cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.10843991
title: The track 1 evaluation dataset of ADD 2022

description: Audio deepfake detection is an emerging topic, which was included in the ASVspoof 2021. However, the recent shared tasks have not covered many real-life and challenging scenarios. The first Audio Deep synthesis Detection challenge (ADD) was motivated to fill in the gap. The ADD 2022 (http://addchallenge.cn/add2022) includes three tracks: low-quality fake audio detection (LF), partially fake audio detection (PF) and audio fake game (FG). The LF track focuses on dealing with bona fide and fully fake utterances with various real-world noises etc. The PF track aims to distinguish the partially fake audio from the real. The FG track is a rivalry game, which includes two tasks: an audio generation task and an audio fake detection task. In this paper, we describe the datasets, evaluation metrics, and protocols. We also report major findings that reflect the recent advances in audio deepfake detection tasks.

The ADD 2022 dataset is publicly available.

This data set is licensed with a CC BY-NC-ND 4.0 license.

If you use this dataset, please cite the following paper:



Jiangyan Yi, Ruibo Fu, Jianhua Tao, Shuai Nie, Haoxin Ma, Chenglong Wang, Tao Wang, Zhengkun Tian, Ye Bai, Cunhang Fan, Shan Liang, Shiming Wang, Shuai Zhang, Xinrui Yan, Le Xu, Zhengqi Wen, Haizhou Li:ADD 2022: the first Audio Deep Synthesis Detection Challenge. ICASSP 2022: 9216-9220
DOI: 10.15468/dl.kkxy7w
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Teuthis gibbosus De Vis, 1884",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007766-240626123714530/datasets/export for details.
DOI: 10.15468/dl.whh7ns
title: Occurrence Download

description: A dataset containing 832 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is one of (Miconia cuspidata Naudin)"
 ]
}

 The dataset includes 832 records from 87 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007767-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.q2td2d
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Teuthis kopsii (Bleeker, 1851)",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007768-240626123714530/datasets/export for details.
DOI: 10.15468/dl.u5ah46
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Teuthis nebulosa (Quoy & Gaimard, 1825)",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007769-240626123714530/datasets/export for details.
DOI: 10.5281/zenodo.12188126
title: The train and dev sets of ADD 2022

description: Audio deepfake detection is an emerging topic, which was included in the ASVspoof 2021. However, the recent shared tasks have not covered many real-life and challenging scenarios. The first Audio Deep synthesis Detection challenge (ADD) was motivated to fill in the gap. The ADD 2022 (http://addchallenge.cn/add2022) includes three tracks: low-quality fake audio detection (LF), partially fake audio detection (PF) and audio fake game (FG). The LF track focuses on dealing with bona fide and fully fake utterances with various real-world noises etc. The PF track aims to distinguish the partially fake audio from the real. The FG track is a rivalry game, which includes two tasks: an audio generation task and an audio fake detection task. In this paper, we describe the datasets, evaluation metrics, and protocols. We also report major findings that reflect the recent advances in audio deepfake detection tasks.

The ADD 2022 dataset is publicly available.

This data set is licensed with a CC BY-NC-ND 4.0 license.

If you use this dataset, please cite the following paper:



Jiangyan Yi, Ruibo Fu, Jianhua Tao, Shuai Nie, Haoxin Ma, Chenglong Wang, Tao Wang, Zhengkun Tian, Ye Bai, Cunhang Fan, Shan Liang, Shiming Wang, Shuai Zhang, Xinrui Yan, Le Xu, Zhengqi Wen, Haizhou Li:ADD 2022: the first Audio Deep Synthesis Detection Challenge. ICASSP 2022: 9216-9220
DOI: 10.5281/zenodo.12188127
title: The train and dev sets of ADD 2022

description: Audio deepfake detection is an emerging topic, which was included in the ASVspoof 2021. However, the recent shared tasks have not covered many real-life and challenging scenarios. The first Audio Deep synthesis Detection challenge (ADD) was motivated to fill in the gap. The ADD 2022 (http://addchallenge.cn/add2022) includes three tracks: low-quality fake audio detection (LF), partially fake audio detection (PF) and audio fake game (FG). The LF track focuses on dealing with bona fide and fully fake utterances with various real-world noises etc. The PF track aims to distinguish the partially fake audio from the real. The FG track is a rivalry game, which includes two tasks: an audio generation task and an audio fake detection task. In this paper, we describe the datasets, evaluation metrics, and protocols. We also report major findings that reflect the recent advances in audio deepfake detection tasks.

