A New Dual Channel Speech Enhancement Approach Based on Accelerated Particle Swarm Optimization (APSO)

Prajna, K.; Rao, G. Sasi Bhushan; Reddy, K. Vijaya Kumar; Maheswari, R. Uma

doi:10.5815/ijisa.2014.04.01

Cited by 16 publications

(12 citation statements)

References 16 publications

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“…[18] at Cambridge University in 2007 in order to accelerate the convergence of the algorithm is to use the global best only. PSO and APSO-based optimizations have already been studied by the researchers for optimal design of substation grounding grid [34], performance analysis of MIMO radar waveform [35], design of frame structures [36], dual channel speech enhancement [37] and a faster path planner [38] etc.…”

Section: A Particle Swarm Optimization (Pso)mentioning

confidence: 99%

Optimal power allocation scheme for plug-in hybrid electric vehicles using swarm intelligence techniques

et al. 2016

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Green technologies gain popularity to reduce the pollution and give higher penetration of renewable energy source in the transportation. This research induce that the extensive involvement of plug-in hybrid electric vehicles (PHEVs) requires adequate charging allocation strategy using a combination of smart grid systems and smart charging infrastructures. It is also noticed that daytime charging station are necessary for daily usage of PHEVs due to the limited all-electric-range. Most of the research in the past have been stated that only proper charging control and infrastructure management can assure the larger participation of PHEVs.Therefore, researchers are trying to develop efficient control mechanism for charging infrastructure in order to facilitate upcoming PHEVs penetration in highway. Nevertheless, most of the past researcher already aware with the issue related to intelligent energy management. Yet, these studies could not fill the gap of the problem associated with intelligent energy management and require formulation of mathematical models with extensive use of computational intelligence-based optimization techniques to solve many technical problems. The outcome of this research study provides four optimization techniques that includes Hybrid method within swarm intelligence group for the State-of-Charge (SoC) optimization of PHEVs. The finding of this research simulation results obtained for maximizing the highly nonlinear objective function evaluate the comparative performance of all four techniques in terms of best fitness, convergence speed and computation time. Finally, the hybridization method (PSOGSA) presented in this dissertation uses the advantages of both PSO and GSA optimization and thus produce higher best fitness values. This study evaluates the performance of standard PSO, then Accelerated version of PSO (APSO), GSA algorithm and then Hybrid of PSO and GSA. The hybridization method (PSOGSA) uses the advantages of both PSO and GSA optimization and thus produce higher best fitness values. However, PSOGSA method takes much longer computational time than single methods because of incorporating two single methods in one algorithm. This research study suggests that PSOGSA method is a great promise for SoC optimization but it takes much longer computational time.

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Section: A Particle Swarm Optimization (Pso)mentioning

confidence: 99%

Optimal power allocation scheme for plug-in hybrid electric vehicles using swarm intelligence techniques

et al. 2016

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“…The reason of using the individual best is mainly to increase the diversity in the quality solutions; however, this diversity can be simulated using some randomness. Subsequently, there is no convincing reason for using the individual best, unless the optimization problem of interest is multimodal and highly nonlinear [24]. A simplified version, which could accelerate the convergence of the algorithm is to use the global best only.…”

Section: Accelerated Particle Swarm Optimizationmentioning

confidence: 99%

“…PSO-and APSO-based optimizations have already been studied by researchers for the optimal design of a substation grounding grid [19], non-convex optimization [20,21], performance analysis of MIMO radar waveform [22], design of frame structures [23], dual channel speech enhancement [24], synthesis gas production [25] and a faster path planner [26]. Specifically, we are investigating the use of the accelerated particle swarm optimization (APSO) method for developing real-time and large-scale optimizations for allocating power.…”

Section: Introductionmentioning

confidence: 99%

Swarm intelligence based State-of-Charge optimization for charging Plug-in Hybrid Electric Vehicles

Rahman

Vasant

Singh

et al. 2015

WIT Transactions on Ecology and the Environment

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Transportation electrification has undergone major changes since the last decade. Success of the smart grid with renewable energy integration solely depends upon the large-scale penetration of Plug-in Hybrid Electric Vehicles (PHEVs) for a sustainable and carbon-free transportation sector. One of the key performance indicators in the hybrid electric vehicle is the State-of-Charge (SoC), which needs to be optimized for the betterment of charging infrastructure using stochastic computational methods. In this paper, a newly emerged accelerated particle swarm optimization (APSO) technique was applied and compared with standard Particle swarm optimization (PSO), considering charging time and battery capacity. Simulation results obtained for maximizing the highly non-linear objective function indicate that APSO achieves some improvement in terms of best fitness and computation time.

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“…This was shown to have a comparatively simpler implementation and a much faster convergence speed. APSO was used for speech enhancement in 2014 by (Prajna et al, 2014). The authors conducted study on dual channel speech enhancement and compared the results of APSO with PSO.…”

Section: Introductionmentioning

confidence: 99%

Speech Enhancement Based on Adaptive Noise Cancellation and Particle Swarm Optimization

Taha¹,

Wajid²,

Hussain³

2019

Journal of Computer Science

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Speech enhancement is used in almost all modern communication systems. This is due to the quality of speech being degraded by environmental interference factors, such as: Acoustic additive noise, acoustic reverberation or white Gaussian noise. This paper, explores the potential of different benchmark optimization techniques for enhancing the speech signal. This is accomplished by fine tuning filter coefficients using a diverse set of adaptive filters for noise suppression in speech signals. We consider the Particle Swarm Optimization (PSO) and its variants in conjunction with the Adaptive Noise Cancellation (ANC) approach, for delivering dual speech enhancement. Comparative simulation results demonstrate the potential of an optimized coefficient ANC over a fixed one. Experiments are performed at different signal to noise ratios (SNRs), using two benchmark datasets: the NOIZEUS and Arabic dataset. The performance of the proposed algorithms is evaluated by maximising the perceptual evaluation of speech quality (PESQ) and comparing to the audio-only Wiener Filter (AW) and the Adaptive PSO for dual channel (APSOforDual) algorithms.

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A New Dual Channel Speech Enhancement Approach Based on Accelerated Particle Swarm Optimization (APSO)

Cited by 16 publications

References 16 publications

Optimal power allocation scheme for plug-in hybrid electric vehicles using swarm intelligence techniques

Optimal power allocation scheme for plug-in hybrid electric vehicles using swarm intelligence techniques

Swarm intelligence based State-of-Charge optimization for charging Plug-in Hybrid Electric Vehicles

Speech Enhancement Based on Adaptive Noise Cancellation and Particle Swarm Optimization

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