A Generalized Proportionate Subband Adaptive Second-Order Volterra Filter for Acoustic Echo Cancellation in Changing Environments

Burton, Trevor; Goubran, Rafik

doi:10.1109/tasl.2011.2134089

Cited by 26 publications

(9 citation statements)

References 22 publications

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“…Finally, we remark that the proposed method can be extended straightforwardly to include various conventional algorithms, e.g., proportionate NLMS for Volterra filters [7], [8], by introducing the variable-metric [48]. Such an extension will be discussed in other occasions.…”

Section: Resultsmentioning

confidence: 99%

“…where the system output z j is defined in (8), y j is the filter output, and the results are averaged over 100 runs. The system h and H are generated by (4) and (5), where s and S are generated from [−1, 1] uniform distribution with L = 15, and r is generated according to ITU-T G.168 [46] with M = 256 (see also Fig.…”

Section: Numerical Examplesmentioning

confidence: 99%

“…Since then, the "sparsity" has started to be exploited for accelerating the convergence speed of adaptive identification of the sparse AIR Manuscript [13], [14], and recently has been utilized for the steadystate performance improvements with use of the weighted 1 norm [15]- [17]. At the same time, it has been reported experimentally that the overall nonlinear echo path can also be simulated effectively by sparse second-order Volterra systems [7], [8], where we call the Volterra system is sparse if the coefficient vector and matrix are approximately sparse. Indeed, the sparsity in the Volterra system has also been exploited to improve the performance of the adaptive NLAEC algorithms [7], [8], [18], which suggests that we could improve further the performance of the adaptive NLAEC if we effectively utilize more precise features than the "sparsity" of the overall nonlinear echo path.…”

Section: Introductionmentioning

confidence: 99%

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Exploiting Group Sparsity in Nonlinear Acoustic Echo Cancellation by Adaptive Proximal Forward-Backward Splitting

Kuroda

Ono

Yamagishi

et al. 2013

IEICE Trans. Fundamentals

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In this paper, we propose a use of the group sparsity in adaptive learning of second-order Volterra filters for the nonlinear acoustic echo cancellation problem. The group sparsity indicates sparsity across the groups, i.e., a vector is separated into some groups, and most of groups only contain approximately zero-valued entries. First, we provide a theoretical evidence that the second-order Volterra systems tend to have the group sparsity under natural assumptions. Next, we propose an algorithm by applying the adaptive proximal forward-backward splitting method to a carefully designed cost function to exploit the group sparsity effectively. The designed cost function is the sum of the weighted group 1 norm which promotes the group sparsity and a weighted sum of squared distances to data-fidelity sets used in adaptive filtering algorithms. Finally, Numerical examples show that the proposed method outperforms a sparsity-aware algorithm in both the system-mismatch and the echo return loss enhancement. key words: group sparsity, nonlinear acoustic echo cancellation (NLAEC), adaptive Volterra filter, weighted group 1 norm, adaptive proximal forward-backward splitting (APFBS)

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Section: Resultsmentioning

confidence: 99%

Section: Numerical Examplesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Exploiting Group Sparsity in Nonlinear Acoustic Echo Cancellation by Adaptive Proximal Forward-Backward Splitting

Kuroda

Ono

Yamagishi

et al. 2013

IEICE Trans. Fundamentals

View full text Add to dashboard Cite

show abstract

“…The definition of ERLE is

ERLE = 10 normallg \frac{E ([], d^{2} ((), k))}{E ([], e^{2} ((), k))}

where d ( n ) is the signal at the microphone and e ( n ) is the residual signal at the adaptive filter output in Fig. .…”

Section: Experiments Setupmentioning

confidence: 99%

A joint echo cancellation algorithm for quick suppression of howls in hearing aids

Liang

Wang

et al. 2017

IEEJ Transactions Elec Engng

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When the echo path of a hearing aid suddenly changes, howls easily occur. To quickly suppress the howls, a joint echo cancellation (JEC) algorithm, which combines the variable step normalized least mean square (VNLMS) algorithm with the notch filter algorithm, is proposed. According to whether the hearing aid howls or not, different strategies are used. First, when there are no howls, the echo signal is estimated using VNLMS and the step factor is computed according to three types of filter states, which are defined based on the normalized distance between the short-term average and the long-term average of the filter coefficients. Then, different step factors are used for different states. Second, when there are howls, the update of VNLMS is frozen to stabilize the howl frequency. To improve the detection accuracy, a howling detection algorithm based on the zoom-fast Fourier transformation (ZoomFFT) is proposed. The ZoomFFT algorithm can analyze the spectrum of a narrowband signal in a specified high sampling frequency. Then, the notch filters based on the estimated howl frequencies are dynamically generated to restrain the howls. Finally, when the howls are suppressed, VNLMS is reactivated. Compared to other echo cancellation algorithms, the proposed algorithm can quickly suppress the howls, and JEC has the best comprehensive performance. Furthermore, the quality of the processed speech is high, and the operation time is short. Thus, the proposed algorithm is suitable for low-power-consumption and small-volume products such as hearing aids.

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“…2, the input-output relationship can be rewritten as (31) where, according to the definitions in (16), (17) and (18), (19), and . The REMFN filter is then updated using the output-error method (32) If simplified REMFN filters are used, the basis functions in become those given in the Appendix.…”

Section: Adaptation Of Remfn Filtersmentioning

confidence: 99%

Recursive Even Mirror Fourier Nonlinear Filters and Simplified Structures

Carini

Sicuranza

2014

IEEE Trans. Signal Process.

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In this paper, a novel class of recursive nonlinear filters is presented as an extension of finite-memory even mirror Fourier nonlinear filters. These filters are characterized by two relevant properties: i) they are able to arbitrarily well approximate any discrete-time, time-invariant, causal, infinite-memory, continuous, nonlinear system and ii) they are always stable according to the bounded-input-bounded-output criterion. Even though recursive models can represent many systems with fewer coefficients than their finite-memory counterparts, it is still possible to further reduce their computational complexity. In fact, while in general simplified structures lead to a loss of performance, it is pointed out in the paper that in various common real-world situations, they are able of giving remarkable complexity reductions without negatively affecting the modeling capabilities.Index Terms-BIBO stability, nonlinear systems, recursive even mirror Fourier nonlinear filters, simplified structures, universal approximators. 1053-587X

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A Generalized Proportionate Subband Adaptive Second-Order Volterra Filter for Acoustic Echo Cancellation in Changing Environments

Cited by 26 publications

References 22 publications

Exploiting Group Sparsity in Nonlinear Acoustic Echo Cancellation by Adaptive Proximal Forward-Backward Splitting

Exploiting Group Sparsity in Nonlinear Acoustic Echo Cancellation by Adaptive Proximal Forward-Backward Splitting

A joint echo cancellation algorithm for quick suppression of howls in hearing aids

Recursive Even Mirror Fourier Nonlinear Filters and Simplified Structures

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