scholarly journals Speech Enhancement and Recognition using Kalman Filter Modified via Radial Basis Function

2020 ◽  
Vol 9 (2) ◽  
Author(s):  
Mario Barnard ◽  
Farag M. Lagnf ◽  
Amr S. Mahmoud ◽  
Mohamed Zohdy
Keyword(s):  
Kalman Filter ◽  
Speech Recognition ◽  
Radial Basis Function ◽  
Speech Enhancement ◽  
Basis Function ◽  
Audio Signal ◽  
Linear Functions ◽  
Non Linear ◽  
Radial Basis

In this paper, a Radial Basis Function-based Kalman filter has been utilized to perform speech enhancement of an audio signal. Moreover, in order to accomplish speech recognition, correlation after detecting signal envelop has been applied. Based on the simulation result, it shows that using the radial basis function-based Kalman filter (non-linear functions to estimate Q parameter) should lead to obtain better results.

scholarly journals Accurate radial basis functions technique for competitive and efficient solutions of non-linear Black-Scholes equations

2020 ◽  
Vol 20 (4) ◽  
pp. 60-83
Author(s):  
Vinícius Magalhães Pinto Marques ◽  
Gisele Tessari Santos ◽  
Mauri Fortes
Keyword(s):  
Radial Basis Function ◽  
Basis Function ◽  
Efficient Solutions ◽  
Basis Functions ◽  
Adaptive Scheme ◽  
Call Options ◽  
Black Scholes ◽  
Non Linear ◽  
Radial Basis ◽  
Complex Models

ABSTRACTObjective: This article aims to solve the non-linear Black Scholes (BS) equation for European call options using Radial Basis Function (RBF) Multi-Quadratic (MQ) Method.Methodology / Approach: This work uses the MQ RBF method applied to the solution of two complex models of nonlinear BS equation for prices of European call options with modified volatility. Linear BS models are also solved to visualize the effects of modified volatility.  Additionally, an adaptive scheme is implemented in time based on the Runge-Kutta-Fehlberg (RKF) method.

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