Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article One-Class SVMs Challenges in Audio Detection and Classification Applications | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2008 Article ID 834973 14 pages doi 2008 834973 Research Article One-Class SVMs Challenges in Audio Detection and Classification Applications Asma Rabaoui Hachem Kadri Zied Lachiri and Noureddine Ellouze Unite de Recherche Signal Image et Reconnaissance des Formes Ecole Nationale d Ingenieurs de Tunis ENIT BP 37 Campus Universitaire 1002 Tunis Tunisia Correspondence should be addressed to Asma Rabaoui Received 2 October 2007 Revised 7 January 2008 Accepted 24 April 2008 Recommended by Sergios Theodoridis 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 ofthe exponential family modeland 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. Copyright 2008 Asma Rabaoui et al. This is an open access article .
