1One-class SVMs challenges in audio detection and classification applications

Support Vector Machines (SVMs) have gained great attention and have been used extensively and successfully in the field of sounds (events) recognition. However, the extension of SVMs to real-world signal processing applications is still an ongoing research topic. Our work consists on illustrating the potential of SVMs on recognizing impulsive audio signals belonging to a complex real-world dataset. We propose to apply optimized One-Class Support Vector Machines (1-SVMs) to tackle both audio detection and classification tasks in the recognition process. First, we propose an efficient and accurate approach for detecting events in a continuous audio stream. The proposed unsupervised detection 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. Then, we apply novel discriminative algorithms based on 1-SVMs with new dissimilarity measure in order to address a supervised sounds classification task. We compare the novel detection and classification methods with other popular approaches. The remarkable recognition results achieved in our experiments illustrate the potential of these methods and indicate that 1-SVMs are well-suited for event recognition tasks