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 of 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 sound detection and classification tasks in the sound recognition process. First, we propose an efficient and accurate approach for detecting events in a continuous audio stream. The proposed unsupervised sound detection method which does not require any pretrained 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 sound-classification task. We compare the novel sound detection and classification methods with other popular approaches. The remarkable sound recognition results achieved in our experiments illustrate the potential of these methods and indicate that 1-SVMs are well suited for event-recognition tasks.

This paper introduces a new method of heartbeat classification based on the support vector machine classifier using morphological descriptors and High Order Statistic using MIT/BIH Arrhythmia database. Using the morphological descriptors and polynomial kernel, we have obtained an average sensitivity equal to 89,92% and an average specificity about 82,45%, and in the case of Gaussian kernel, we have obtained an average sensitivity equal to 94,26% and an average specificity about 79,02%. Using the High Order Statistic and polynomial kernel, we have obtained an average sensitivity equal to 95,86% and an average specificity about 90,20%, and in the case of Gaussian kernel, we have obtained an average sensitivity equal to 97,15% and an average specificity about 93,07%. The association of the two parameters increases the averages of classification rates; so the sensitivity is 98,38% and the specificity to 94,87% with polynomial kernel and respectively about 94,43% et 95,81 % with Gaussian kernel.