%0 Journal Article %A Asma Rabaoui and Zied Lachiri and Noureddine Ellouze %D 2009 %J International Journal of Computer and Information Engineering %B World Academy of Science, Engineering and Technology %I Open Science Index 35, 2009 %T Using HMM-based Classifier Adapted to Background Noises with Improved Sounds Features for Audio Surveillance Application %U https://publications.waset.org/pdf/12203 %V 35 %X Discrimination between different classes of environmental sounds is the goal of our work. The use of a sound recognition system can offer concrete potentialities for surveillance and security applications. The first paper contribution to this research field is represented by a thorough investigation of the applicability of state-of-the-art audio features in the domain of environmental sound recognition. Additionally, a set of novel features obtained by combining the basic parameters is introduced. The quality of the features investigated is evaluated by a HMM-based classifier to which a great interest was done. In fact, we propose to use a Multi-Style training system based on HMMs: one recognizer is trained on a database including different levels of background noises and is used as a universal recognizer for every environment. In order to enhance the system robustness by reducing the environmental variability, we explore different adaptation algorithms including Maximum Likelihood Linear Regression (MLLR), Maximum A Posteriori (MAP) and the MAP/MLLR algorithm that combines MAP and MLLR. Experimental evaluation shows that a rather good recognition rate can be reached, even under important noise degradation conditions when the system is fed by the convenient set of features. %P 2609 - 2618