{"refrec":{"BRefID":229193,"RR":"<b>Gillespie, D.; Caillat, M.; Gordon, J.</b> (2013). Automatic detection and classification of odontocete whistles. <i>J. Acoust. Soc. Am. 134(3)</i>: 2427-2437. <a href=\"http://dx.doi.org/10.1121/1.4816555\" target=\"_blank\">http://dx.doi.org/10.1121/1.4816555</a>","BEntID":220914,"PublicFlag":1,"CheckedFlag":1,"wosflag":1,"vabbflag":1,"RefStringPartII":". <i>J. Acoust. Soc. Am. 134(3)</i>: 2427-2437. <a href=\"http://dx.doi.org/10.1121/1.4816555\" target=\"_blank\">http://dx.doi.org/10.1121/1.4816555</a>","DocTypID":8,"DocType":"Journal article","MarineFlag":1,"FreshFlag":0,"BrackishFlag":0,"TerrestrialFlag":0,"Authorstring":"Gillespie, D.; Caillat, M.; Gordon, J.","OrigTitleTranslFlag":0,"Authorstringtrunc":"Gillespie, D. <i>et al.</i>","Englishabstract":"Methods for the fully automatic detection and species classification of odontocete whistles are described. The detector applies a number of noise cancellation techniques to a spectrogram of sound data and then searches for connected regions of data which rise above a pre-determined threshold. When tested on a dataset of recordings which had been carefully annotated by a human operator, the detector was able to detect (recall) 79.6% of human identified sounds that had a signal-to-noise ratio above 10?dB, with 88% of the detections being valid. A significant problem with automatic detectors is that they tend to partially detect whistles or break whistles into several parts. A classifier has been developed specifically to work with fragmented whistle detections. By accumulating statistics over many whistle fragments, correct classification rates of over 94% have been achieved for four species. 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