A multi-class method for detecting audio events in news broadcasts

  • Authors:
  • Sergios Petridis;Theodoros Giannakopoulos;Stavros Perantonis

  • Affiliations:
  • Computational Intelligence Laboratory, Institute of Informatics and Telecommunications, National Center of Scientific Research Demokritos;Computational Intelligence Laboratory, Institute of Informatics and Telecommunications, National Center of Scientific Research Demokritos;Computational Intelligence Laboratory, Institute of Informatics and Telecommunications, National Center of Scientific Research Demokritos

  • Venue:
  • SETN'10 Proceedings of the 6th Hellenic conference on Artificial Intelligence: theories, models and applications
  • Year:
  • 2010

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Abstract

We propose a method for audio event detection in video streams from news Apart from detecting speech, which is obviously the major class in such content, the proposed method detects five non-speech audio classes The major difficulty of the particular task lies in the fact that most of the non-speech audio events are actually background sounds, with speech as the primary sound We have adopted a set of 21 statistics computed on a mid-term basis over 7 audio features A variation of the One Vs All classification architecture has been adopted and each binary classification problem is modeled using a separate probabilistic Support Vector Machine Experiments have shown that the proposed method can achieve high precision rates for most of the audio events of interest.