Abstract
This paper is focused on the detection of abnormal situations via sound information. As a main feature extraction algorithm, basic spectral low - level descriptors defined in MPEG-7 standard were used. Various settings for spectral descriptors such as Audio Spectrum Envelope, Audio Spectrum Flatness, Audio Spectrum Centroid and Audio Spectrum Spread were used and many experiments were done for finding the limits of using them for the purpose of acoustic event detection in urban environment. For improving the recognition rate we also applied the feature selection algorithm called Minimum Redundancy Maximum Relevance. The proposed framework of recognizing potentially dangerous acoustic events such as breaking glass and gun shots, based on the extraction of basic spectral descriptors through well known Hidden Markov Models based classification is presented here.
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Vozarikova, E., Lojka, M., Juhar, J., Cizmar, A. (2012). Performance of Basic Spectral Descriptors and MRMR Algorithm to the Detection of Acoustic Events. In: Dziech, A., Czyżewski, A. (eds) Multimedia Communications, Services and Security. MCSS 2012. Communications in Computer and Information Science, vol 287. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30721-8_34
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DOI: https://doi.org/10.1007/978-3-642-30721-8_34
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