Optimum design of multistage half-band FIR filter for audio conversion using a simulated annealing algorithm

Ma, Rongxuan; Wang, Yonghao; Hu, Wei; Zhu, Xiaojun; Zhang, Kai

doi:10.1109/iciea.2018.8397692

Cited by 8 publications

(4 citation statements)

References 11 publications

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“…8 that demonstrates the performance of g-LHBP filters for a given K and . In addition, Table IV shows the comparison of the proposed method with the previous state-of-the-art works, where examples given in [16]- [19], [25], [28] have been considered for comparison purposes. It can be seen that the g-LHBP filters outperform the other designed filters in terms of transition bandwidth and peak-to-peak passband/stopband ripples.…”

Section: Design Examplesmentioning

confidence: 99%

“…However, classical design involves approximation of the desired frequency response by some suitable closed-form polynomial [14]- [18] because such a closed-form solution mainly focuses on the flatness of the filter but not on the exact frequency response [11]- [13]. Several methods and implementation tricks have been proposed for the design of MAXFLAT FIR half-band filters [15], [16], [19], [20]. These filters have exact cutoff frequency at the middle of the frequency band ω = π / 2 and allow computationally efficient implementations because almost half of their impulse response (IR) coefficients are zeros.…”

Section: Introductionmentioning

confidence: 99%

“…Most of the popular MAXFLAT FIR halfband filters are designed by the Lagrange half-band polynomial (LHBP) which has the maximum number of zeros at 1 [21], [22]. This class of filters has many applications such as filter banks, wavelets-based compression, and multirate techniques [15], [16], [19], [23], [24]. However, similarly to the existing MAXFLAT FIR half-band filters, the LHBP filters do not also have any independent ("free") parameters.…”

Section: Introductionmentioning

confidence: 99%

“…Khan [30], [31] reported FIR half-band filters with narrow transition-band that have their points of flatness at the middle point between the passband and stopband, but such a filter does not have any independent parameters to control the frequency response and the sharpness of the transition-band can be achieved with a comparatively higher value of filter length. Ma et al have proposed a cascaded half-band filter design by controlling stopband attenuation for a fixed transition bandwidth [16]. This multistage algorithm allows computation reduction of more than 4% per input sample as compared to conventional filters, but there exists the limitation of half-band filter design because a heuristic threshold value is required to meet a specified narrow transition band.…”

Section: Introductionmentioning

confidence: 99%

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Design of FIR Half-Band Filter With Controllable Transition-Band Steepness

et al. 2021

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Maximally flat (MAXFLAT) half-band filters usually have wider transition-band than other filters although the frequency response is maximally flat (i.e., no ripples) in the passband and stopband. This is due to fact that the maximum possible number of zeros at z = ±1 is imposed in half-band close form solution, which leaves no degree of freedom, and thus no independent parameters for direct control of the frequency response. This paper describes a novel method for the design of FIR half-band filters with an explicit control of the transition bandwidth. The proposed method is based on a generalized Lagrange halfband polynomial (g-LHBP) with coefficients parameterizing a 0-th coefficient h 0 and allows the frequency response of this filter type to be controllable by adjusting h 0 . Then, h 0 is modeled as a steepness parameter of the transition-band and this is accomplished through theoretically analyzing a polynomial recurrence relation of the g-LHBP. This method also provides explicit formulas for direct computation of design parameters related to choosing a desired filter characteristic (by a reasonable trade-off between the transition-band sharpness and passband & stopband flatness). The examples are shown to provide a complete and accurate solution for the design of such filters with relatively sharper transition-band steepness than other existing half-band filters.

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Section: Design Examplesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Design of FIR Half-Band Filter With Controllable Transition-Band Steepness

et al. 2021

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FIR digital filter design based on improved artificial bee colony algorithm

Lian

Tian

2022

Soft Comput

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The traditional swarm intelligence optimization algorithm is prone to fall into local optimal solutions in finite impulse response (FIR) digital filter design, and has slow convergence speed. In order to optimize the design of FIR filter, a FIR digital filter design method based on improved bee colony (ABC) algorithm is proposed．This improved ABC algorithm can adaptively adjust the step size of the selected neighborhood of nectar source location. At the same time, the information of the global optimal solution is used to guide the search of candidate solution, which improves the global search ability of the algorithm. The improved ABC algorithm can balance the conflict between local search ability and global search ability, so it can achieve better optimization effect.The time and space complexity of the algorithm is analyzed in detail. Then, the improved ABC algorithm is used to design three typical FIR digital filters, namely low-pass, band-pass and band-stop filter. The performance of the designed filter is tested by simulation experiments. The experimental results show that compared with other state-of-the-art optimization algorithms, the proposed FIR filter design method has achieved better effect and performance. Meanwhile, the proposed design method has shorter optimization time. The superiority of the proposed method is verified.

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