Nowadays, meta-heuristic algorithms have been emerged as a potential solution in adaptive filtering applications since they offer good convergence properties. Nonetheless, most of them fall into local minimum since their optimization is based on single-solution technique. As a consequence, these algorithms present a high misadjustment level and require a large population to find the optimal solution. Recently, the grey wolf optimization (GWO) algorithm have emerged as a potential solution since it requires a smaller population and possesses a stronger global optimization ability with lesser control parameters. From an engineering perspective, its compactness is an attractive feature. Therefore, this opens new horizons in the implementation of this algorithm in resource-constrained devices. In this paper, we present for the first time the use of the GWO algorithm for system identification and acoustic echo canceller (AEC) and its implementation in a field programmable gate array (FPGA) device to validate its effectiveness. Our results show that the use of the GWO algorithm achieves lower steady-state mean square error (MSE) and requires less computational resources when compared with one of the most used meta-heuristic algorithm.

Recently, the use of acoustic echo canceller (AEC) systems in portable devices has significantly increased. Therefore, the need for superior audio quality in resource-constrained devices opens new horizons in the creation of high-convergence speed adaptive algorithms and optimal digital designs. Nowadays, AEC systems mainly use the least mean square (LMS) algorithm, since its implementation in digital hardware architectures demands low area consumption. However, its performance in acoustic echo cancellation is limited. In addition, this algorithm presents local convergence optimization problems. Recently, new approaches, based on stochastic optimization algorithms, have emerged to increase the probability of encountering the global minimum. However, the simulation of these algorithms requires high-performance computational systems. As a consequence, these algorithms have only been conceived as theoretical approaches. Therefore, the creation of a low-complexity algorithm potentially allows the development of compact AEC hardware architectures. In this paper, we propose a new convex combination, based on grey wolf optimization and LMS algorithms, to save area and achieve high convergence speed by exploiting to the maximum the best features of each algorithm. In addition, the proposed convex combination algorithm shows superior tracking capabilities when compared with existing approaches. Furthermore, we present a new neuromorphic hardware architecture to simulate the proposed convex combination. Specifically, we present a customized time-multiplexing control scheme to dynamically vary the number of search agents. To demonstrate the high computational capabilities of this architecture, we performed exhaustive testing. In this way, we proved that it can be used in real-world acoustic echo cancellation scenarios.

In realistic sound scenarios, cutting-edge active noise control (ANC) systems still suffer critical instabilities since these systems works under impulsive noise signals. To decrease the negative impact of this noise, several authors have made extraordinary efforts to develop efficient algorithms in terms of convergence speed and misadjustment. However, these algorithms still exhibits limited convergence capabilities. In this work, we present for the first time, the development of a switching algorithm based on maximum versoria criterion (MVC) algorithm. Our results demonstrate that the proposed switching algorithm exhibits good convergence properties while maintaining a lower computational cost when compared with existing algorithms.

Recently, various active noise control (ANC) systems have been developed to attenuate unwanted signals within airplane cabins. Most of these works use the filtered-x least mean squares (FxLMS) algorithm due to its low computational cost, however its convergence speed is slow. To overcome this, several variations of the LMS algorithm have been developed to offer better convergence properties, such as affine projection algorithms and recent cutting-edge methods based on the q-gradient concept. Here, we present for the first time, a new affine projection algorithm based on the q-gradient, and the design of an ANC system applied to headrests of airplane seats. To evaluate the effectiveness of the proposal, we perform tests inside a Boeing 727-200 airplane cabin. The results demonstrate that the proposed algorithm achieves faster convergence speed than the conventional version, and its practical implementation shows good performance in attenuating noise signals within an airplane.