Decorrelation of audio signals is a critical step for spatial sound reproduction on multichannel configurations. Correlated signals yield a focused phantom source between the reproduction loudspeakers and may produce undesirable comb-filtering artifacts when the signal reaches the listener with small phase differences. Decorrelation techniques reduce such artifacts and extend the spatial auditory image by randomizing the phase of a signal while minimizing the spectral coloration. This paper proposes a method to optimize the decorrelation properties of a sparse noise sequence, called velvet noise, to generate short sparse FIR decorrelation filters. The sparsity allows a highly efficient time-domain convolution. The listening test results demonstrate that the proposed optimization method can yield effective and colorless decorrelation filters. In comparison to a white noise sequence, the filters obtained using the proposed method preserve better the spectrum of a signal and produce good quality broadband decorrelation while using 76% fewer operations for the convolution. Satisfactory results can be achieved with an even lower impulse density which decreases the computational cost by 88%.

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Recent research in deep learning has shown that neural networks can learn differential equations governing dynamical systems. In this paper, we adapt this concept to Virtual Analog (VA) modeling to learn the ordinary differential equations (ODEs) governing the first-order and the second-order diode clipper. The proposed models achieve performance comparable to state-of-the-art recurrent neural networks (RNNs) albeit using fewer parameters. We show that this approach does not require oversampling and allows to increase the sampling rate after the training has completed, which results in increased accuracy. Using a sophisticated numerical solver allows to increase the accuracy at the cost of slower processing. ODEs learned this way do not require closed forms but are still physically interpretable.

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