A new approach to dual channel speech enhancement based on hybrid PSOGSA

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

doi:10.1007/s10772-014-9245-5

Cited by 14 publications

(8 citation statements)

References 14 publications

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“…GSA [80] • Improved PESQ scores when compared to SPSO algorithm PSOGSA [81] • Better than GSA and SPSO BAT [78] • Better improved quality and intelligibility of enhanced speech than PSO, SPSO, APSO, GSA, PSOGSA Modified BAT [79] • Better than BAT and GA Table 7. Advantages and dis-advantages of multimodal speech enhancement methods Pros cons Goecke et.…”

Section: Discussionmentioning

confidence: 99%

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A Survey on Techniques for Enhancing Speech

Tayseer¹,

Adeel²,

Hussain³

2018

IJCA

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Speech enhancement is used in almost all the modern communication systems. It is obvious that when speech is being transmitted, its quality may degrade due to interference in the environment it is passing through. Some of the interferences that may affect the speech quality of transit include acoustic additive noise, acoustic reverberation or white Gaussian noise. This paper focuses on the techniques that appeared in the literature to enhance the signal of speech. Various methods used include wiener filter, statistical methods, subspace method, basic spectral subtraction method and spectral subtraction. In this paper authors will discuss various such methods along with their advantages and disadvantages.

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Section: Discussionmentioning

confidence: 99%

“…In 2015, a hybrid PSOGSA was presented to enhance the noise distorted speech signals in dual channel systems [81]. Each agent in the swarm, representing the filter coefficients is deemed as a candidate solution.…”

Section: Bat Algorithmmentioning

confidence: 99%

A Survey on Techniques for Enhancing Speech

Tayseer¹,

Adeel²,

Hussain³

2018

IJCA

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“…The parameter settings for GSA are demonstrated in Table 4. PSOGSA-based optimization has already been used by the researchers for economic load dispatch [44], optimal static state estimation [45], dual channel speech enhancement [46], training feed-forward neural networks [47] and multi distributed generation planning [48] 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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“…Moreover, PSOGSA-based optimization has already been used by the researchers for economic load dispatch (Dubey, Pandit et al, 2013), optimal static state estimation (Mallick, Ghoshal et al, 2013), dual channel speech enhance-ment (Kunche, Rao et al, 2014), training feed-forward neural networks and multi-distributed generation planning (Tan, Hassan et al, 2013). Specifically, we are investigating the use of the Hybrid particle swarm optimization Gravitational Search Algorithm (PSOGSA) method for developing real-time and large-scale optimizations for allocating power.…”

Section: Hybrid Particle Swarm and Gravitational Search Optimization mentioning

confidence: 99%

Handbook of Research on Modern Optimization Algorithms and Applications in Engineering and Economics

Vasant¹,

Weber²,

Vo³

2016

Advances in Computational Intelligence and Robotics

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Electrification of Transportation has undergone major modifications since the last decade. Success of combining smart grid technology and renewable energy exclusively depends upon the large-scale participation of Plug-in Hybrid Electric Vehicles (PHEVs) towards reach the desired pollution-free transportation industry. One of the key Performance pointers of hybrid electric vehicle is the State-of-Charge (SoC) which needs to be enhanced for the advancement of charging station using computational intelligence methods. In this Chapter, authors applied Hybrid Particle swarm and gravitational search Optimization (PSOGSA) technique for intelligently allocating energy to the PHEVs considering constraints such as energy price, remaining battery capacity, and remaining charging time. Computational experiment results attained for maximizing the highly non-linear fitness function estimates the performance measure of both the techniques in terms of best fitness value and computation time.

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A new approach to dual channel speech enhancement based on hybrid PSOGSA

Cited by 14 publications

References 14 publications

A Survey on Techniques for Enhancing Speech

A Survey on Techniques for Enhancing Speech

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

Handbook of Research on Modern Optimization Algorithms and Applications in Engineering and Economics

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