Michael Betser

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In this paper, we present an approach for speaker diarization based on segmentation followed by bottom-up clustering, where clusters are modeled using adapted Gaussian mixture models. We propose a novel inter-cluster distance in the model parameter space which is easily computable and which can both be used as the dissimilarity measure in the clustering(More)
We propose a context-based model of video abstraction exploiting both audio and video features and applied to tennis TV programs. We can automatically produce different types of summary of a given video depending on the users’ constraints or preferences. We have first designed an efficient and accurate temporal segmentation of the video into segments(More)
Detecting and tracking broad sound classes in audio documents is an important step toward structuration. In the case of complex audio scenes, such as TV broadcast sound tracks, one problem is that several audio events may occur simultaneously. In this paper, we propose a two-step approach to detect superimposed events. The first step is a blind segmentation(More)
ABSTRACT Detecting and tracking broad sound classes in audio documents is an important step toward their structuration. In the case of complex audio scenes, such as the sound track of a TV broadcast, one problem is that several classes of sound maybe present simultaneously. It is therefore important to detect such superimposed events. Most methods would(More)
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