Accuracy of three modelling methods in detecting feeding buzzes in test-data.

<p>The accuracy in detecting feeding buzzes in test-data (i.e. proportion of accurately classified passes) was examined for three classification methods (LDA: Linear discriminant analysis; RF: Random forests; ANN: Artificial neural networks) at incremental detection threshold levels.</p> <p>(PDF)</p>

Publication Details

Journal
Figshare
Published
2024-08-20
DOI
https://doi.org/10.1371/journal.pone.0306063.s005
Primary Topic
Speech Recognition and Synthesis
Type
article
Field-Weighted Citation Impact
0.00
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article

Accuracy of three modelling methods in detecting feeding buzzes in test-data.

Figshare
Speech Recognition and Synthesis
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Accuracy of three modelling methods in detecting feeding buzzes in test-data.

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Abstract

<p>The accuracy in detecting feeding buzzes in test-data (i.e. proportion of accurately classified passes) was examined for three classification methods (LDA: Linear discriminant analysis; RF: Random forests; ANN: Artificial neural networks) at incremental detection threshold levels.</p> <p>(PDF)</p>

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Accuracy of three modelling methods in detecting feeding buzzes in test-data. · Figshare (2024) | TGRS Research Map | TGRS