Using One-Class SVMs and Wavelets for Audio Surveillance

This paper presents a procedure aimed at recognizing environmental sounds for surveillance and security applications. We propose to apply One-Class Support Vector Machines (1-SVMs) together with a sophisticated dissimilarity measure as a discriminative framework in order to address audio classification, and hence, sound recognition. We illustrate the performance of this method on an audio database, which consists of above 1,000 sounds belonging to 9 classes. Additionally, the use of a set of state-of-the-art audio features is studied. Additionally, we introduce a set of novel features obtained by combining elementary features. Experimental results are presented and show the superiority of this novel sound recognition method. We show that the 1-SVM clearly overperforms the conventional HMM-based system and we emphasize that the largest improvement is achieved when the system is fed by a set of features that comprises wavelet coefficients.

• Type: Journal
• Year: 2008
• Where: TIFS
• Authors: Asma Rabaoui, Manuel Davy, Stéphane Rossignol, Zied Ellouze
• Keywords: Discriminative Framework, One-class Support Vector, Sophisticated Dissimilarity Measure, TIFS 2008