The ADD 2022 dataset is publicly available.

This data set is licensed with a CC BY-NC-ND 4.0 license.

If you use this dataset, please cite the following paper:



Jiangyan Yi, Ruibo Fu, Jianhua Tao, Shuai Nie, Haoxin Ma, Chenglong Wang, Tao Wang, Zhengkun Tian, Ye Bai, Cunhang Fan, Shan Liang, Shiming Wang, Shuai Zhang, Xinrui Yan, Le Xu, Zhengqi Wen, Haizhou Li:ADD 2022: the first Audio Deep Synthesis Detection Challenge. ICASSP 2022: 9216-9220
DOI: 10.6084/m9.figshare.26122492
title: Annotation_48Lotus_merge_transcript.fasta.transdecoder.genome.gff3

description: Gene annotations for known genes and novel genes in the <i>Nelumbo</i> population including <i>N. nucifera</i>, <i>N. lutea</i>, their interspecific F1 hybrids, and backcrossing F2 generations.
DOI: 10.6084/m9.figshare.26122492.v1
title: Annotation_48Lotus_merge_transcript.fasta.transdecoder.genome.gff3

description: Gene annotations for known genes and novel genes in the <i>Nelumbo</i> population including <i>N. nucifera</i>, <i>N. lutea</i>, their interspecific F1 hybrids, and backcrossing F2 generations.
DOI: 10.6084/m9.figshare.26123953
title: Gene expression from 48 samples from <i>N. nucifera</i>, <i>N. lutea</i>, their interspecific F1 hybrids, and backcrossing F2 generations.

description: The expression of 42,820 genes from the <i>Nelumbo</i> population including <i>N. nucifera</i>, <i>N. lutea</i>, their interspecific F1 hybrids, and backcrossing F2 generations.
DOI: 10.15468/dl.348z85
title: Occurrence Download

description: A dataset containing 669 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is one of (Miconia dichrophylla J.F.Macbr., Miconia elaeagnoides (Sw.) Griseb.)"
 ]
}

 The dataset includes 669 records from 62 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007770-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.384tzk
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Teuthis tumifrons (Valenciennes, 1835)",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007771-240626123714530/datasets/export for details.
DOI: 10.5281/zenodo.12188082
title: The adaption sets of ADD 2022

description: Audio deepfake detection is an emerging topic, which was included in the ASVspoof 2021. However, the recent shared tasks have not covered many real-life and challenging scenarios. The first Audio Deep synthesis Detection challenge (ADD) was motivated to fill in the gap. The ADD 2022 (http://addchallenge.cn/add2022) includes three tracks: low-quality fake audio detection (LF), partially fake audio detection (PF) and audio fake game (FG). The LF track focuses on dealing with bona fide and fully fake utterances with various real-world noises etc. The PF track aims to distinguish the partially fake audio from the real. The FG track is a rivalry game, which includes two tasks: an audio generation task and an audio fake detection task. In this paper, we describe the datasets, evaluation metrics, and protocols. We also report major findings that reflect the recent advances in audio deepfake detection tasks.

The ADD 2022 dataset is publicly available.

This data set is licensed with a CC BY-NC-ND 4.0 license.

If you use this dataset, please cite the following paper:



Jiangyan Yi, Ruibo Fu, Jianhua Tao, Shuai Nie, Haoxin Ma, Chenglong Wang, Tao Wang, Zhengkun Tian, Ye Bai, Cunhang Fan, Shan Liang, Shiming Wang, Shuai Zhang, Xinrui Yan, Le Xu, Zhengqi Wen, Haizhou Li:ADD 2022: the first Audio Deep Synthesis Detection Challenge. ICASSP 2022: 9216-9220
DOI: 10.5281/zenodo.12188083
title: The adaption sets of ADD 2022

