Journal: bioRxiv
Article Title: Same data, different results? Evaluating machine learning approaches for individual identification in animal vocalisations
doi: 10.1101/2024.04.14.589403
Figure Lengend Snippet: Sequential steps of the methodology used for evaluating the robustness of individual identity estimation in mammal vocalisations. Notations: spec-temp – spectro-temporal, MFCC – Mel-frequency Cepstral Coefficients, HCTSA - Highly Comparative Time Series Analysis, DFA – Discriminant Function Analysis, NN – Neural Networks, SVM – Support Vector Machines, RF – Random Forest. Grey arrows indicate a subset of the former group. Orange outlines indicate data used in dataset 2.
Article Snippet: A greater variation in the performance of classifiers was observed when using the spectro-temporal dataset (Δ = 0.184), and to an even greater extent, HCTSA feature extraction methods (Δ = 0.632).
Techniques: Plasmid Preparation