Non-uniform cosine modulated filter banks using meta-heuristic algorithms in CSD space

Abstract

This paper presents an efficient design of non-uniform cosine modulated filter banks (CMFB) using canonic signed digit (CSD) coefficients. CMFB has got an easy and efficient design approach. Non-uniform decomposition can be easily obtained by merging the appropriate filters of a uniform filter bank. Only the prototype filter needs to be designed and optimized. In this paper, the prototype filter is designed using window method, weighted Chebyshev approximation and weighted constrained least square approximation. The coefficients are quantized into CSD, using a look-up-table. The finite precision CSD rounding, deteriorates the filter bank performances. The performances of the filter bank are improved using suitably modified meta-heuristic algorithms. The different meta-heuristic algorithms which are modified and used in this paper are Artificial Bee Colony algorithm, Gravitational Search algorithm, Harmony Search algorithm and Genetic algorithm and they result in filter banks with less implementation complexity, power consumption and area requirements when compared with those of the conventional continuous coefficient non-uniform CMFB.

Efficient design of non-uniform cosine modulated filter banks for digital hearing aids

“…Table 1 shows the performance comparison of different nonuniform cosine modulated filter banks using merging method. It is observed that the method proposed in [18] gives minimum amplitude distortion error with minimum filter order.…”

“…Initially an NPR uniform CMFB is designed using linear search technique [18]. Only the prototype filter is designed and optimized.…”

Design of low complexity sharp MDFT filter banks with perfect reconstruction using hybrid harmony-gravitational search algorithm

“…The HSA explores the search space for finding the candidate solutions with good fitness value. The various phases involved in HSA are initialization of harmony memory, harmony improvisation, memory consideration, pitch adjustment, random selection, memory updates and termination [16,39,40]. The weights of the objective function for HSA are obtained as, b 1 ¼ 0:6, b 2 ¼ 2, b 3 ¼ 1 and b 4 ¼ 0:03 by trial and error method, in order to get the desired specifications.…”

“…The positions of the masses are updated in each iteration. The various steps involved in GSA are initialization of agents, fitness evaluation, gravitational constant update, acceleration of agents calculation, update the velocity and position of agents and termination [17,39,40]. The weights of the objective function for GSA are obtained as, b 1 ¼ 1, b 2 ¼ 2, b 3 ¼ 1 and b 4 ¼ 0:05 by trial and error method, in order to get the desired specifications.…”

An Efficient Method for Designing Multiplier-Less Non-uniform Filter Bank Based on Hybrid Method Using CSE Technique

“…Therefore, authors have used different optimization algorithms such as differential evolution (DE), harmony search algorithm (HSA), gravitational search algorithm (GSA) for designing multiplier-less M−channel uniform and nonuniform filter banks to enhance the performance by minimizing the errors occurred due to quantization and CSD conversion of filter coefficients [3,13,19]. This was further modified using meta-heuristic algorithms to design frequency response masking (FRM)-based multiplier-less NUFB [2,18]. While in [24,28], a linear search technique and DE algorithm have been used for designing the filter banks, where CSD represented filter coefficients are optimized.…”