01551nas 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