Download Differentiable All-Pole Filters for Time-Varying Audio Systems Infinite impulse response filters are an essential building block of many time-varying audio systems, such as audio effects and synthesisers. However, their recursive structure impedes end-toend training of these systems using automatic differentiation. Although non-recursive filter approximations like frequency sampling and frame-based processing have been proposed and widely used in previous works, they cannot accurately reflect the gradient of the original system. We alleviate this difficulty by reexpressing a time-varying all-pole filter to backpropagate the gradients through itself, so the filter implementation is not bound to the technical limitations of automatic differentiation frameworks. This implementation can be employed within audio systems containing filters with poles for efficient gradient evaluation. We demonstrate its training efficiency and expressive capabilities for modelling real-world dynamic audio systems on a phaser, time-varying subtractive synthesiser, and feed-forward compressor. We make our code and audio samples available and provide the trained audio effect and synth models in a VST plugin1 .
Download DiffVox: A Differentiable Model for Capturing and Analysing Vocal Effects Distributions This study introduces a novel and interpretable model, DiffVox,
for matching vocal effects in music production. DiffVox, short
for “Differentiable Vocal Fx", integrates parametric equalisation,
dynamic range control, delay, and reverb with efficient differentiable implementations to enable gradient-based optimisation for
parameter estimation. Vocal presets are retrieved from two datasets,
comprising 70 tracks from MedleyDB and 365 tracks from a private collection. Analysis of parameter correlations reveals strong
relationships between effects and parameters, such as the highpass and low-shelf filters often working together to shape the low
end, and the delay time correlating with the intensity of the delayed signals. Principal component analysis reveals connections to
McAdams’ timbre dimensions, where the most crucial component
modulates the perceived spaciousness while the secondary components influence spectral brightness. Statistical testing confirms
the non-Gaussian nature of the parameter distribution, highlighting
the complexity of the vocal effects space. These initial findings on
the parameter distributions set the foundation for future research
in vocal effects modelling and automatic mixing.