DAFx-2020 - Vienna (virtual)

This paper proposes a neural network for carrying out parametric equalizer (EQ) matching. The novelty of this neural network solution is that it can be optimized directly in the frequency domain by means of differentiable biquads, rather than relying solely on a loss on parameter values which does not correlate directly with the system output. We compare the performance of the proposed neural network approach with that of a baseline algorithm based on a convex relaxation of the problem. It is observed that the neural network can provide better matching than the baseline approach because it directly attempts to solve the non-convex problem. Moreover, we show that the same network trained with only a parameter loss is insufficient for the task, despite the fact that it matches underlying EQ parameters better than one trained with a combination of spectral and parameter losses.

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