description: Audio deepfake detection is an emerging topic, which was included in the ASVspoof 2021. However, the recent shared tasks have not covered many real-life and challenging scenarios. The first Audio Deep synthesis Detection challenge (ADD) was motivated to fill in the gap. The ADD 2022 (http://addchallenge.cn/add2022) includes three tracks: low-quality fake audio detection (LF), partially fake audio detection (PF) and audio fake game (FG). The LF track focuses on dealing with bona fide and fully fake utterances with various real-world noises etc. The PF track aims to distinguish the partially fake audio from the real. The FG track is a rivalry game, which includes two tasks: an audio generation task and an audio fake detection task. In this paper, we describe the datasets, evaluation metrics, and protocols. We also report major findings that reflect the recent advances in audio deepfake detection tasks.

The ADD 2022 dataset is publicly available.

This data set is licensed with a CC BY-NC-ND 4.0 license.

If you use this dataset, please cite the following paper:



Jiangyan Yi, Ruibo Fu, Jianhua Tao, Shuai Nie, Haoxin Ma, Chenglong Wang, Tao Wang, Zhengkun Tian, Ye Bai, Cunhang Fan, Shan Liang, Shiming Wang, Shuai Zhang, Xinrui Yan, Le Xu, Zhengqi Wen, Haizhou Li:ADD 2022: the first Audio Deep Synthesis Detection Challenge. ICASSP 2022: 9216-9220
DOI: 10.15468/dl.zvjbp4
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Teuthys nebulosa (Quoy & Gaimard, 1825)",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007773-240626123714530/datasets/export for details.
DOI: 10.5281/zenodo.12188055
title: The track 3.2 round 2 evaluation dataset of ADD 2022

description: Audio deepfake detection is an emerging topic, which was included in the ASVspoof 2021. However, the recent shared tasks have not covered many real-life and challenging scenarios. The first Audio Deep synthesis Detection challenge (ADD) was motivated to fill in the gap. The ADD 2022 (http://addchallenge.cn/add2022) includes three tracks: low-quality fake audio detection (LF), partially fake audio detection (PF) and audio fake game (FG). The LF track focuses on dealing with bona fide and fully fake utterances with various real-world noises etc. The PF track aims to distinguish the partially fake audio from the real. The FG track is a rivalry game, which includes two tasks: an audio generation task and an audio fake detection task. In this paper, we describe the datasets, evaluation metrics, and protocols. We also report major findings that reflect the recent advances in audio deepfake detection tasks.

The ADD 2022 dataset is publicly available.

This data set is licensed with a CC BY-NC-ND 4.0 license.

If you use this dataset, please cite the following paper:



Jiangyan Yi, Ruibo Fu, Jianhua Tao, Shuai Nie, Haoxin Ma, Chenglong Wang, Tao Wang, Zhengkun Tian, Ye Bai, Cunhang Fan, Shan Liang, Shiming Wang, Shuai Zhang, Xinrui Yan, Le Xu, Zhengqi Wen, Haizhou Li:ADD 2022: the first Audio Deep Synthesis Detection Challenge. ICASSP 2022: 9216-9220
DOI: 10.5281/zenodo.12179357
title: The track 3.2 round 2 evaluation dataset of ADD 2022

description: Audio deepfake detection is an emerging topic, which was included in the ASVspoof 2021. However, the recent shared tasks have not covered many real-life and challenging scenarios. The first Audio Deep synthesis Detection challenge (ADD) was motivated to fill in the gap. The ADD 2022 (http://addchallenge.cn/add2022) includes three tracks: low-quality fake audio detection (LF), partially fake audio detection (PF) and audio fake game (FG). The LF track focuses on dealing with bona fide and fully fake utterances with various real-world noises etc. The PF track aims to distinguish the partially fake audio from the real. The FG track is a rivalry game, which includes two tasks: an audio generation task and an audio fake detection task. In this paper, we describe the datasets, evaluation metrics, and protocols. We also report major findings that reflect the recent advances in audio deepfake detection tasks.

The ADD 2022 dataset is publicly available.

This data set is licensed with a CC BY-NC-ND 4.0 license.

