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DenaJGibbon/README.md

Hi there 👋

Welcome to Dena J. Clink's GitHub page. This is where I archive my code from peer-reviewed publications, and highlight current projects I am working on.

R Packages

gibbonR - R package for automated detection, classification and visualization of acoustic signals using traditional machine learning
https://github.com/DenaJGibbon/gibbonR

gibbonNetR - R package for automated detection, classification and visualization of acoustic signals using deep learning
https://github.com/DenaJGibbon/gibbonNetR

behaviouR - R package for online teaching of fundamental concepts in behavior and ecology
https://github.com/DenaJGibbon/behaviouR

Publications

You can access PDFs of all of my publications on my website: https://www.denaclink.com/.

Tutorials

I am slowly starting to convert my code into tutorials. You can access tutorials here: https://www.denaclink.com/post/.

The R code for select publications is available here.

Clink, D. J., Zafar, M. *, Ahmad, A.H. & Lau, A.R. * (2021). Limited evidence for individual signatures or site-level patterns of variation in male Northern gray gibbon (Hylobates funereus) duet codas. International Journal of Primatology. 896-914. Code here.

Clink, D. J., Groves, T.*, Ahmad, A.H. & H. Klinck. (2021). Not by the light of the moon: investigating circadian rhythms and ecological predictors of calling in Bornean great argus. PLOS ONE. 16: e0246564. Code here.

Clink, D. J., Crofoot, M. C., & A.J. Marshall. (2018). Application of a semi-automated vocal fingerprinting approach to monitor Bornean gibbon females in an experimentally fragmented landscape in Sabah, Malaysia. Bioacoustics. 1-17. Code here.

Clink, D. J., Grote, M.N., Crofoot, M. C., & A.J. Marshall. (2018). Understanding sources of variance and correlation among features of Bornean gibbon (Hylobates muelleri) female calls. Journal of the Acoustical Society of America. 142: 1-11. Code here.

Clink, D. J., Charif, R.A., Crofoot, M. C., & A.J. Marshall. (2018). Evidence for vocal performance constraints in a female non-human primate. Animal Behaviour. 141: 85-94. Code here.

Pinned

  1. gibbonR gibbonR Public

    An R package for the detection and classification of acoustic signals using machine learning

    R 14 3

  2. behaviouR behaviouR Public

    an R package for online teaching of fundamental concepts in behavior and ecology

    R 4 1

  3. gibbonID gibbonID Public

    The goal of gibbonID is make clustering and visualization of acoustic data in R easy.

    R

  4. MFCC-Vocal-Fingerprinting MFCC-Vocal-Fingerprinting Public

    Code to do MFCC feature extraction on gibbon calls and use LDA/SVM for classification

    R 7

  5. Performance-Constraints Performance-Constraints Public

    Code to calculate bandwidth and signal-to-noise ratio of trill notes, along with model selection

    R