nccratliri/vad-bengalese-finch

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The declared licence (apache-2.0) is classified as permissive.

Declared licence: apache-2.0, from the tagEvidence: README.md at 9933c06
Licence files: No licence file at the top level of the repositoryEvidence: file list at 9933c06
Declared sources: None listed in the card metadata
Access gated on Hugging Face: No

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Citation text from the card

@article {10.7554/eLife.68837,
article_type = {journal},
title = {Fast and accurate annotation of acoustic signals with deep neural networks},
author = {Steinfath, Elsa and Palacios-Muñoz, Adrian and Rottschäfer, Julian R and Yuezak, Deniz and Clemens, Jan},
editor = {Calabrese, Ronald L and Egnor, SE Roian and Troyer, Todd},
volume = 10,
year = 2021,
month = {nov},
pub_date = {2021-11-01},
pages = {e68837},
citation = {eLife 2021;10:e68837},
doi = {10.7554/eLife.68837},
url = {https://doi.org/10.7554/eLife.68837},
abstract = {Acoustic signals serve communication within and across species throughout the animal kingdom. Studying the genetics, evolution, and neurobiology of acoustic communication requires annotating acoustic signals: segmenting and identifying individual acoustic elements like syllables or sound pulses. To be useful, annotations need to be accurate, robust to noise, and fast. We here introduce \textit{DeepAudioSegmenter} (\textit{DAS)}, a method that annotates acoustic signals across species based on a deep-learning derived hierarchical presentation of sound. We demonstrate the accuracy, robustness, and speed of \textit{DAS} using acoustic signals with diverse characteristics from insects, birds, and mammals. \textit{DAS} comes with a graphical user interface for annotating song, training the network, and for generating and proofreading annotations. The method can be trained to annotate signals from new species with little manual annotation and can be combined

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dataset_licence({ "id": "nccratliri/vad-bengalese-finch" })
signal: "permissive"
stated_licence: "apache-2.0"
sources: 0