02053nas a2200205   4500008004100000245007200041210006900113300001000182490000700192520139600199653001601595653001701611653002101628653002401649653001901673653002201692100002401714700001401738856009501752       2014                        eng d00aDecision support for the efficient annotation of bioacoustic events0 aDecision support for the efficient annotation of bioacoustic eve  a14-210 v253 a<p>Acoustic sensors allow scientists to scale environmental monitoring over large spatiotemporal scales. The faunal vocalisations captured by these sensors can answer ecological questions, however, identifying these vocalisations within recorded audio is difficult: automatic recognition is currently intractable and manual recognition is slow and error prone. In this paper, a semi-automated approach to call recognition is presented. An automated decision support tool is tested that assists users in the manual annotation process. The respective strengths of human and computer analysis are used to complement one another. The tool recommends the species of an unknown vocalisation and thereby minimises the need for the memorization of a large corpus of vocalisations. In the case of a folksonomic tagging system, recommending species tags also minimises the proliferation of redundant tag categories.</p>

<p>We describe two algorithms: (1) a “naïve” decision support tool (16%–64% sensitivity) with efficiency of O(n) but which becomes unscalable as more data is added and (2) a scalable alternative with 48% sensitivity and an efficiency ofO(log n). The improved algorithm was also tested in a HTML-based annotation prototype. The result of this work is a decision support tool for annotating faunal acoustic events that may be utilised by other bioacous- tics projects.</p>10aannotations10abioacoustics10adecision support10aFaunal vocalisation10asemi-automated10aSimilarity search1 aTruskinger, Anthony1 aRoe, Paul  uhttps://bio.acousti.ca/es/content/decision-support-efficient-annotation-bioacoustic-events