|Publication Type:||Journal Article|
|Year of Publication:||2017|
|Alkuperäinen tekijä:||Nguyen, DThanh, Ogunbona, PO, Li, W, Tasker, E, Yearwood, J|
|Journal:||The Journal of the Acoustical Society of America|
|Pagination:||1281 - 1290|
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.
|Short Title:||The Journal of the Acoustical Society of America|
Detection of ground parrot vocalisation: A multiple instance learning approach