A Flexible Framework for Expectation Maximization-Based MIMO System Identification for Time-Variant Linear Acoustic Systems

IEEE Open Journal of Signal Processing, pp. 1–9

Abstract

Quasi-continuous system identification of time-variant linear acoustic systems can be applied in various audio signal processing applications when numerous acoustic transfer functions must be measured. A prominent application is measuring head-related transfer functions. We treat the underlying multiple-input-multiple-output (MIMO) system identification problem in a state-space model as a joint estimation problem for states, representing impulse responses, and state-space model parameters using the expectation maximization (EM) algorithm. We address limitations of prior work by imposing different model structures, especially for dependencies within a (transformed) state vector. This results in block diagonal matrix structures, for which we derive M-step update rules. Making assumptions about this model structure and choosing a block size for a given application define the computational complexity. In examples, we found that applying this framework yields improvements of up to 10 dB in relative system distance in comparison to a conventional method.

Authors 2

1. Tobias Kabzinski Aachen Institute of Communication Systems

   RWTH Aachen University

   Affiliation as printed

   Institute of Communication Systems (IKS), RWTH Aachen University, Aachen, Germany

2. Peter Jax Aachen Institute of Communication Systems

   RWTH Aachen University

   Affiliation as printed

   Institute of Communication Systems (IKS), RWTH Aachen University, Aachen, Germany