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Predicting interestingness of media content remains an important, but challenging research subject. The difficulty comes first from the fact that, besides being a high-level semantic concept, interestingness is highly subjective and its global definition has not been agreed yet. This paper presents the use of up-to-date deep learning techniques for solving the task. We perform experiments with both...
This paper addresses the challenging problem of single-channel audio source separation. We introduce a novel user-guided framework where source models that govern the separation process are learned on-the-fly from audio examples retrieved online. The user only provides the search keywords that describe the sources in the mixture. In this framework, the generic spectral characteristics of each source...
The problem of speech inpainting consists in recovering some parts in a speech signal that are missing for some reasons. To our best knowledge none of the existing methods allows satisfactory inpainting of missing parts of large size such as one second and longer. In this work we address this challenging scenario. Since in the case of such long missing parts entire words can be lost, we assume that...
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