Adaptive Combination of Volterra Kernels and Its Application to Nonlinear Acoustic Echo Cancellation

Azpicueta-Ruiz, Luis A.; Zeller, Marcus; Figueiras-Vidal, Anı́bal R.; Arenas‐García, Jerónimo; Kellermann, Walter

doi:10.1109/tasl.2010.2045185

Cited by 110 publications

(75 citation statements)

References 32 publications

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“…These combined schemes are introduced to improve robustness when several kinds of adverse scenario conditions can impair the filter performance, and to facilitate the selection of filter parameters, alleviating the different trade-offs inherit to adaptive filters, for instance the well-know speed of convergence vs steady-state misadjustment compromise [26]. Combination of adaptive filters have been successfully employed in different signal processing applications, including system identification [26], [27], signal modality characterization [28], array beamforming [29], [30], adaptive line enhancement [31], and acoustic applications, such as AEC [32]- [35] and ANC [23], [36], [37].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Filtered-x Algorithms for Room Equalization Based on Block-Based Combination Schemes

Fúster

Diego

Azpicueta-Ruiz

et al. 2016

IEEE/ACM Trans. Audio Speech Lang. Process.

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Abstract-Room equalization has become essential for sound reproduction systems to provide the listener with the desired acoustical sensation. Recently, adaptive filters have been proposed as an effective tool in the core of these systems. In this context, this paper introduces different novel schemes based on the combination of adaptive filters idea: a versatile and flexible approach that permits obtaining adaptive schemes combining the capabilities of several independent adaptive filters.In this way, we have investigated the advantages of a scheme called combination of block-based adaptive filters which allows a blockwise combination splitting the adaptive filters into nonoverlapping blocks. This idea was previously applied to the plant identification problem, but has to be properly modified to obtain a suitable behavior in the equalization application. Moreover, we propose a scheme with the aim of further improving the equalization performance using the a priori knowledge of the energy distribution of the optimal inverse filter, where the block filters are chosen to fit with the coefficients energy distribution. Furthermore, the biased block-based filter is also introduced as a particular case of the combination scheme, especially suited for low signal-to-noise ratios (SNRs) or sparse scenarios. Although the combined schemes can be employed with any kind of adaptive filter, we employ the filtered-x improved proportionate normalized least mean square (Fx-IPNLMS) algorithm as basis of the proposed algorithms, allowing to introduce a novel combination scheme based on partitioned block schemes where different blocks of the adaptive filter use different parameter settings. Several experiments are included to evaluate the proposed algorithms in terms of convergence speed and steady-state behavior for different degrees of sparseness and SNRs.

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Section: Introductionmentioning

confidence: 99%

Adaptive Filtered-x Algorithms for Room Equalization Based on Block-Based Combination Schemes

Fúster

Diego

Azpicueta-Ruiz

et al. 2016

IEEE/ACM Trans. Audio Speech Lang. Process.

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“…Other solutions have been proposed, such as data reusing (DR) [22][23][24][25], mixed-norm updates [26][27][28][29], and variable step size (VSS) [30][31][32]. Combinations of AFs, however, have shown several advantages over these techniques in terms of performance and robustness [33][34][35][36][37][38][39][40][41][42][43][44][45][46][47][48][49]. In some cases, it even provides a more general framework under which these adaptive algorithms can be cast (as argued in Chapter 8).…”

Section: Motivationmentioning

confidence: 99%

“…A particularly important aspect of AFs is their adaptive algorithm [18,19] and a classification based on this feature is found in Table 2. Notable cases that did not fit in this table include IPNLMS combinations [36], the LMS + sign-error LMS (RMN) [29], APA combinations [37,46,49], and combinations involving nonlinear AFs [40,[125][126][127].…”

Section: Component Filtersmentioning

confidence: 99%

“…This is by no means restrictive as larger parallel structures can be built by combining two-AFs structures hierarchically [36,40,46,137]. For example, a parallel combination of four AFs can be constructed using two combinations of two AFs combined as if they were component filters ( Figure 13).…”

Section: Supervising Rulesmentioning

confidence: 99%

“…After all, combinations rely on the operation of more than one AF and therefore necessarily require more operations that any of their individual components. In fact, the established consensus is that a combination has the computational complexity of the sum of its components (given the low complexity of the typical supervisors) [33,35,40,45,47,73,92,96,133].…”

Section: Combination As a Complexity-reduction Techniquementioning

confidence: 99%

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Combinations of adaptive filters.

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A robust adaptive decomposable Volterra filter based on the hyperbolic tangent Leclerc function and its performance analysis

Liu,

Li,

2024

Adaptive Control & Signal

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SummaryMost of the existing adaptive filter algorithms pay more attention to improving performance while ignoring the computational complexity and the impact of the impulsive environment. When encountering the impulsive noise environments, the performance of traditional nonlinear adaptive filter may be significantly reduced and usually needs high computational cost. Therefore, this article proposes a hyperbolic tangent Leclerc robust nonlinear adaptive filter based on the low complexity decomposable Volterra model (HTLNAF‐DVM). The filter is implemented by imposing a rank‐one structure on the full Volterra model to get a product of linear filters, and employs a hyperbolic tangent Leclerc function as a robust norm to effectively improve the robustness against the impulsive noise. In addition, we give the theoretical analyses of the steady‐state mean‐square performance of the proposed HTLNAF‐DVM. Finally, the simulation results prove that the proposed HTLNAF‐DVM algorithm has better performance than the existing algorithms and fit well with the theory.

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Adaptive Combination of Volterra Kernels and Its Application to Nonlinear Acoustic Echo Cancellation

Cited by 110 publications

References 32 publications

Adaptive Filtered-x Algorithms for Room Equalization Based on Block-Based Combination Schemes

Adaptive Filtered-x Algorithms for Room Equalization Based on Block-Based Combination Schemes

Combinations of adaptive filters.

A robust adaptive decomposable Volterra filter based on the hyperbolic tangent Leclerc function and its performance analysis

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