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Automatic bird species recognition method using their voices is presented in this paper. The selected bird species have been detected by hidden Markov models (HMM) classifier using Mel-frequency cepstral coefficients (MFCC). In order to support recognition process, analysed signals have been appropriately filtered before classification in the so called prefiltration process. The prefiltration strategy assumed using n-th order IIR Butterworth filter bank. Each filter from the filter bank was applied for band pass filtration in the bird species specific and signal type band. Increase of recognition accuracy has been observed in case of prefiltration with properly chosen filter order. Experiments have been carried out on the set of bird voices containing 30 bird species, one of which is endangered with extinction.

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International audienceThis paper presents a framework combining BPMN and BR as a tool for Knowledge Management (KM). An attempt at providing a common model supported with Artificial Intelligence (AI) techniques and tools is put forward. Through an extended example it is shown how to combine BPMN and BR and how to pass to semantic level enabling building executable specifications and knowledge analysis. Some of the problems concerning these two approaches can be to certain degree overcome thanks to their complementary nature. We only deal with a restricted view of Knowledge Management, where knowledge can be modeled explicitly in a formal representation, and it does not take into account the hidden, personal knowledge

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