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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 Exploiting Temporal Feature Integration for Generalized Sound Recognition | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2009 Article ID 807162 12 pages doi 10.1155 2009 807162 Research Article Exploiting Temporal Feature Integration for Generalized Sound Recognition Stavros Ntalampiras 1 Ilyas Potamitis EURASIP Member 2 and Nikos Fakotakis1 Electrical and Computer Engineering Department University of Patras 26500 Rio-Patras Greece 2Department of Music Technology and Acoustics Technological Educational Institute of Crete Daskalaki-Perivolia Crete 74100 Greece Correspondence should be addressed to Stavros Ntalampiras sntalampiras@upatras.gr Received 13 July 2009 Revised 25 September 2009 Accepted 18 November 2009 Recommended by Douglas O Shaughnessy This paper presents a methodology that incorporates temporal feature integration for automated generalized sound recognition. Such a system can be of great use to scene analysis and understanding based on the acoustic modality. The performance of three feature sets based on Mel filterbank MPEG-7 audio protocol and wavelet decomposition is assessed. Furthermore we explore the application of temporal integration using the following three different strategies a short-term statistics b spectral moments and c autoregressive models. The experimental setup is thoroughly explained and based on the concurrent usage of professional sound effects collections. In this way we try to form a representative picture of the characteristics of ten sound classes. During the first phase of our implementation the process of audio classification is achieved through statistical models HMMs while a fusion scheme that exploits the models constructed by various feature sets provided the highest average recognition rate. The proposed system not only uses diverse groups of sound parameters but also employs the advantages of temporal feature integration. Copyright 2009 Stavros Ntalampiras et al. This is an open access article distributed under the Creative Commons Attribution License
