Automatic Topic Segmentation for Video Lectures Using Low and High-Level Audio Features

  • Eduardo R. Soares UFJF
  • Eduardo Barrére UFJF

Resumo


Nowadays, video lectures are a very popular way to transmit knowledge, and because of that, there are many repositories with a large catalog of those videos on web. Despite all benefits that this high availability of video lectures brings, some problems also emerge from this scenario. One of these problems is that, it is very difficult find relevant content associate with those videos. Many times, students must to watch the entire video lecture to find the point of interest and, sometimes, these points are not found. For that reason, in this work we propose a novel method based on early fusion of low and high-level audio features for automatic topic segmentation in video lectures. We have performed experiments in two sets of video lectures where we obtained very satisfactory results that evidence the applicability of our method on improving content search in those videos.
Palavras-chave: Topic segmentation, Video lectures, Automatic Speech Recognition, Semantic annotation, Knowledge base, Audio processing
Publicado
16/10/2018
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SOARES, Eduardo R.; BARRÉRE, Eduardo. Automatic Topic Segmentation for Video Lectures Using Low and High-Level Audio Features. In: SIMPÓSIO BRASILEIRO DE SISTEMAS MULTIMÍDIA E WEB (WEBMEDIA), 24. , 2018, Salvador. Anais do XXIV Simpósio Brasileiro de Multimídia e Web. Porto Alegre: Sociedade Brasileira de Computação, oct. 2018 . p. 189-196.

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