Robust statistical methods were first adopted in computer vision to improve the performance of feature extraction algorithms at the bottom level of the vision hierarchy. These methods tolerate the presence of data points that do not obey the assumed model such points are typically called “outlier”. Recently, various robust statistical methods have been developed and applied to computer vision t...

This paper proposes adaptive Gaussian backend (AGB), a novel approach to robust language identification (LID). In this approach, a given test sample is compared to language-specific training data in order to dynamically select data for a trialspecific language model. Discriminative AGB additionally weights the training data to maximize discrimination against the test segment. Evaluated on heavi...

Automatic identification of audio titles on radio broadcasts is a first step towards automatic annotation of radio programmes. Systems designed for the purpose of identification have to deal with a variety of postprocessing potentially imposed on audio material at the radio stations. One of the more difficult techniques to be handled is time-scaling, i.e., the variation of playback speed. In th...

This paper presents a new model updating approach for structural damage localization and quantification. Based on the Modal Assurance Criterion (MAC), a new damage-sensitive cost function is introduced by employing the main diagonal and anti-diagonal members of the calculated Generalized Flexibility Matrix (GFM) for the monitored structure and its analytical model. Then, ...