01559nas a2200217   4500008004100000245008300041210006900124260001600193300001200209490000700221520092500228100002301153700002001176700001901196700002101215700001701236700001901253700001601272700001801288856003501306       2019                        eng d00aDescriptions of known vocalisations of the Night Parrot Pezoporus occidentalis0 aDescriptions of known vocalisations of the Night Parrot Pezoporu  cJan-01-2019  a79 - 880 v363 a<p>Until recently, the only descriptions of Night Parrot Pezoporus occidentalis vocalisations were anecdotal reports from observers in the late 19th and early 20th centuries. The 2013 discovery of an extant population of Night Parrots in western Queensland led to the first contemporary descriptions, and further calls were described following the 2017 discovery of Night Parrots at a site in central Western Australia. Ongoing field studies have shown that the species has a complex repertoire and, although there are some similarities in vocalisations between sites, there is much spatial and temporal variation. It is likely that call recognition will be the primary means of detection for this cryptic species, underscoring the importance of cataloguing vocalisations across the species&rsquo; range. Accordingly, we describe all known vocalisations from the three sites where Night Parrots are known to occur.</p>
1 aLeseberg, Nicholas1 aMurphy, Stephen1 aJackett, Nigel1 aGreatwich, Bruce1 aBrown, Jamie1 aHamilton, Neil1 aJoseph, Leo1 aWatson, James  uhttp://www.birdlife.org.au/afo01551nas a2200193   4500008004100000022001400041245008300055210006900138260001600207300001600223490000800239520095400247100002301201700002501224700001601249700002201265700001901287856005101306       2017                        eng d  a0001-496600aDetection of ground parrot vocalisation: A multiple instance learning approach0 aDetection of ground parrot vocalisation A multiple instance lear  cJan-09-2017  a1281 - 12900 v1423 a<p>Ground parrot vocalisation can be considered as an audio event. Test-based diverse density multiple instance learning (TB-DD-MIL) is proposed for detecting this event in audio files recorded in the field. The proposed method is motivated by the advantages of multiple instance learning from incomplete training data. Spectral features suitable for encoding the vocal source information of the ground parrot vocalization are also investigated. The proposed method was benchmarked against a dataset collected in various environmental conditions and an audio detection evaluation scheme is proposed. The evaluation includes a study on performance of the various vocal source features and comparison with other classification techniques. Experimental results indicated that the most appropriate feature to encode ground parrot calls is the spectral bandwidth and the proposed TB-DD-MIL method outperformed other existing classification methods.</p>
1 aNguyen, Duc, Thanh1 aOgunbona, Philip, O.1 aLi, Wanqing1 aTasker, Elizabeth1 aYearwood, John  uhttp://asa.scitation.org/doi/10.1121/1.4999318