If you use this dataset, please cite the following paper:



Jiangyan Yi, Ruibo Fu, Jianhua Tao, Shuai Nie, Haoxin Ma, Chenglong Wang, Tao Wang, Zhengkun Tian, Ye Bai, Cunhang Fan, Shan Liang, Shiming Wang, Shuai Zhang, Xinrui Yan, Le Xu, Zhengqi Wen, Haizhou Li:ADD 2022: the first Audio Deep Synthesis Detection Challenge. ICASSP 2022: 9216-9220
DOI: 10.5281/zenodo.11158368
title: The track 3.2 round 1 evaluation dataset of ADD 2022

description: Audio deepfake detection is an emerging topic, which was included in the ASVspoof 2021. However, the recent shared tasks have not covered many real-life and challenging scenarios. The first Audio Deep synthesis Detection challenge (ADD) was motivated to fill in the gap. The ADD 2022 (http://addchallenge.cn/add2022) includes three tracks: low-quality fake audio detection (LF), partially fake audio detection (PF) and audio fake game (FG). The LF track focuses on dealing with bona fide and fully fake utterances with various real-world noises etc. The PF track aims to distinguish the partially fake audio from the real. The FG track is a rivalry game, which includes two tasks: an audio generation task and an audio fake detection task. In this paper, we describe the datasets, evaluation metrics, and protocols. We also report major findings that reflect the recent advances in audio deepfake detection tasks.

The ADD 2022 dataset is publicly available.

This data set is licensed with a CC BY-NC-ND 4.0 license.

If you use this dataset, please cite the following paper:



Jiangyan Yi, Ruibo Fu, Jianhua Tao, Shuai Nie, Haoxin Ma, Chenglong Wang, Tao Wang, Zhengkun Tian, Ye Bai, Cunhang Fan, Shan Liang, Shiming Wang, Shuai Zhang, Xinrui Yan, Le Xu, Zhengqi Wen, Haizhou Li:ADD 2022: the first Audio Deep Synthesis Detection Challenge. ICASSP 2022: 9216-9220
DOI: 10.5281/zenodo.12188035
title: The track 3.2 round 1 evaluation dataset of ADD 2022

description: Audio deepfake detection is an emerging topic, which was included in the ASVspoof 2021. However, the recent shared tasks have not covered many real-life and challenging scenarios. The first Audio Deep synthesis Detection challenge (ADD) was motivated to fill in the gap. The ADD 2022 (http://addchallenge.cn/add2022) includes three tracks: low-quality fake audio detection (LF), partially fake audio detection (PF) and audio fake game (FG). The LF track focuses on dealing with bona fide and fully fake utterances with various real-world noises etc. The PF track aims to distinguish the partially fake audio from the real. The FG track is a rivalry game, which includes two tasks: an audio generation task and an audio fake detection task. In this paper, we describe the datasets, evaluation metrics, and protocols. We also report major findings that reflect the recent advances in audio deepfake detection tasks.

The ADD 2022 dataset is publicly available.

This data set is licensed with a CC BY-NC-ND 4.0 license.

If you use this dataset, please cite the following paper:



Jiangyan Yi, Ruibo Fu, Jianhua Tao, Shuai Nie, Haoxin Ma, Chenglong Wang, Tao Wang, Zhengkun Tian, Ye Bai, Cunhang Fan, Shan Liang, Shiming Wang, Shuai Zhang, Xinrui Yan, Le Xu, Zhengqi Wen, Haizhou Li:ADD 2022: the first Audio Deep Synthesis Detection Challenge. ICASSP 2022: 9216-9220
DOI: 10.15468/dl.7tanhf
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Theutis margaritifera (Valenciennes, 1835)",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007776-240626123714530/datasets/export for details.
DOI: 10.15468/dl.ab7nz9
title: Occurrence Download

description: A dataset containing 1608 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is one of (Miconia elegans Cogn., Miconia elegans var. latialata Cogn., Miconia elegans var. pauciflora Cogn.)"
 ]
}

 The dataset includes 1608 records from 110 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007775-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.10996590
title: The track 2 evaluation dataset of ADD 2022

description: Audio deepfake detection is an emerging topic, which was included in the ASVspoof 2021. However, the recent shared tasks have not covered many real-life and challenging scenarios. The first Audio Deep synthesis Detection challenge (ADD) was motivated to fill in the gap. The ADD 2022 (http://addchallenge.cn/add2022) includes three tracks: low-quality fake audio detection (LF), partially fake audio detection (PF) and audio fake game (FG). The LF track focuses on dealing with bona fide and fully fake utterances with various real-world noises etc. The PF track aims to distinguish the partially fake audio from the real. The FG track is a rivalry game, which includes two tasks: an audio generation task and an audio fake detection task. In this paper, we describe the datasets, evaluation metrics, and protocols. We also report major findings that reflect the recent advances in audio deepfake detection tasks.

