Exploring Non-linear Transformations for an Entropybased Voice Activity Detector

In this paper we explore the use of non-linear transformations in order to improve the performance of an entropy based voice activity detector (VAD). The idea of using a non-linear transformation comes from some previous work done in speech linear prediction (LPC) field based in source separation techniques, where the score function was added into the classical equations in order to take into account the real distribution of the signal. We explore the possibility of estimating the entropy of frames after calculating its score function, instead of using original frames. We observe that if signal is clean, estimated entropy is essentially the same; but if signal is noisy transformed frames (with score function) are able to give different entropy if the frame is voiced against unvoiced ones. Experimental results show that this fact permits to detect voice activity under high noise, where simple entropy method fails.

Perceived Barriers to Physical Activity and Related Factors in Spanish University Students

Objective: The purpose of this study was to identify the influence of readiness of change for physical activity (PA), sociodemographic factors, lifestyle and physical activity status (PAS) on perceived barriers among Spanish university students. Participants: Seven hundred and seventy two (n = 772) men and women ages 17 - 39 at a north-west regional university in Spain participated in the study. Methods: The International Physical Activity Questionnaire, the States of Change for Physical Activity Behaviour Questionnaire and the Self-perceived Barriers for Physical Activity Questionnaire were used. Description, correlation and multiple regression analyses were completed. Results: Participants self-perceived low average-score barriers (2.6 ± 1.4 over 10.0). The 3-higher scores barriers corresponded to “too much work”; “lack of time for exercise” and “laziness”. Gender, PAS and self-perceived health were shown to be associated with perceived barriers. Conclusions: University institutions should consider those factors that predict barriers to PA to develop effective intervention programs.