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. © Springer-Verlag Berlin Heidelberg 2010.
CITATION STYLE
Petridis, S., Giannakopoulos, T., & Perantonis, S. (2010). A multi-class method for detecting audio events in news broadcasts. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6040 LNAI, pp. 399–404). https://doi.org/10.1007/978-3-642-12842-4_50
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