Title: Adaptive Filtering Algorithms for Acoustic Echo Cancellation and Acoustic Feedback Control in Speech Communication Applications (Adaptieve-filteralgoritmen voor akoestische-echo-onderdrukking en akoestische-feedbackbeheersing in spraakcommunicatietoepassingen)
Other Titles: Adaptive Filtering Algorithms for Acoustic Echo Cancellation and Acoustic Feedback Control in Speech Communication Applications
Authors: Gil-Cacho Lorenzo, Jose Manuel; S0198452
Issue Date: 16-Dec-2013
Abstract: Multimedia consumer electronics are nowadays everywhere from teleconferencing, hands-free communications, in-car communications to smart TV applications and more. We are living in a world of telecommunication where ideal scenarios for implementing these applications are hard to find. Instead, practical implementations typically bring many problems associated to each real-life scenario. This thesis mainly focuses on two of these problems, namely, acoustic echo and acoustic feedback. On the one hand, acoustic echo cancellation (AEC) is widely used in mobile and hands-free telephony where the existence of echoes degrades the intelligibility and listening comfort. On the other hand, acoustic feedback limits the maximum amplification that can be applied in, e.g., in-car communications or in conferencing systems, before howling due to instability, appears. Even though AEC and acoustic feedback cancellation (AFC) are functional in many applications, there are still open issues. This means that many of the issues associated to practical AEC and AFC are overlooked. In this thesis, we contribute to the development of a number of algorithms to tackle the main issues associated to AEC and AFC namely, (1) that very long room impulse responses (RIRs) make standard adaptive filters converge slowly and lead to a high computational complexity, (2) that double-talk (DT) situations in AEC and model mismatch in AFC distort the near-end signal , and (3) that the nonlinear response of some of the elements forming part of the audio chain makes linear adaptive filters fail. In the view of computational complexity reduction, we consider introducing the concept of common-acoustical-pole room modeling into AEC. To this end, we perform room transfer function (RTF) equalization by compensating for the main acoustic resonances common to multiple RTFs in the room. We discuss the utilization of different norms (i.e., 2-norm and 1-norm) and models (i.e., all-pole and pole-zero) for RTF modeling and equalization. A computationally cheap extension from single-microphone AEC to multi-microphone AEC is then presented for the case of a single loudspeaker. The RTF models used for multi-microphone AEC share a fixed common denominator polynomial, which is calculated off-line by means of a multi-channel warped linear prediction. This allows high computational savings. In the context of acoustic feedback control, we develop a method for acoustic howling suppression based on adaptive notch filters (ANF) with regularization (RANF). This method achieves frequency tracking, howling suppression and improved howling detection in a one-stage scheme. The RANF approach to howling suppression introduces minimal processing delay and minimal complexity, in contrast to non-parametric block-based methods that feature a non-parametric frequency analysis in a two-stage scheme.To tackle the issue of robustness to double-talk (DT) in AEC and robustness to model mismatch in AFC, a new adaptive filtering framework is proposed. It is based on the frequency-domain adaptive filtering (FDAF) implementation of the so-called PEM-AFROW (FDAF-PEM-AFROW) algorithm. We show that FDAF-PEM-AFROW is related to the best, i.e., minimum-variance, linear unbiased estimate (BLUE) of the echo path. We derive and define the instantaneous pseudo-correlation (IPC) measure between the near-end signal and the loudspeaker signal. The IPC measure serves as an indication of the effect of a DT situation occurring during adaptation due to the correlation between these two signals. Based on the good results obtained using FDAF-PEM-AFROW, we derive a practical and computationally efficient algorithm for DT-robust AEC and for AFC. The proposed algorithm features two modifications in the FDAF-PEM-AFROW: (a) the WIener variable Step sizE (WISE), and (b) the GRAdient Spectral variance Smoothing (GRASS), leading to the WISE-GRASS-FDAF-PEM-AFROW. The WISE modification is implemented as a single-channel noise reduction Wiener filter where the Wiener filter gain is used as a variable step size in the adaptive filter. On the other hand, the GRASS modification aims at reducing the variance in the noisy gradient estimates based on time-recursive averaging of instantaneous gradients.In the last part of this thesis, the nonlinear response of (active) loudspeakers forming part of the audio chain is studied. We consider the description of odd and even nonlinearities in (active) loudspeakers by means of periodic random-phase multisine signals. The fact that the odd nonlinear contributions are more predominant than the even ones implies that at least a 3rd-order nonlinear model of the loudspeaker should be used. Therefore, we consider the identification and validation of a model of the loudspeaker using several linear-in-the-parameters nonlinear adaptive filters, namely, Hammerstein and Legendre polynomial filters of various orders, and a simplified $3$rd-order Volterra filter of various lengths. In our measurement set-up, the obtained results imply however, that a $3$rd-order nonlinear filter fails to capture all the nonlinearities, meaning that odd and even nonlinear contributions are produced by higher-order nonlinearities. High-order Volterra filters are impractical in AEC and AFC due to their large computational complexity together with inherently slow convergence. On the other hand, the kernel affine projection algorithm (KAPA) has been successfully applied to many areas in signal processing but not yet to nonlinear AEC (NLAEC). In KAPA, and kernel methods in general, the kernel trick is applied to work implicitly in a high-dimensional (possibly infinite-dimensional) space without having to transform the input data into this space. This is one of the most appealing characteristics of kernel methods, as opposed to nonlinear adaptive filters requiring explicit nonlinear expansions of the input data as, for instance, Volterra filters. In fact, all computations can be done by evaluating the kernel function in the input space. This fact provides powerful modeling capabilities to kernel adaptive algorithms where the computational complexity will be determined by the input dimension, independent of the order of the nonlinearity. Our contributions in this context are to apply KAPA to the NLAEC problem, to develop a sliding-window leaky KAPA (SWL-KAPA) that is well suited for NLAEC applications, and to propose a suitable kernel function, consisting of a weighted sum of a linear and a Gaussian kernel.
Publication status: published
KU Leuven publication type: TH
Appears in Collections:ESAT - STADIUS, Stadius Centre for Dynamical Systems, Signal Processing and Data Analytics
Electrical Engineering (ESAT) TC, Technology Campus Geel
Technologiecluster ESAT Elektrotechnische Engineering

Files in This Item:

There are no files associated with this item.

Request a copy


All items in Lirias are protected by copyright, with all rights reserved.