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Bachelor-Vortrag: Significance-Aware Filtering for Nonlinear Acoustic Echo Cancellation

Tom Deckenbrunnen
Dienstag, 10. November 2020
10:00 Uhr
virtueller Konferenzraum

The acoustic echoes arising in hands-free applications for mobile speech communication contain considerable nonlinear distortions of the far-end signal. Some causes of these nonlinearities include the progressive miniaturization of components and the need for high play-back volume, two opposing paradigm. It is therefore necessary to use methods of nonlinear acoustic echo cancellation to provide sufficient quality of communication. However, the limited resources in mobile devices render many of these methods to alleviating the high computational complexity.

One approach to alleviating the high computational demand is the so-called Significance-Aware filtering. Specifically, the cascaded models based on parallel Significance-Aware decomposition of the system identification task are treated in this thesis. The aim of this thesis is the evaluation and enhancement of those structures. In particular, methods for incorporating nonlinear memory into the parallel Significance-Aware models are proposed. Beyond that, an analysis of the necessary adaptation control for these structures is offered.

Results show that better performance can be achieved by the addition of nonlinear memory to the parallel Significance-Aware models. It is furthermore shown that the proposed enhancements solve a specific problem of adaptation control. Nevertheless, adaptation control remains a major point of interest for the parallel Significance-Aware models.

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