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Multimedia Event Detection (MED) is an annual task in the NIST TRECVID evaluation, and requires participants to build indexing and retrieval systems for locating videos in which certain predefined events are shown. Typical systems focus heavily on the use of visual data. Audio data, however, also contains rich information that can be effectively used for video retrieval, and MED could benefit from the attention of researchers in audio analysis. We present several systems for performing MED using only audio data, report the results of each system on the TRECVID MED 2011 development dataset, and compare the strengths and weaknesses of each approach.

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Diseases such as autism, cardiovascular disease, and the autoimmune disorders are difficult to treat because of the remarkable degree of variation among affected individuals. Subtyping research seeks to refine the definition of such complex, multi-organ diseases by identifying homogeneous patient subgroups. In this paper, we propose the Probabilistic Subtyping Model (PSM) to identify subgroups based on clustering individual clinical severity markers. This task is challenging due to the presence of nuisance variability — variations in measurements that are not due to disease subtype — which, if not accounted for, generate biased estimates for the group-level trajectories. Measurement sparsity and irregular sampling patterns pose additional challenges in clustering such data. PSM uses a hierarchical model to account for these different sources of variability. Our experiments demonstrate that by accounting for nuisance variability, PSM is able to more accurately model the marker data. We also discuss novel subtypes discovered using PSM and the resulting clinical hypotheses that are now the subject of follow up clinical experiments.