Improved online identification of acoustic MISO systems based on separated input signal components

Creating an immersive listening experience and providing the audience with improved spatial realism is the goal of many adaptive audio reproduction techniques such as room equalization or crosstalk cancellation. The majority of these approaches currently relies on acoustic impulse responses (AIRs) that have been measured prior to the actual audio reproduction. In order to maintain a high degree of adaptivity, however, the AIRs need to be estimated online during the reproduction process, which turns out to be a severely ill-conditioned problem due to the high inter-channel correlation of the loudspeaker signals. In this paper, we present a novel approach to MISO system identification with realistically correlated excitation. Based on the idea of separate treatment of correlated and uncorrelated signal components, we propose two extended filter structures for gradient-descent-based adaptive system identification and provide theoretical analysis and experimental validation of their effectiveness.