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MathWorks Inc hctsa
Hctsa, supplied by MathWorks Inc, 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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MathWorks Inc hctsa
Hctsa, supplied by MathWorks Inc, 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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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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Average 90 stars, based on 1 article reviews
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MathWorks Inc highly comparative time-series analysis (hctsa) toolbox
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.
Highly Comparative Time Series Analysis (Hctsa) Toolbox, supplied by MathWorks Inc, 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/result/highly comparative time-series analysis (hctsa) toolbox/product/MathWorks Inc
Average 90 stars, based on 1 article reviews
highly comparative time-series analysis (hctsa) toolbox - by Bioz Stars, 2026-05
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MathWorks Inc matlab-based highly comparative time series analysis (hctsa) toolbox
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.
Matlab Based Highly Comparative Time Series Analysis (Hctsa) Toolbox, supplied by MathWorks Inc, 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/result/matlab-based highly comparative time series analysis (hctsa) toolbox/product/MathWorks Inc
Average 90 stars, based on 1 article reviews
matlab-based highly comparative time series analysis (hctsa) toolbox - by Bioz Stars, 2026-05
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MathWorks Inc highly comparable time series analysis (hctsa) toolbox
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.
Highly Comparable Time Series Analysis (Hctsa) Toolbox, supplied by MathWorks Inc, 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/result/highly comparable time series analysis (hctsa) toolbox/product/MathWorks Inc
Average 90 stars, based on 1 article reviews
highly comparable time series analysis (hctsa) toolbox - by Bioz Stars, 2026-05
90/100 stars
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MathWorks Inc hctsa matlab package
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 Matlab Package, supplied by MathWorks Inc, 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/result/hctsa matlab package/product/MathWorks Inc
Average 90 stars, based on 1 article reviews
hctsa matlab package - by Bioz Stars, 2026-05
90/100 stars
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MathWorks Inc hctsa software tool
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 Software Tool, supplied by MathWorks Inc, 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/result/hctsa software tool/product/MathWorks Inc
Average 90 stars, based on 1 article reviews
hctsa software tool - by Bioz Stars, 2026-05
90/100 stars
  Buy from Supplier

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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