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hctsa feature extraction methods  (SPECTRO Analytical)
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SPECTRO Analytical hctsa feature extraction methods
Sequential steps of the methodology used for evaluating the robustness of individual identity estimation in mammal vocalisations. Notations: spec-temp <t>–</t> <t>spectro-temporal,</t> MFCC – Mel-frequency Cepstral Coefficients, <t>HCTSA</t> - 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.
Hctsa Feature Extraction Methods, supplied by SPECTRO Analytical, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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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.

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