Figure 3

Comparison of FlySongSegmenter with hand-annotated song. We examined the accuracy of FlySongSegmenter by comparing automated segmentation with manual segmentation (manual annotation was performed on 60 seconds of data starting at minute 5 of a recording from each of ten males). (a) Approximately 5 s from a recording from one male showing pulses detected by FlySongSegmenter (blue and red) and manually (black) and sine trains detected by FlySongSegmenter (red) and manually (black). False positive pulses (arrow) are typically removed by winnowing with the pulse model. (b-d) The pulses found before and after model-based culling by FlySongSegmenter were compared to those identified manually (see Methods for more information on these measures). Culling pulses with the pulse model reduced sensitivity (b), but increased the positive predictive value (c). Overall, all methods of culling pulses resulted in similar, high overall accuracy, as measured by the F-score (d). (e-g) FlySongSegmenter often estimated shorter (e) and more (f) sine trains than manual annotation. Overall, FlySongSegmenter estimated approximately equal amounts of total sine song as manual annotation did (g). (h, i) Histograms of inter-pulse interval distributions (pooled across n = 10) of all pulses detected manually (h) or reported in Pulses.ModelCull (i). Overlaid on the histograms are fits from a two-component Gaussian mixture model (black and red lines) and the two underlying Gaussian components (grey lines). The inset in i shows just the mixture model fits for both datasets. IPI: inter-pulse interval.