New approaches based on One-Class SVMS for impulsive sounds recognition tasks

This paper proposes to apply optimized one-class support vector machines (1-SVMs) to tackle some audio recognition tasks. We show that 1-SVMs provide a significant improvement in performance on event detection and classification. We propose an efficient and accurate approach for detecting events in a continuous audio stream. The proposed method which does not require any pre-trained models is based on the use of the exponential family model and 1-SVMs to approximate the generalized likelihood ratio. Besides, we apply novel discriminative algorithms based on 1-SVMs with new dissimilarity measure in order to address a supervised sounds classification task. We illustrate the potential of 1-SVMs on a complex real-world dataset containing impulsive sounds. We compare the novel detection and classification methods with other popular approaches.