Optimizing Acoustic Signal Detection

Documentation for package ‘ohun’ version 1.0.2

consensus_detection	Remove ambiguous detections
diagnose_detection	Evaluate the performance of a sound event detection procedure
energy_detector	Detects the start and end of sound events
get_envelopes	Extract absolute amplitude envelopes
get_templates	Find templates representative of the structural variation of sound events
label_detection	Label detections from a sound event detection procedure
label_spectro	Plot a labeled spectrogram
lbh1	Long-billed hermit recording
lbh2	Long-billed hermit recording
lbh_reference	Example data frame of a selection table including all sound events of interests
merge_overlaps	Merge overlapping selections
optimize_energy_detector	Optimize energy-based sound event detection
optimize_template_detector	Optimize acoustic template detection
plot_detection	Plot detection and reference annotations
split_acoustic_data	Splits sound files and associated annotations
summarize_acoustic_data	Summarize information about file format in an acoustic data set
summarize_diagnostic	Summarize detection diagnostics
summarize_reference	Summarize temporal and frequency dimensions of annotations and gaps
template_correlator	Acoustic templates correlator using time-frequency cross-correlation
template_detector	Acoustic template detection from time-frequency cross-correlations