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Paper Citation Record · LEDGER

MetaPerch: Learning from metadata for bioacoustics foundation models

As of 10 August 2026, this Paper Citation Record lists 88 of 88 outbound references and 0 inbound Pith citation observations for arXiv:2607.14072.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2607.14072 v1

Coverage vector

measured 88 of 88 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-02T02:54:07.597200Z

measured 88 of 88 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

88 of 88 outbound references displayed

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  • verified fuzzy0
  • unresolved87
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9baec61d-9edd-4c49-b9ab-93bae7185e51 · outbound

This paper cites European conference on computer vision , pages=.

MetaPerch: Learning from metadata for bioacoustics foundation models European conference on computer vision , pages=

Reference 1

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Observation d0cef644-7d9d-435f-9f82-80ca14f5d9fd · outbound

This paper cites 2021 , publisher=.

MetaPerch: Learning from metadata for bioacoustics foundation models 2021 , publisher=

Reference 2

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Observation 74496333-bec6-49fd-91c7-015c883a508c · outbound

This paper cites Scientific Reports , volume=.

MetaPerch: Learning from metadata for bioacoustics foundation models Scientific Reports , volume=

Reference 3

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Observation 0df214c6-aa91-4e29-a1ec-c3652dcf761a · outbound

This paper cites BIRB: A Generalization Benchmark for Information Retrieval in Bioacoustics.

MetaPerch: Learning from metadata for bioacoustics foundation models BIRB: A Generalization Benchmark for Information Retrieval in Bioacoustics

Reference 4

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Observation 3f87bb49-9767-4d68-925f-a68560265666 · outbound

This paper cites AVEX: What Matters for Animal Vocalization Encoding.

MetaPerch: Learning from metadata for bioacoustics foundation models AVEX: What Matters for Animal Vocalization Encoding

Reference 5

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Observation 95dedeaa-bafd-434a-9ad4-bb2afa8d4c26 · outbound

This paper cites arXiv preprint arXiv:2508.01277 , year=.

MetaPerch: Learning from metadata for bioacoustics foundation models arXiv preprint arXiv:2508.01277 , year=

Reference 6

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Observation 560f3f9d-a6e3-4e45-8995-01c90f94e4de · outbound

This paper cites arXiv preprint arXiv:2508.04665 , year=.

MetaPerch: Learning from metadata for bioacoustics foundation models arXiv preprint arXiv:2508.04665 , year=

Reference 7

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Observation 516a6c35-6ad7-4baa-8249-b3fc534f2fa4 · outbound

This paper cites EfficientNet : Rethinking model scaling for convolutional Neural Networks.

MetaPerch: Learning from metadata for bioacoustics foundation models EfficientNet : Rethinking model scaling for convolutional Neural Networks

Reference 8

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Observation 77d34eea-018d-460f-a7f4-7fea68b7dcb8 · outbound

This paper cites an unresolved cited work.

MetaPerch: Learning from metadata for bioacoustics foundation models Unresolved cited work

Reference 9

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Observation c0679bb7-a58d-4dcf-b1b4-bc25e3c0b6e8 · outbound

This paper cites Advances in neural information processing systems , volume=.

MetaPerch: Learning from metadata for bioacoustics foundation models Advances in neural information processing systems , volume=

Reference 10

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Observation 3ce2c489-6b9a-43dd-b654-330793d2e076 · outbound

This paper cites Forty-third International Conference on Machine Learning , year=.

MetaPerch: Learning from metadata for bioacoustics foundation models Forty-third International Conference on Machine Learning , year=

Reference 11

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Observation 56dc295b-9699-49a0-b09d-e7f36f9bb3a4 · outbound

This paper cites International Conference on Learning Representations , year=.

MetaPerch: Learning from metadata for bioacoustics foundation models International Conference on Learning Representations , year=

Reference 12

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Observation 593ac2d5-b146-42c6-860a-31f43aa1a521 · outbound

This paper cites 2019 16th International Conference on Machine Vision Applications (MVA) , pages=.

