FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4385399143> ?p ?o ?g. }

• W4385399143 endingPage "34" @default.
• W4385399143 startingPage "19" @default.
• W4385399143 abstract "In the field of wildlife observation and conservation, approaches involving machine learning on audio recordings are becoming increasingly popular. Unfortunately, available datasets from this field of research are often not optimal learning material; Samples can be weakly labeled, of different lengths or come with a poor signal-to-noise ratio. In this work, we introduce a generalized approach that first relabels subsegments of MEL spectrogram representations, to achieve higher performances on the actual multi-class classification tasks. For both the binary pre-sorting and the classification, we make use of convolutional neural networks (CNN) and various data-augmentation techniques. We showcase the results of this approach on the challenging ComparE 2021 dataset, with the task of classifying between different primate species sounds, and report significantly higher Accuracy and UAR scores in contrast to comparatively equipped model baselines." @default.
• W4385399143 created "2023-07-31" @default.
• W4385399143 creator A5049855435 @default.
• W4385399143 creator A5057818846 @default.
• W4385399143 creator A5064469861 @default.
• W4385399143 creator A5069846773 @default.
• W4385399143 creator A5083875469 @default.
• W4385399143 creator A5092362837 @default.
• W4385399143 date "2023-01-01" @default.
• W4385399143 modified "2023-10-18" @default.
• W4385399143 title "Improving Primate Sounds Classification Using Binary Presorting for Deep Learning" @default.
• W4385399143 cites W1182535303 @default.
• W4385399143 cites W1534477342 @default.
• W4385399143 cites W1977214418 @default.
• W4385399143 cites W1982320825 @default.
• W4385399143 cites W2005008005 @default.
• W4385399143 cites W2010851696 @default.
• W4385399143 cites W2060399587 @default.
• W4385399143 cites W2097117768 @default.
• W4385399143 cites W2114455599 @default.
• W4385399143 cites W2155524176 @default.
• W4385399143 cites W2194775991 @default.
• W4385399143 cites W2272905828 @default.
• W4385399143 cites W2526050071 @default.
• W4385399143 cites W2538812409 @default.
• W4385399143 cites W2585464829 @default.
• W4385399143 cites W2591013610 @default.
• W4385399143 cites W2954996726 @default.
• W4385399143 cites W2963099423 @default.
• W4385399143 cites W2963446712 @default.
• W4385399143 cites W2963451564 @default.
• W4385399143 cites W2963460857 @default.
• W4385399143 cites W2963669405 @default.
• W4385399143 cites W2977423666 @default.
• W4385399143 cites W2978506400 @default.
• W4385399143 cites W3012971020 @default.
• W4385399143 cites W3016228121 @default.
• W4385399143 cites W3033798382 @default.
• W4385399143 cites W3049446265 @default.
• W4385399143 cites W3097426020 @default.
• W4385399143 cites W3112657180 @default.
• W4385399143 cites W3172534252 @default.
• W4385399143 cites W3173678936 @default.
• W4385399143 cites W3196831814 @default.
• W4385399143 cites W3196959475 @default.
• W4385399143 cites W3197490082 @default.
• W4385399143 cites W3197596377 @default.
• W4385399143 cites W3198360817 @default.
• W4385399143 cites W3198909755 @default.
• W4385399143 cites W4224917059 @default.
• W4385399143 cites W4312235993 @default.
• W4385399143 doi "https://doi.org/10.1007/978-3-031-39059-3_2" @default.
• W4385399143 hasPublicationYear "2023" @default.
• W4385399143 type Work @default.
• W4385399143 citedByCount "0" @default.
• W4385399143 crossrefType "book-chapter" @default.
• W4385399143 hasAuthorship W4385399143A5049855435 @default.
• W4385399143 hasAuthorship W4385399143A5057818846 @default.
• W4385399143 hasAuthorship W4385399143A5064469861 @default.
• W4385399143 hasAuthorship W4385399143A5069846773 @default.
• W4385399143 hasAuthorship W4385399143A5083875469 @default.
• W4385399143 hasAuthorship W4385399143A5092362837 @default.
• W4385399143 hasConcept C108583219 @default.
• W4385399143 hasConcept C111696304 @default.
• W4385399143 hasConcept C11413529 @default.
• W4385399143 hasConcept C119857082 @default.
• W4385399143 hasConcept C12267149 @default.
• W4385399143 hasConcept C153180895 @default.
• W4385399143 hasConcept C154945302 @default.
• W4385399143 hasConcept C162324750 @default.
• W4385399143 hasConcept C187736073 @default.
• W4385399143 hasConcept C202444582 @default.
• W4385399143 hasConcept C2777212361 @default.
• W4385399143 hasConcept C2780451532 @default.
• W4385399143 hasConcept C28490314 @default.
• W4385399143 hasConcept C33923547 @default.
• W4385399143 hasConcept C41008148 @default.
• W4385399143 hasConcept C45273575 @default.
• W4385399143 hasConcept C48372109 @default.
• W4385399143 hasConcept C66905080 @default.
• W4385399143 hasConcept C81363708 @default.
• W4385399143 hasConcept C94375191 @default.
• W4385399143 hasConcept C9652623 @default.
• W4385399143 hasConceptScore W4385399143C108583219 @default.
• W4385399143 hasConceptScore W4385399143C111696304 @default.
• W4385399143 hasConceptScore W4385399143C11413529 @default.
• W4385399143 hasConceptScore W4385399143C119857082 @default.
• W4385399143 hasConceptScore W4385399143C12267149 @default.
• W4385399143 hasConceptScore W4385399143C153180895 @default.
• W4385399143 hasConceptScore W4385399143C154945302 @default.
• W4385399143 hasConceptScore W4385399143C162324750 @default.
• W4385399143 hasConceptScore W4385399143C187736073 @default.
• W4385399143 hasConceptScore W4385399143C202444582 @default.
• W4385399143 hasConceptScore W4385399143C2777212361 @default.
• W4385399143 hasConceptScore W4385399143C2780451532 @default.
• W4385399143 hasConceptScore W4385399143C28490314 @default.
• W4385399143 hasConceptScore W4385399143C33923547 @default.
• W4385399143 hasConceptScore W4385399143C41008148 @default.

• next