@article {58396,
	title = {Descriptions of known vocalisations of the Night Parrot Pezoporus occidentalis},
	journal = {Australian Field Ornithology},
	volume = {36},
	year = {2019},
	month = {Jan-01-2019},
	pages = {79 - 88},
	abstract = {<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>
},
	doi = {10.20938/afo10.20938/afo36079088},
	url = {http://www.birdlife.org.au/afo},
	author = {Leseberg, Nicholas and Murphy, Stephen and Jackett, Nigel and Greatwich, Bruce and Brown, Jamie and Hamilton, Neil and Joseph, Leo and Watson, James}
}
@article {50769,
	title = {Detection of ground parrot vocalisation: A multiple instance learning approach},
	journal = {The Journal of the Acoustical Society of America},
	volume = {142},
	year = {2017},
	month = {Jan-09-2017},
	pages = {1281 - 1290},
	abstract = {<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>
},
	issn = {0001-4966},
	doi = {10.1121/1.4999318},
	url = {http://asa.scitation.org/doi/10.1121/1.4999318},
	author = {Nguyen, Duc Thanh and Ogunbona, Philip O. and Li, Wanqing and Tasker, Elizabeth and Yearwood, John}
}
