Abstract
This paper is focused on the acoustic events detection. Particularly two types of acoustic events (gun shot, breaking glass) were investigated. For any detection task the feature extraction methods play very important role. The feature extraction influences the recognition rate, therefore it is most important in any pattern recognition task. In this paper the impact of Mel-Frequency Cepstral Coefficients - MFCC and selected set of MPEG-7 low-level descriptors were examined. The best feature set contained MFCC and selected descriptors such as ASC, ASS, ASF. They were used to represent the sounds of acoustic events and background. We obtained the improvement of the detection rate using the mentioned set of features. In this task GMM classifiers are used to model the sound classes. This paper describes a basic aspect of our work.
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.
References
Huang, W., Lau, S., Tan, T., Li, L., Wyse, L.: Audio events classification using hierarchical structure. In: ICICS-PCM, pp. 1299–1303 (2003)
Ghulam, M., Yousef, A.A., Mansour, A., Mohammad, N.H.: Environment recognition using selected MPEG-7 audio features and Mel-Frequency Cepstral Coefficients. In: International Conference on Digital Telecommunications, pp. 11–16 (2010)
Cristiani, M., Bicego, M., Murino, V.: Audio-visual event recognition in surveillance video sequences. IEEE Transactions on Multimedia 9/2, 257–266 (2007)
Temko, A., Malkin, R., Zieger, C., Macho, D., Nadeu, C., Omologo, M.: Acoustic Event Detection and Classification in Smart-Room Environment: Evaluation of CHIL Project Systems. In: The IV Biennial Workshop on Speech Technology (2006)
Rougui, J.E., Istrate, D., Souidene, W.: Audio sound event identification for distress situations and context awareness. In: International Conference of Engineering in Medicine and Biology Society, Minneapolis, September 3-6, pp. 3501–3504 (2009)
Zheng, F., Zhang, G., Song, Z.: Comparison of different implementations of MFCC. Journal of Computer Science and Technology 16/6, 582–589 (2001)
Psutka, J., Müller, L., Psutka, J.V.: Comparison of MFCC and PLP parametrizations in the speaker independent continuous speech recognition task. In: Eurospeech, Aalborg, September 3-7, pp. 1813–1816 (2001)
Mitrovic, D., Zeppelzauer, M., Eidenberger, H.: Analysis of the data quality of audio descriptions of environmental sounds. Journal of Digital Information Management 5/2, 48–55 (2007)
Casey, M.: General sound classification and similarity in MPEG-7, pp. 153–164. Cambridge University Press, Cambridge (2001)
Kim, H.G., Moreau, N., Sikora, T.: MPEG-7 audio and beyond: Audio content indexing and retrieval, p. 304. Wiley, Chichester (2005); ISBN: 978-0-470-09334-4
Ntalampiras, S., Potamitis, I., Fakotakis, N.: Automatic recognition of urban environmental sounds events. In: CIP, Santorini, June 9-10, pp. 110–113 (2008)
Young, S., et al.: The HTK Book. Cambridge University, Cambridge (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Vozáriková, E., Juhár, J., Čižmár, A. (2011). Acoustic Events Detection Using MFCC and MPEG-7 Descriptors. In: Dziech, A., Czyżewski, A. (eds) Multimedia Communications, Services and Security. MCSS 2011. Communications in Computer and Information Science, vol 149. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21512-4_23
Download citation
DOI: https://doi.org/10.1007/978-3-642-21512-4_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21511-7
Online ISBN: 978-3-642-21512-4
eBook Packages: Computer ScienceComputer Science (R0)