Network Architectures for Manifold Learning in MIMO Acoustic System Identification

Authors:

Hardenbicker, T. ,  Hahn, J. ,  Jax, P.

Book Title:

2025 33rd European Signal Processing Conference (EUSIPCO)

Organization:

IEEE

Pages:

p.p. 396-400

Date:

Sep. 2025

DOI:

10.23919/EUSIPCO63237.2025.11226195

Language:

English

Abstract

Identification of linear time-varying acoustic systems with multiple inputs and outputs is required in signal processing tasks like echo cancellation or crosstalk cancellation. When all inputs are excited simultaneously, identification is difficult because each output is a superposition of the influence of all inputs. If the inputs are correlated, identification is even more difficult due to the so-called non-uniqueness problem. A recent approach uses an extended Kalman filter to identify acoustic systems on nonlinear lower-dimensional manifolds. We extend this approach to MIMO systems. Instead of simply increasing the size of the neural networks, we propose architectural variants to control the number of parameters. We show that restricting the size of the network in exchange for its flexibility is beneficial for online system identification.

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