TY - JOUR
T1 - A Compact and High-Performance Acoustic Echo Canceller Neural Processor Using Grey Wolf Optimizer along with Least Mean Square Algorithms
AU - Pichardo, Eduardo
AU - Anides, Esteban
AU - Vazquez, Angel
AU - Garcia, Luis
AU - Avalos, Juan G.
AU - Sánchez, Giovanny
AU - Pérez, Héctor M.
AU - Sánchez, Juan C.
N1 - Publisher Copyright:
© 2023 by the authors.
PY - 2023/3
Y1 - 2023/3
N2 - 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.
AB - 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.
KW - AEC system
KW - FPGA
KW - LMS
KW - grey wolf optimization
KW - neuromorphic architecture
KW - real world application
KW - spiking neural P system
KW - swarm intelligence
UR - http://www.scopus.com/inward/record.url?scp=85151573067&partnerID=8YFLogxK
U2 - 10.3390/math11061421
DO - 10.3390/math11061421
M3 - Artículo
AN - SCOPUS:85151573067
SN - 2227-7390
VL - 11
JO - Mathematics
JF - Mathematics
IS - 6
M1 - 1421
ER -