Download Audio Processor Parameters: Estimating Distributions Instead of Deterministic Values Audio effects and sound synthesizers are widely used processors
in popular music.
Their parameters control the quality of the
output sound. Multiple combinations of parameters can lead to
the same sound.
While recent approaches have been proposed
to estimate these parameters given only the output sound, those
are deterministic, i.e. they only estimate a single solution among
the many possible parameter configurations.
In this work, we
propose to model the parameters as probability distributions instead
of deterministic values. To learn the distributions, we optimize
two objectives: (1) we minimize the reconstruction error between
the ground truth output sound and the one generated using the
estimated parameters, asisit usuallydone, but also(2)we maximize
the parameter diversity, using entropy. We evaluate our approach
through two numerical audio experiments to show its effectiveness.
These results show how our approach effectively outputs multiple
combinations of parameters to match one sound.