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Deep convolutional network for animal sound classification and source attribution using dual audio recordings
This paper introduces an end-to-end feedforward convolutional neural network that is able to reliably classify the source and type of animal calls in a noisy environment using two streams of audio data after being trained on a dataset of modest size and imperfect labels. The data consists of audio r...
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| Publicado no: | J Acoust Soc Am |
|---|---|
| Main Authors: | , , , , , , , , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
Acoustical Society of America
2019
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC6786887/ https://ncbi.nlm.nih.gov/pubmed/30823820 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1121/1.5087827 |
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