Lateral inhibition mechanism in computational auditory model and its application in robust speech recognition

2002 ◽  
Author(s):  
Lu Xugang ◽  
Li Gang ◽  
Wang Lipo
Keyword(s):  
Speech Recognition ◽  
Lateral Inhibition ◽  
Inhibition Mechanism ◽  
Robust Speech Recognition ◽  
Auditory Model

Auditory model for robust speech recognition in real world noisy environments

1997 ◽  
Vol 33 (1) ◽  
pp. 12 ◽  
Author(s):  
Doh-Suk Kim ◽  
Soo-Young Lee ◽  
Rhee M. Kil ◽  
Xuelong Zhu
Keyword(s):  
Speech Recognition ◽  
Real World ◽  
Robust Speech Recognition ◽  
Noisy Environments ◽  
Auditory Model

Whale optimization algorithm based on lateral inhibition for image matching and vision-guided AUV docking

2020 ◽  
pp. 1-12
Author(s):  
Zheping Yan ◽  
Jinzhong Zhang ◽  
Jialing Tang
Keyword(s):  
Lateral Inhibition ◽  
Optimization Algorithm ◽  
Image Matching ◽  
Autonomous Underwater Vehicle ◽  
Optimal Solution ◽  
Search Space ◽  
Whale Optimization Algorithm ◽  
Inhibition Mechanism ◽  
Whale Optimization ◽  
Accuracy And Stability

The accuracy and stability of relative pose estimation of an autonomous underwater vehicle (AUV) and a target depend on whether the characteristics of the underwater image can be accurately and quickly extracted. In this paper, a whale optimization algorithm (WOA) based on lateral inhibition (LI) is proposed to solve the image matching and vision-guided AUV docking problem. The proposed method is named the LI-WOA. The WOA is motivated by the behavior of humpback whales, and it mainly imitates encircling prey, bubble-net attacking and searching for prey to obtain the globally optimal solution in the search space. The WOA not only balances exploration and exploitation but also has a faster convergence speed, higher calculation accuracy and stronger robustness than other approaches. The lateral inhibition mechanism can effectively perform image enhancement and image edge extraction to improve the accuracy and stability of image matching. The LI-WOA combines the optimization efficiency of the WOA and the matching accuracy of the LI mechanism to improve convergence accuracy and the correct matching rate. To verify its effectiveness and feasibility, the WOA is compared with other algorithms by maximizing the similarity between the original image and the template image. The experimental results show that the LI-WOA has a better average value, a higher correct rate, less execution time and stronger robustness than other algorithms. The LI-WOA is an effective and stable method for solving the image matching and vision-guided AUV docking problem.

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