Wyniki wyszukiwania - BazTech

Ograniczanie wyników

Znaleziono wyników: 2

Liczba wyników na stronie

Wyniki wyszukiwania

Wyszukiwano:
w słowach kluczowych: dopasowanie fazowe

Sortuj według:

Ogranicz wyniki do:

TEO-CFCC Characteristic Parameter Extraction Method for Speaker Recognition in Noisy Environments

Li L., An D., Zhao D., Rong C., Ma S.

Przegląd Elektrotechniczny

2013

R. 89, nr 2a

118--121

This paper proposes TEO-CFCC characteristic parameter extraction method. Signal phase matching is applied to eliminate speech noise on the basis of CFCC characteristic parameter, and then Teager energy operator is added to the acquisition of CFCC characteristic parameter. In this way TEO-CFCC characteristic parameter is obtained and the energy of speech becomes one of the characteristic parameters for speaker recognition. Experiment results show that the recognition accuracy can reach to 83.2% in a -5dB SNR of vehicle interior noise environment by using TEO-CFCC characteristic parameter.

W artykule przedstawiono metodę wyznaczania parametrów charakterystycznych filtru TEO-CFCC. Zastosowano tu dopasowywanie fazowe sygnału, dla eliminacji z mowy szumów oraz operator Teagera do wyrugowania parametrów. Badania eksperymentalne pokazuję, że dokładność rozpoznania głosu wynosi 83,2% przy -5dB SNR we wnętrzu pojazdu.

An improved region-growth algorithm for dense matching

Xing Y. J., Meng J. J., Sun J., Wang Z. Q.

Journal of Achievements in Materials and Manufacturing Engineering

2006

Vol. 18, nr 1-2

323--326

Purpose: Improve the accuracy and speed of the region-growth algorithm between two 2D images. Design/methodology/approach: The algorithm includes two parts: the selection of seeds points and propagation. Some improvements are made in each one. For the first part, the best-first strategy is used to assure the accuracy of seeds. The epipolar line constraint and continuity constraint reduce the double phase matching course into single phase matching. For the second one, a dynamic and adaptive window is adopted instead of the large window. Findings: In the first section, the process of searching and the computational duties are decreased in large extent. And in the second one, the adaptive window makes the searching course more efficient in time and space. It is really difficult to get the most suitable window to search for the points as soon as possible. If it can be easily got, it will advance the efficiency of search. It is the future work. Practical implications: The method can be used in many different images, such as the structural images and the facial images. Originality/value: The original value is the region-growth algorithm, and in this paper I made some betterments to advance the efficiency and accuracy.