An Application of Expectation-Maximization for Model Verification

A description which summarizes entire and usually big set of data is called its model. The problem investigated in the paper consists in verification of models of data coming from a simulation experiment of selecting candidates for operators of mobile robot (more strictly building reliable predictive model of the data). The models are validated using train-and-test method and verified with the help of the EM (expectation-maximization) algorithm which was originally designed for solving clustering problems with missing data. Actually, the selecting is a clustering problem because the candidates are assigned to ‘chosen’, ‘accepted’ or ‘rejected’ subgroups. For such a case the missing data is the category (the subgroup) for which a candidate should be assigned on the basis of his activity measured during the simulation experiment. The paper explains the procedure of model verification. It also shows experimental results and draws conclusions

Automatic Classification of African Elephant (\u3cem\u3eLoxodonta africana\u3c/em\u3e) Follicular and Luteal Rumbles

Recent research in African elephant vocalizations has shown that there is evidence for acoustic differences in the rumbles of females based on the phase of their estrous cycle (1). One reason for these differences might be to attract a male for reproductive purposes. Since rumbles have a fundamental frequency near 10Hz, they attenuate slowly and can be heard over a distance of several kilometers. This research exploits differences in the rumbles to create an automatic classification system that can determine whether a female rumble was made during the luteal or follicular phase of the ovulatory cycle. This system could be used as the basis for a non-invasive technique to determine the reproductive status of a female African elephant. The classification system is based on current state-of-the-art human speech processing systems. Standard features and models are applied with the necessary modifications to account for the physiological, anatomical and language differences between humans and African elephants. The long-term goal of this research is to develop a universal analysis framework and robust feature set for animal vocalizations that can be applied to many species. This research represents an application of this framework. The vocalizations used for this study were collected from a group of three female captive elephants. The elephants are fitted with radio-transmitting microphone collars and released into one of three naturalistic yards on a daily basis. Although this data collection setup is good for determining the speaker of each vocalization, it suffers from many potential noise sources such as RF interference, passing vehicles, and the flapping of the elephant’s ears against the collar