A novel FastICA algorithm based on improved secant method for Intelligent drive

Abstract

Blind Source Separation(BSS) is one of the research hotspots in the field of signal processing. In order to improve the accuracy of speech recognition in driving environment, the driver’s speech signal must be enhanced to improve its signal to noise ratio(SNR). Independent component analysis (ICA) algorithm is the most classical and efficient blind statistical signal processing technique. Compared with other improved ICA algorithms, fixed-point algorithm (FastICA) is well known for its fast convergence speed and good robustness. However, the convergence of FastICA algorithm is comparatively susceptible to the initial value selection of the original demixing matrix and the calculation of the iterative process is relatively large. In this paper, the gradient descent method is used to reduce the effect of initial value. What’s more, the improved secant method is proposed to speed up the convergence rate and reduce the amount of computation. As the results of mixed speech separation experiment turn out, the improved algorithm is of better performance relative to the standard FastICA algorithm. Experimental results show that the proposed algorithm improves the speech quality of the target driver. It is suitable for speech separation in driving environment with low SNR.

A Real-Time Complex Road AI Perception Based on 5G-V2X for Smart City Security

“…First, we extract the overall road area by training the Mask R-CNN [11][12][13][14][15]. The identified road area is used as the constraint area, the lane mark is detected in the area, the obtained discrete lane-line featurepoint information is clustered by the least-squares method, and the lane lines are fitted in a different field of view using straight-line and curve-fitting models [16,17].…”