Kalman filter-based microphone array signal processing using the equivalent source model

This paper demonstrates that microphone array signal processing can be implemented by using adaptive model-based filtering approaches. Nearfield and farfield sound propagation models are formulated into state-space forms in light of the Equivalent Source Method (ESM). In the model, the unknown source amplitudes of the virtual sources are adaptively estimated by using Kalman filters (KFs). The nearfield array aimed at noise source identification is based on a Multiple-Input–Multiple-Output (MIMO) state-space model with minimal realization, whereas the farfield array technique aimed at speech quality enhancement is based on a Single-Input–Multiple-Output (SIMO) state-space model. Performance of the nearfield array is evaluated in terms of relative error of the velocity reconstructed on the actual source surface. Numerical simulations for the nearfield array were conducted with a baffled planar piston source. From the error metric, the proposed KF algorithm proved effective in identifying noise sources. Objective simulations and subjective experiments are undertaken to validate the proposed farfield arrays in comparison with two conventional methods. The results of objective tests indicated that the farfield arrays significantly enhanced the speech quality and word recognition rate. The results of subjective tests post-processed with the analysis of variance (ANOVA) and a post-hoc Fisher's least significant difference (LSD) test have shown great promise in the KF-based microphone array signal processing techniques.