Convex combination of MIMO filters for multichannel acoustic echo cancellation

Abstract

This paper introduces a new framework for multichannel adaptive filtering, aiming at improving performance of an overall filtering system. The proposed architecture relies on the properties of the adaptive combination of filters which exploits the capabilities of different constituents, thus adaptively providing at least the behaviour of the best performing filter. Applying this concept to multichannel filtering systems, we define a scheme for the combination of multiple-input multiple-output (MIMO) filters. More precisely the proposed structure involves the combination of two different multiple-input singleoutput (MISO) systems for each MIMO output. We propose such framework with application to the multichannel acoustic echo cancellation (MAEC) with the goal of giving robustness to the system against impulsive background noise, and thus improving overall cancelling performance. Experimental results show the effectiveness of the proposed combined MAEC in the presence of adverse environmental conditions. © 2013 University of Trieste and University of Zagreb

Steady-State Performance of an Adaptive Combined MISO Filter Using the Multichannel Affine Projection Algorithm

“…n (for the simplicity of notation, we omit the projection order subscript for k = 0), and it is the same for both MISO filters. Moreover, in (11)…”

“…One solution to this problem is represented by the adaptive combination of adaptive filters [1][2][3][4][5][6], which exploits the diversity of parallel adaptive branches. Combined structures of filtering have been applied to a wide variety of online adaptive filtering applications [7][8][9][10][11][12][13][14][15] due to their capabilities of detecting and selecting instant by instant the best-performing filter. To this end, the analysis of the mean squared performance aims at showing the effectiveness of adaptive combined schemes and how to exploit them the best [6,[16][17][18][19][20].…”

“…MISO filters are widely used in several applications, such as adaptive beamforming [21,22], OFDM systems [23], channel equalization [24], and audio and speech applications [25,26]. These have been chosen as a baseline scheme of the multichannel case, but the combined MISO structure, and the related analysis thereof, can also be easily extended to multiple-input multiple-output (MIMO) filters, as in [11], since an MIMO filter can be seen as a parallel of MISO filters. In order to adapt MISO filters, we adopt a multichannel affine projection algorithm (APA) [27,28], which, being characterized by a projection order, allows us to generalize the analysis with respect to both gradient-based and Hessian-based adaptive algorithms.…”