Efficient FPGA implementation of the DCD-RLS algorithm for stereo acoustic echo cancellation

Stanciu, Cristian; Anghel, Cristian; Stanciu, Lucian

doi:10.1109/isscs.2015.7204008

Cited by 6 publications

(4 citation statements)

References 7 publications

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“…The forgetting factors for the adaptive filters are chosen as λ i = 1 − 1/(2 k i L i ) for the tensor-based methods, respectively, as λ = 1 − 1/(2 k L) for the classical RLS. The forms facilitate efficient hardware implementations of multiplications [20,32]. For a similar reason, the filter lengths have the form L i = 2 q i , respectively, L = 2 q .…”

Section: Simulation Resultsmentioning

confidence: 99%

Tensor-Based Recursive Least-Squares Adaptive Algorithms with Low-Complexity and High Robustness Features

et al. 2022

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The recently proposed tensor-based recursive least-squares dichotomous coordinate descent algorithm, namely RLS-DCD-T, was designed for the identification of multilinear forms. In this context, a high-dimensional system identification problem can be efficiently addressed (gaining in terms of both performance and complexity), based on tensor decomposition and modeling. In this paper, following the framework of the RLS-DCD-T, we propose a regularized version of this algorithm, where the regularization terms are incorporated within the cost functions. Furthermore, the optimal regularization parameters are derived, aiming to attenuate the effects of the system noise. Simulation results support the performance features of the proposed algorithm, especially in terms of its robustness in noisy environments.

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Section: Simulation Resultsmentioning

confidence: 99%

Tensor-Based Recursive Least-Squares Adaptive Algorithms with Low-Complexity and High Robustness Features

et al. 2022

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“…Correspondingly, R(n) can be updated by exploiting its highly redundant structure. The matrix is Hermitian, and every consecutive two columns, starting from the first one, comprise the same information, each pair of values being conjugated and having switched positions in the neighboring column [2,19,33]. The same rules apply to the numerical composition of the rows.…”

Section: Step Actions ×mentioning

confidence: 99%

“…The latter will be employed again in the next adaptive filter iteration (in step 5). Moreover, considering the unknown complex-valued system as being fixed for a given number of iterations, as the adaptive filter converges, the absolute values of the real and imaginary parts comprising r(n) tend to decrease [33]. The task in step 6 is suitable for LSMs, which can compute both mentioned vectors, ∆ h(n) and r(n).…”

Section: Step Actions ×mentioning

confidence: 99%

On the Regularization of Recursive Least-Squares Adaptive Algorithms Using Line Search Methods

Stanciu,

Anghel,

Fîciu

et al. 2024

Electronics

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Stereophonic acoustic echo cancellation (SAEC) requires the identification of four unknown impulse responses corresponding to four loudspeaker-to-microphone pairs. Recent developments in the field of adaptive filters for SAEC setups have allowed for the handling of a single complex-valued adaptive impulse response, instead of the four classical real-valued adaptive filters. With the simplified framework provided by the widely linear (WL) model, more advanced versions of recursive least-squares (RLS) were employed in order to take advantage of their superior tracking speeds when working with highly correlated input signals (such as speech). Despite the performances and numerical stability provided by using exponentially weighted versions of the RLS family in combination with line search methods (LSMs), the SAEC configurations have limited capabilities in mitigating the negative effects caused by high-level disturbances affecting the two microphone signals. Such is the case of double-talk scenarios, which considerably reduce the update accuracy of the adaptive system. This paper analyzes a regularization technique for the named WL-RLS-LSM adaptive filters by adjusting the correlation matrix associated with the input signals and creating a reaction in the update process. The proposed method is designed to considerably slow (or even freeze) the adaptation process while the disturbance is manifested. Simulation results are discussed in order to validate the theoretical content.

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“…From a hardware implementation point of view, the multiplications with lambda can be performed more efficiently with only bit-shifts and subtractions [18,29]. Furthermore, for the RLS-DCD-T, its DCD specific parameters are chosen considering several aspects.…”

Section: Simulations and Practical Considerationsmentioning

confidence: 99%

Low-Complexity Recursive Least-Squares Adaptive Algorithm Based on Tensorial Forms

et al. 2021

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Modern solutions for system identification problems employ multilinear forms, which are based on multiple-order tensor decomposition (of rank one). Recently, such a solution was introduced based on the recursive least-squares (RLS) algorithm. Despite their potential for adaptive systems, the classical RLS methods require a prohibitive amount of arithmetic resources and are sometimes prone to numerical stability issues. This paper proposes a new algorithm for multiple-input/single-output (MISO) system identification based on the combination between the exponentially weighted RLS algorithm and the dichotomous descent iterations in order to implement a low-complexity stable solution with performance similar to the classical RLS methods.

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Efficient FPGA implementation of the DCD-RLS algorithm for stereo acoustic echo cancellation

Cited by 6 publications

References 7 publications

Tensor-Based Recursive Least-Squares Adaptive Algorithms with Low-Complexity and High Robustness Features

Tensor-Based Recursive Least-Squares Adaptive Algorithms with Low-Complexity and High Robustness Features

On the Regularization of Recursive Least-Squares Adaptive Algorithms Using Line Search Methods

Low-Complexity Recursive Least-Squares Adaptive Algorithm Based on Tensorial Forms

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