In this paper a corrective solution for more accurate esophageal voice cycle marks detection is presented While the algorithm developed in a previous work adapts the MDVP voice period marks to esophageal voice, it shows lacks when analyzing unsteady /a/ vowel samples and with high levels of noise. The proposed solution is aimed to iteratively correct the parameters (nmin and threshold) used in the previous algorithm. The corrective system filters the noise of voice recordings and then uses the output of the previous algorithm to calculate the most reliable fundamental frequency, based on the most stable part of the given signal. The results were acquired by comparing the pitch values of the previous algorithm with and without the proposed solution and the calculating the error against the real value. The corrective solution presents a considerable improvement in the accuracy of the voice cycle detection algorithm by reducing the standard deviation of the error dataset from 4.591 to 0.593 and the mean error value from 2.459 down to 0.370. Furthermore, it reduces up to a 54% all errors above J%.
Matching two regions represented in bathymetric data that have some form of geographical overlapping is an important and challenging aspect in underwater mapping. It is important because of the possible error in estimating the geographical location of each point underwater. It is challenging due to the size of the acquired bathymetric data points. The matching could also play a vital role in the registration of underwater images and/or maps fusion, if both bathymetric and intensity scans are considered. Compared to the exhaustive search that requires polynomial time, O(n2), an efficient bathymetric matching algorithm is proposed in this work that finds several match-points in linear time, requiring thus O(n) computations. The paper thus presents a new algorithm that allows to compile the bathymetric data of the common areas of two submarine areas that have been sampled in underwater missions.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.