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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
https://www.bioz.com/product/highly+comparable+time+series+analysis+%28hctsa%29+toolbox/bio_rxiv__2024__04__14__589403-108-24-13?v=SPECTRO+Analytical
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hctsa feature extraction methods - by Bioz Stars, 2026-07
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haematological analyser abx micros 16 ot  (HORIBA Ltd)
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HORIBA Ltd haematological analyser abx micros 16 ot
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.
Haematological Analyser Abx Micros 16 Ot, supplied by HORIBA Ltd, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/highly+comparable+time+series+analysis+%28hctsa%29+toolbox/10__2478_slash_humo___2013___0018-54-13-11?v=HORIBA+Ltd
Average 90 stars, based on 1 article reviews
haematological analyser abx micros 16 ot - by Bioz Stars, 2026-07
90/100 stars
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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