Neural network‐based design of 2‐channel quadrature mirror filter banks
Yue‐Dar Jou, Zhan‐Pei Lin, Fu‐Kun Chen · International Journal of Circuit Theory and Applications · 2018
Summary Two‐channel quadrature mirror filter banks can be efficiently established by the combination of real‐valued infinite impulse response all‐pass filters without incurring aliasing and magnitude distortions. The design problem of filter banks is therefore to seek for the phase error minimization of the all‐pass filter coefficients. In this paper, a neural network‐based Lyapunov energy function is used to relate to the objective function of the designed all‐pass filter coefficients. Based on the architecture of neural networks and suitable selection of Hopfield‐related parameters, the all‐pass filter coefficients are obtained when the networks reach convergence. By further using the suitable combination of the designed all‐pass filters, the 2‐channel quadrature mirror filter banks with perfect reconstruction can be efficiently accomplished. Simulation results indicate that the neural network‐based approach has the advantage of satisfactory performance in magnitude and group delay responses in a parallelism manner.