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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Mark, B., Jose, J.M.: Audio-based event detection for sports video. In: Bakker, E.M., Lew, M., Huang, T.S., Sebe, N., Zhou, X.S. (eds.) CIVR 2003. LNCS, vol. 2728, pp. 61–65. Springer, Heidelberg (2003)
Baillie, M., Jose, J.: An audio-based sports video segmentation and event detection algorithm. In: 2004 Conference on Computer Vision and Pattern Recognition Workshop, pp. 110–110 (2004)
Tzanetakis, G., Chen, M.: Building audio classifiers for broadcast news retrieval. In: 5th International Workshop on Image Analysis for Multimedia Interactive Services, Lisboa, Portugal, April 2004, pp. 21–23 (2004)
Huang, R., Hansen, J.: Advances in unsupervised audio segmentation for the broadcast news and ngsw corpora. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, 2004, vol. 1 (2004)
Giannakopoulos, T.: Study and application of acoustic information for the detection of harmful content, and fusion with visual information. PhD thesis, Dpt. of Informatics and Telecommunications, University of Athens, Greece (2009)
Panagiotakis, C., Tziritas, G.: A speech/music discriminator based on rms and zero-crossings 7(1), 155–166 (2005)
Tzanetakis, G., Cook, P.: Musical genre classification of audio signals. IEEE Transactions on Speech and Audio Processing 10(5), 293–302 (2002)
Hyoung-Gook, K., Nicolas, M., Sikora, T.: MPEG-7 Audio and Beyond: Audio Content Indexing and Retrieval. John Wiley & Sons, Chichester (2005)
Misra, H., et al.: Spectral entropy based feature for robust asr. In: ICASSP, Montreal, Canada (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Petridis, S., Giannakopoulos, T., Perantonis, S. (2010). A Multi-class Method for Detecting Audio Events in News Broadcasts. In: Konstantopoulos, S., Perantonis, S., Karkaletsis, V., Spyropoulos, C.D., Vouros, G. (eds) Artificial Intelligence: Theories, Models and Applications. SETN 2010. Lecture Notes in Computer Science(), vol 6040. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12842-4_50
Download citation
DOI: https://doi.org/10.1007/978-3-642-12842-4_50
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-12841-7
Online ISBN: 978-3-642-12842-4
eBook Packages: Computer ScienceComputer Science (R0)