A Generalized FLANN Filter for Nonlinear Active Noise Control

Sicuranza, Giovanni L.; Carini, Alberto

doi:10.1109/tasl.2011.2136336

Cited by 163 publications

(81 citation statements)

References 9 publications

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“…This asymmetric weighting will make the filter converge faster in situations where the optimal solution is sparse (or quasisparse), in contrast with the original SFLAF, which has a uniform weighting for all the functional links. The elements of the proportionate matrix (16) are derived from the improved proportionate normalized least mean square (IPNLMS) algorithm [30] with application to the proposed SFLAF model, thus yielding: 17) where the scalar ξ is a small positive value avoiding divisions by zero. In (17), the proportionality factors −1 ≤ α L , α FL ≤ 1 have the task of balancing the proportionality.…”

Section: B Derivation Of the Proportionate Matrixmentioning

confidence: 99%

“…The elements of the proportionate matrix (16) are derived from the improved proportionate normalized least mean square (IPNLMS) algorithm [30] with application to the proposed SFLAF model, thus yielding: 17) where the scalar ξ is a small positive value avoiding divisions by zero. In (17), the proportionality factors −1 ≤ α L , α FL ≤ 1 have the task of balancing the proportionality. In fact, when they assume a value close to 1 a high degree of sparseness is expected both for the functional link expansion and for the linear branch, while, on the contrary, a low degree is expected when the proportionality factors are close to −1, thus reducing in the limit the adaptation to a normalized least mean square (NLMS) algorithm.…”

Section: B Derivation Of the Proportionate Matrixmentioning

confidence: 99%

“…In particular, we consider the class of functional link adaptive filters (FLAFs). The FLAF was firstly introduced as an effective method for nonlinear modeling [1], [16], [17]. The split FLAF (SFLAF) scheme was proposed to better exploit the capabilities of linear and nonlinear filtering in parallel.…”

Section: Introductionmentioning

confidence: 99%

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Full proportionate functional link adaptive filters for nonlinear acoustic echo cancellation

Comminiello

Scarpiniti

Azpicueta-Ruiz

et al. 2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-Nonlinear acoustic echo cancellation (NAEC) can be mainly addressed by solving two different sub-problems: the estimation of the acoustic impulse response and the modeling of the nonlinearities rebounding in it, mostly caused by the electroacoustic chain. Both the modeling processes share an important characteristic: the majority of the parameters to be estimated are very close to zero, with only a small fraction of them having non-negligible magnitude. In this paper, a novel NAEC model is proposed taking into account both the above sub-problems under a joint optimization problem. In particular, the proposed model involves two separate and parallel filters, one mainly focusing on the estimation of the acoustic impulse response (AIR) and the other one aiming at the nonlinear modeling. In order to optimize the modeling processes, both the filters are adapted by using a joint proportionate algorithm. Experimental results prove the effectiveness of the proposed model in NAEC problems.

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Section: B Derivation Of the Proportionate Matrixmentioning

confidence: 99%

Section: B Derivation Of the Proportionate Matrixmentioning

confidence: 99%

See 1 more Smart Citation

Full proportionate functional link adaptive filters for nonlinear acoustic echo cancellation

Comminiello

Scarpiniti

Azpicueta-Ruiz

et al. 2017

2017 25th European Signal Processing Conference (EUSIPCO)

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show abstract

“…Then, the transformed signal can be processed by a linear model. In particular, we take into account the nonlinear functional link adaptive filters (FLAFs) [3], in which the nonlinear expansion is carried out by the so-called functional links [14,15,19,1], and the subsequent linear model is an adaptive filter.…”

Section: Introductionmentioning

confidence: 99%

Online Selection of Functional Links for Nonlinear System Identification

Comminiello

Scardapane

Scarpiniti

et al. 2015

Advances in Neural Networks: Computational and Theoretical Issues

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This paper introduces a new method for improving nonlinear modeling performance in online learning by using functional link-based models. The proposed algorithm is capable of selecting the useful nonlinear elements resulting from the functional expansion, while setting to zero the ones that does not bring any improvement of the modeling performance. This allows to reduce any gradient noise due to a possible overestimate of the solution, thus preventing any overfitting phenomena. The proposed model is assessed in several nonlinear identification problems, including different levels of nonlinearity, showing significant improvements

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“…Standard FLANN filter strategy does not use products of input samples with different time shifts, thus, its performance can be deteriorated in some situations. To alleviate this problem, different modifications, such as the generalized FLANN (GFLANN) [53], the completed FLANN (CFLANN) [54] and finally the Fourier nonlinear (FN) filter [55], have been proposed with application to nonlinear active noise control. However, when used in room equalization, the nonlinear expansion of the input signal of order P produces M = 2P +1 functions that have to be filtered through the estimated acoustic channel for its use in the nonlinear filtered-x algorithm.…”

Section: Z(n)mentioning

confidence: 99%

Linear and nonlinear room compensation of audio rendering systems

Criado¹

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Common audio systems are designed with the intent of creating real and immersive scenarios that allow the user to experience a particular acoustic sensation that does not depend on the room he is perceiving the sound. However, acoustic devices and multichannel rendering systems working inside a room, can impair the global audio effect and thus the 3D spatial sound.In order to preserve the spatial sound characteristics of multichannel rendering techniques, adaptive filtering schemes are presented in this dissertation to compensate these electroacoustic effects and to achieve the immersive sensation of the desired acoustic system. Adaptive filtering offers a solution to the room equalization problem that is doubly interesting. First of all, it iteratively solves the room inversion problem, which can become computationally complex to obtain when direct methods are used. Secondly, the use of adaptive filters allows to follow the time-varying room conditions.In this regard, adaptive equalization (AE) filters try to cancel the echoes due to the room effects. In this work, we consider this problem and propose effective and robust linear schemes to solve this equalization problem by using adaptive filters. To do this, different adaptive filtering schemes are introduced in the AE context. These filtering schemes are based on three strategies previously introduced in the literature: the convex combination of filters, the biasing of the filter weights and the block-based filtering. More specifically, and motivated by the sparse nature of the acoustic impulse response and its corresponding optimal inverse filter, we introduce different adaptive equalization algorithms.In addition, since audio immersive systems usually require the use of multiple transducers, the multichannel adaptive equalization problem should be also taken into account when new single-channel approaches are presented, in the sense that they can be straightforwardly extended to the ii Abstract multichannel case.On the other hand, when dealing with audio devices, consideration must be given to the nonlinearities of the system in order to properly equalize the electroacoustic system. For that purpose, we propose a novel nonlinear filtered-x approach to compensate both room reverberation and nonlinear distortion with memory caused by the amplifier and loudspeaker devices.Finally, it is important to validate the algorithms proposed in a realtime implementation. Thus, some initial research results demonstrate that an adaptive equalizer can be used to compensate room distortions. ResumenLos sistemas de audio actuales están diseñados con la idea de crear escenarios reales e inmersivos que permitan al usuario experimentar determinadas sensaciones acústicas que no dependan de la sala o situación donde se esté percibiendo el sonido. Sin embargo, los dispositivos acústicos y los sistemas multicanal funcionando dentro de salas, pueden perjudicar el efecto global sonoro y de esta forma, el sonido espacial 3D.Para poder preservar las características espaciales ...

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A Generalized FLANN Filter for Nonlinear Active Noise Control

Cited by 163 publications

References 9 publications

Full proportionate functional link adaptive filters for nonlinear acoustic echo cancellation

Full proportionate functional link adaptive filters for nonlinear acoustic echo cancellation

Online Selection of Functional Links for Nonlinear System Identification

Linear and nonlinear room compensation of audio rendering systems

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