gain triples from a synthetic plate-reverb impulse response. The first method combines pooled spectral features with time-domain and absolute-scale conditioning in a real-valued convolutional count-density network, while the second uses a complex-valued Transformer count-density network. Both methods jointly infer the modal count and per-mode attributes directly from the IR. On an independently generated 100-IR comparison set, the two neural estimators achieve lower overall challenge error than the evaluated classical baselines, with frequency and decay estimation substantially more accurate than gain estimation.