Download Diagonal Complex-Valued State Space Models for System Identification and Modeling of Metal Plate Reverbs Accurate and interpretable modeling of plate reverbs remains an important challenge in virtual analog modeling of audio effects. While existing neural network-based black-box approaches already achieve high-quality synthesis and strong perceptual quality, they often lack the possibility to identify the underlying physically meaningful complex, long-memory modal behavior. In this work, we address this limitation by proposing a restricted complex-valued diagonal State Space Model (SSM), showing its equivalence to a parallel second-order all-pole filter, also utilizing efficient training via parallel state computation using the parallel scan algorithm. Additionally, we propose a Matrix Pencil (MP) guided eigenvalue initialization, improving synthesis quality and system identification performance.
Download Non-iterative Modal Parameter Estimation for Plate Reverbs via Matrix-Pencil-Guided State Space Model Initialization Modal parameter identification for plate reverbs remains a challenging problem in virtual-analog audio effect emulation. Though neural network-based black-box approaches achieve high modeling accuracy, they generally lack interpretability and do not provide access to physically meaningful modal parameters. In this work, we present our solution to Task B of the DAFx Plate Reverb Parameter Estimation Challenge. Our method first estimates the total number of modes and then employs a Matrix Pencil (MP)-guided eigenvalue initialization strategy for a diagonal complex-valued State Space Model (SSM), which can be interpreted as a bank of parallel second-order all-pole filters. Exploiting the linearity of the resulting system, we compute the state impulse responses and replace gradient-based optimization with a closed-form least-squares estimation of the modal gains. The proposed approach enables accurate recovery of the modal parameters while maintaining an interpretable system representation.