Improved one-class SVM classifier for sounds classification

Asma Rabaoui 1 Manuel Davy 2 Stéphane Rossignol 3 Zied Lachiri 1 Noureddine Ellouze 1
1 ENIT - Ecole Nationale d'Ingénieurs de Tunis
2 LAGIS-SI
LAGIS - Laboratoire d'Automatique, Génie Informatique et Signal
3 LAGIS - Laboratoire d'Automatique, Génie Informatique et Signal
Abstract : This paper proposes to apply optimized One-Class Support Vector Machines (1-SVMs) as a discriminative framework in order to address a specific audio classification problem. First, since SVM-based classifier with gaussian RBF kernel is sensitive to the kernel width, the width will be scaled in a distribution-dependent way permitting to avoid underfitting and over-fitting problems. Moreover, an advanced dissimilarity measure will be introduced. We illustrate the performance of these methods on an audio database containing environmental sounds that may be of great importance for surveillance and security applications. The experiments conducted on a multi-class problem show that by choosing adequately the SVM parameters, we can efficiently address a sounds classification problem characterized by complex real-world datasets.
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Asma Rabaoui, Manuel Davy, Stéphane Rossignol, Zied Lachiri, Noureddine Ellouze. Improved one-class SVM classifier for sounds classification. IEEE Conference on Advanced Video and Signal Based Surveillance. (AVSS 2007), Sep 2007, London, United Kingdom. pp.117-122, ⟨10.1109/AVSS.2007.4425296⟩. ⟨hal-00259027⟩

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