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.