期刊
BIOACOUSTICS-THE INTERNATIONAL JOURNAL OF ANIMAL SOUND AND ITS RECORDING
卷 25, 期 2, 页码 177-196出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/09524622.2015.1133320
关键词
Acoustics; spectrogram; correlation matching; template matching; template detection; monitoR
类别
资金
- U.S. National Park Service [P10AC00288]
- Vermont Cooperative Fish and Wildlife Research Unit (VTCFWRU)
- U.S. Geological Survey
- University of Vermont
- Vermont Department of Fish and Wildlife
- Wildlife Management Institute
Detecting population-scale reactions to climate change and landuse change may require monitoring many sites for many years, a process that is suited for an automated system. We developed and tested monitoR, an R package for long-term, multi-taxa acoustic monitoring programs. We tested monitoR with two northeastern songbird species: black-throated green warbler (Setophaga virens) and ovenbird (Seiurus aurocapilla). We compared detection results from monitoR in 52 10-minute surveys recorded at 10 sites in Vermont and New York, USA to a subset of songs identified by a human that were of a single song type and had visually identifiable spectrograms (e.g. a signal: noise ratio of at least 10 dB: 166 out of 439 total songs for black-throated green warbler, 502 out of 990 total songs for ovenbird). monitoR's automated detection process uses a 'score cutoff', which is the minimum match needed for an unknown event to be considered a detection and results in a true positive, true negative, false positive or false negative detection. At the chosen score cutoffs, monitoR correctly identified presence for black-throated green warbler and ovenbird in 64% and 72% of the 52 surveys using binary point matching, respectively, and 73% and 72% of the 52 surveys using spectrogram cross-correlation, respectively. Of individual songs, 72% of black-throated green warbler songs and 62% of ovenbird songs were identified by binary point matching. Spectrogram cross-correlation identified 83% of black-throated green warbler songs and 66% of ovenbird songs. False positive rates were < 1% for song event detection.
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