One Class Support Vector Machines for audio abnormal events detection
| Autor: | Cédric Richard, Régis Lengellé, Francois Capman, Sebastien Lecomte |
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| Přispěvatelé: | Laboratoire Modélisation et Sûreté des Systèmes (LM2S), Institut Charles Delaunay (ICD), Université de Technologie de Troyes (UTT)-Centre National de la Recherche Scientifique (CNRS)-Université de Technologie de Troyes (UTT)-Centre National de la Recherche Scientifique (CNRS), Thales Communications [Colombes], THALES, Laboratoire Hippolyte Fizeau (FIZEAU), Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire de la Côte d'Azur, Université Côte d'Azur (UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Université Côte d'Azur (UCA)-Centre National de la Recherche Scientifique (CNRS), THALES [France], Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Université Côte d'Azur (UCA)-Université Côte d'Azur (UCA)-Centre National de la Recherche Scientifique (CNRS) |
| Jazyk: | angličtina |
| Rok vydání: | 2011 |
| Předmět: |
Hyperparameter
Computer science business.industry media_common.quotation_subject 02 engineering and technology Machine learning computer.software_genre Support vector machine 03 medical and health sciences 0302 clinical medicine Robustness (computer science) 0202 electrical engineering electronic engineering information engineering Unsupervised learning Decision function 020201 artificial intelligence & image processing Artificial intelligence business computer [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing 030217 neurology & neurosurgery Normality media_common |
| Zdroj: | 2011 IEEE Statistical Signal Processing Workshop (SSP) 2011 IEEE Statistical Signal Processing Workshop (SSP), Jun 2011, Nice, France. pp.489-492, ⟨10.1109/SSP.2011.5967739⟩ |
| DOI: | 10.1109/SSP.2011.5967739⟩ |
| Popis: | International audience; This paper proposes an unsupervised method for real time detection of abnormal events in the context of audio surveillance. Based on training a One-Class Support Vector Machine (OC-SVM) to model the distribution of the normality (ambience), we propose to construct sets of decision functions. This allows controlling the trade-off between false-alarm and miss probabilities without modifying the trained OC-SVM that best capture the ambience boundaries, or its hyperparameters. Then we present an adaptive online scheme of temporal integration of the decision function output in order to increase performance and robustness. We also introduce a framework to generate databases based on real signals for the evaluation of audio surveillance systems. Finally, we present the performances obtained on the databases. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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