  Improving the interpretability of deep neural networks has recently gained
increased attention, especially when the power of deep learning is leveraged to
solve problems in physics. Interpretability helps us understand a model's
ability to generalize and reveal its limitations. In this paper, we introduce a
causal interpretable deep structure for modeling dynamic systems. Our proposed
model makes use of the harmonic analysis by modeling the system in a
time-frequency domain while maintaining high temporal and spectral resolution.
Moreover, the model is built in an order recursive manner which allows for
fast, robust, and exact second order optimization without the need for an
explicit Hessian calculation. To circumvent the resulting high dimensionality
of the building blocks of our system, a neural network is designed to identify
the frequency interdependencies. The proposed model is illustrated and
validated on nonlinear system identification problems as required for audio
signal processing tasks. Crowd-sourced experimentation contrasting the
performance of the proposed approach to other state-of-the-art solutions on an
acoustic echo cancellation scenario confirms the effectiveness of our method
for real-life applications.
