Estimation of the Fundamental Frequency of the Speech Signal Compressed by G.723.1 Algorithm Applying PCC Interpolation
In this paper the results of the estimation of the fundamental frequency of the speech signal modeled by the G.723.1 method are analyzed. The estimation of the fundamental frequency was performed by the Peaking-Peaks algorithm with the implemented Parametric Cubic Convolution (PCC) interpolation. The efficiency of PCC was tested for Keys, Greville and Greville two-parametric kernel. Depending on MSE a window that gives optimal results was chosen.
The paper presents the algorithm for text line segmentation based on the oriented anisotropic Gaussian kernel. Initially, the document image is split into connected components achieved by bounding boxes. These connected components are cleared from redundant fragments. Furthermore, the binary moments are applied to each of these connected components evaluating local text skewing. According to this information the orientation of the anisotropic Gaussian kernel is set. After the algorithm application the boundary growing areas around connected components are established. These areas are of major importance for the evaluation of text line segmentation. For testing purposes, the algorithm is evaluated under different text samples. Comparative analysis between algorithm with and without orientation based on the anisotropic Gaussian kernel is made. The results show the improvement in the domain of text line segmentation.