Performance analysis of industrial noise cancellation with pso based wiener filter using adaptive LMS &amp;amp; NLMS

S, Lakshmikanth; Natraj, K R; Rekha, K R

doi:10.1109/iccsp.2014.6949863

Cited by 5 publications

(1 citation statement)

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“…Asl and Nezhad [17], [18] present a modified PSO for speech enhancement, claiming improvement in SNR from 20.47 dB to 26.59 dB. PSO is most suitable among other like LMS, NLMS, RLS as stated in [19], it is also outperforms other evolutionary algorithms such as bacteria foraging optimization (BFO), GA and artificial bee colony (ABC) as suggested in [20], [21]. Kunche et al [22] claimed improvement in SNR about 24 dB, when using a hybrid of PSO and gravitational search algorithm (GSA), which referred as PSOGSA, claiming better background noise reduction of the noisy input speech.…”

Section: Introductionmentioning

confidence: 99%

Dual channel speech enhancement using particle swarm optimization

Hamza

Tashan

2021

IJEECS

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Adaptive processing for canceling noise is a powerful technology for signal processing that can completely remove background noise. In general, various adaptive filter algorithms are used, many of which can lack the stability to handle the convergence rate, the number of filter coefficient variations, and error accuracy within tolerances. Unlike traditional methods, to accomplish these desirable characteristics as well as to efficiently cancel noise, in this paper, the cancelation of noise is formulated as a problem of coefficient optimization, where the particle swarm optimization (PSO) is employed. The PSO is structured to minimize the error by using a very short segment of the corrupted speech. In contrast to the recent and conventional adaptive noise cancellation methods, the simulation results indicate that the proposed algorithm has better capability of noise cancelation. The results show great improvement in signal to noise ratio (SNR) of 96.07 dB and 124.54 dB for finite impulse response (FIR) and infinite impulse response (IIR) adaptive filters respectively.

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