KLANN: Linearising Long-Term Dynamics in Nonlinear Audio Effects Using Koopman Networks - Huhtala, Ville; Juvela, Lauri; Schlecht, Sebastian J. (IEEE Signal Processing Letters, 2024)
In recent years, neural network-based black-box modeling of nonlinear audio effects has improved considerably. Present convolutional and recurrent models can model audio effects with long-Term dynamics, but the models require many parameters, thus increasing the processing time. In this letter, we propose KLANN, a Koopman-Linearised Audio Neural Network structure that lifts a one-dimensional signal (mono audio) into a high-dimensional approximately linear state-space representation with nonlinear mapping, and then uses differentiable biquad filters to predict linearly within the lifted state-space. Results show that the proposed models match the high performance of the state-of-The-Art neural models while having a more compact architecture, reducing the number of parameters by tenfold, and having interpretable components.
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Keywords: Artificial neural networks; audio systems; deep learning; electronic music; nonlinear dynamical systems; system identification