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Towards automated monitoring of Orthoptera
(and some other noisy stuff)Ed Baker, Hannah O’Sullivan, Quentin Geissmann
A detour on frogsTachycnemis seychellensis
• Found on a number of islands in the Seychelles
• Phylogeny of populations shows no lineages specific to individual islands
• Analysis of calls (16 parameters in frequency domain from 3 islands)
A detour on frogs
Random forest method (machine learning classification)
Centre mixed as phylogeny would suggest, but differences between islands
Studying acoustics can reveal interesting observations
Having fun in the time domain
• For automated studies lots of work has been done on pulse, and inter-pulse features
• The pulse is the basic element of a song, but there is also higher-level structures (syllables, echemes,… )
• The terminology of higher level structures generally (ideally) reflects biological meaning
Chorthippus dorsatus
Zoomed in, another level of structure is apparent• Periodicity of approximately 80ms (syllable)
Chorthippus dorsatus
Towards computer identification
• The computer does not care about how individual authors define syllables
• Just look for ‘rhythmicity’
In this example the rhythmicity spans over three orders of magnitude
• Continuous wavelet transform
Dynamic Time Warping
Used to compare temporal sequences that may vary in speed
Can create clusters based on this
Time domain analysis
• Beat spectrum has potential for automated identification
• Particularly in combination with frequency domain
Requires:
• Annotated libraries of recorded songs (with multiple recordings of each species)
A new project
Automated Acoustic Observatories
• Three year Leverhulme Trust funded project
• Two main themes
• Evolution of song in the Orthoptera
• Automated identification
Evolution
Supertree of Orthoptera
• 163 source trees
• Mapping of trait data, including acoustics to tree
• Methods from communications theory
As well as understanding the evolution of song, used to guide identification algorithm in categorising unknown songs
Automated Identification
Combine time and frequency domain methods to identify known orthoptera species.
• Acoustic features
• Weighted by location, time of year, habitat (niche models)
Provide as much information as possible about unknown songs
• Automatically record vouchers
• Best-effort attempt to categorise (e.g. family X, call adapted to dense grass)