The ADD 2022 dataset is publicly available.

This data set is licensed with a CC BY-NC-ND 4.0 license.

If you use this dataset, please cite the following paper:



Jiangyan Yi, Ruibo Fu, Jianhua Tao, Shuai Nie, Haoxin Ma, Chenglong Wang, Tao Wang, Zhengkun Tian, Ye Bai, Cunhang Fan, Shan Liang, Shiming Wang, Shuai Zhang, Xinrui Yan, Le Xu, Zhengqi Wen, Haizhou Li:ADD 2022: the first Audio Deep Synthesis Detection Challenge. ICASSP 2022: 9216-9220
DOI: 10.5281/zenodo.12187997
title: The track 2 evaluation dataset of ADD 2022

description: Audio deepfake detection is an emerging topic, which was included in the ASVspoof 2021. However, the recent shared tasks have not covered many real-life and challenging scenarios. The first Audio Deep synthesis Detection challenge (ADD) was motivated to fill in the gap. The ADD 2022 (http://addchallenge.cn/add2022) includes three tracks: low-quality fake audio detection (LF), partially fake audio detection (PF) and audio fake game (FG). The LF track focuses on dealing with bona fide and fully fake utterances with various real-world noises etc. The PF track aims to distinguish the partially fake audio from the real. The FG track is a rivalry game, which includes two tasks: an audio generation task and an audio fake detection task. In this paper, we describe the datasets, evaluation metrics, and protocols. We also report major findings that reflect the recent advances in audio deepfake detection tasks.

The ADD 2022 dataset is publicly available.

This data set is licensed with a CC BY-NC-ND 4.0 license.

If you use this dataset, please cite the following paper:



Jiangyan Yi, Ruibo Fu, Jianhua Tao, Shuai Nie, Haoxin Ma, Chenglong Wang, Tao Wang, Zhengkun Tian, Ye Bai, Cunhang Fan, Shan Liang, Shiming Wang, Shuai Zhang, Xinrui Yan, Le Xu, Zhengqi Wen, Haizhou Li:ADD 2022: the first Audio Deep Synthesis Detection Challenge. ICASSP 2022: 9216-9220
DOI: 10.15468/dl.zmpzty
title: Occurrence Download

description: A dataset containing 266 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is one of (Henriettea patrisiana DC.)"
 ]
}

 The dataset includes 266 records from 46 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007777-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.26143024.v1
title: Untitled Item

description: For PLOS-ONE's reader.
DOI: 10.15468/dl.uc6225
title: Occurrence Download

description: A dataset containing 12679 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Chionoecetes bairdi Rathbun, 1924"
 ]
}

 The dataset includes 12679 records from 34 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007778-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.s4eny5
title: Occurrence Download

description: A dataset containing 2819 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is one of (Pleroma stenocarpum (DC.) Triana, Tibouchina caldensis Cogn., Tibouchina stenocarpa (DC.) Cogn., Tibouchina stenocarpa var. boliviensis Cogn.)"
 ]
}

 The dataset includes 2819 records from 129 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007779-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zxcswt
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Justicia holgueri Wassh."
 ]
}

 The dataset includes 8 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0168937-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.97xho5
title: Occurrence Download

description: A dataset containing 1166 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Milicia Sim",
 "HasCoordinate is true",
 {
 "or" : [
 "BasisOfRecord is Human Observation",
 "BasisOfRecord is Machine Observation",
 "BasisOfRecord is Observation",
 "BasisOfRecord is Literature Occurrence",
 "BasisOfRecord is Unknown evidence"
 ]
 }
 ]
}

 The dataset includes 1166 records from 30 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032818-160910150852091/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.