Towards automated monitoring of Orthoptera (and some other noisy stuff)

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

Gryllotalpa vineae

Gryllotalpa vineae

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

Can see high level structure• Periodicity of approximately 1.5s (echeme)


Zoomed in, another level of structure is apparent• Periodicity of approximately 80ms (syllable)


Repeat…• Periodicity of approximately 4ms (pulse)


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


It’s good to have lots of recordings

Dynamic Time Warping

Used to compare temporal sequences that may vary in speed

Can create clusters based on this

Very short recording

Atypical

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)

Towards automated monitoring of Orthoptera (and some other noisy stuff)

Science