MetaPerch: Learning from metadata for bioacoustics foundation models 2019 16th International Conference on Machine Vision Applications (MVA) , pages=

Reference 13

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Observation 89c87b6e-603a-4e7c-bcc8-7cd6a2942f01 · outbound

This paper cites Journal of machine learning research , volume=.

MetaPerch: Learning from metadata for bioacoustics foundation models Journal of machine learning research , volume=

Reference 14

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Observation 47d78e2d-1936-4edd-97e6-bc537fe06708 · outbound

This paper cites iNaturalist Research-grade Observations.

MetaPerch: Learning from metadata for bioacoustics foundation models iNaturalist Research-grade Observations

Reference 15

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Observation 8759e0e9-7edc-49dc-90d3-c19cc8a120d1 · outbound

This paper cites Xeno-Canto.

MetaPerch: Learning from metadata for bioacoustics foundation models Xeno-Canto

Reference 16

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Observation 327d4ee1-f7e3-4d7a-9aab-dd466d9bde10 · outbound

This paper cites The archive of animal sounds at the Humboldt-university of Berlin.

MetaPerch: Learning from metadata for bioacoustics foundation models The archive of animal sounds at the Humboldt-university of Berlin

Reference 17

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Observation fa93c95f-d993-413b-98f5-239cbf60a000 · outbound

This paper cites FSD50K : An open dataset of human-labeled sound events.

MetaPerch: Learning from metadata for bioacoustics foundation models FSD50K : An open dataset of human-labeled sound events

Reference 18

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Observation 85e63b44-9292-4a87-bf57-c2c6c04ea2d3 · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

MetaPerch: Learning from metadata for bioacoustics foundation models Advances in Neural Information Processing Systems , volume=

Reference 19

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Observation 24f54f9e-8b08-4021-9872-36917a1b9033 · outbound

This paper cites Symes and Viviana Ruiz-Gutiérrez and Ingrid Molina-Mora and Fernando Cediel and Luis Sandoval and Holger Klinck , title =.

MetaPerch: Learning from metadata for bioacoustics foundation models Symes and Viviana Ruiz-Gutiérrez and Ingrid Molina-Mora and Fernando Cediel and Luis Sandoval and Holger Klinck , title =

Reference 20

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Observation 9e1c4d86-884b-46b0-b2e5-073d4e72f8f5 · outbound

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MetaPerch: Learning from metadata for bioacoustics foundation models Zenodo , version = 1, doi =

Reference 21

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Observation 9b0996de-553e-4dea-b8ed-7c6839c4e936 · outbound

This paper cites Alexander Hopping and Stefan Kahl and Holger Klinck , title =.

MetaPerch: Learning from metadata for bioacoustics foundation models Alexander Hopping and Stefan Kahl and Holger Klinck , title =

Reference 22

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This paper cites Wood and Philip Chaon and M.

MetaPerch: Learning from metadata for bioacoustics foundation models Wood and Philip Chaon and M

Reference 23

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MetaPerch: Learning from metadata for bioacoustics foundation models Zenodo , version = 1, doi =

Reference 24

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MetaPerch: Learning from metadata for bioacoustics foundation models Zenodo , version = 1, doi =

Reference 25

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Observation 307e244c-19db-4589-8504-aebdbc032e42 · outbound

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MetaPerch: Learning from metadata for bioacoustics foundation models PeerJ Computer Science , volume=

Reference 26

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This paper cites Marín-Gómez and Irene Mendoza and Miguel A.

MetaPerch: Learning from metadata for bioacoustics foundation models Marín-Gómez and Irene Mendoza and Miguel A

Reference 27

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MetaPerch: Learning from metadata for bioacoustics foundation models Weldy and Tom Denton and Abram B

Reference 28

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MetaPerch: Learning from metadata for bioacoustics foundation models Scientific Data , volume=

Reference 29

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Observation 45144e39-71a4-41cc-83fd-d65575e5c969 · outbound

This paper cites Pacific Islands Passive Acoustic Network ( PIPAN ) 10kHz Data.

MetaPerch: Learning from metadata for bioacoustics foundation models Pacific Islands Passive Acoustic Network ( PIPAN ) 10kHz Data

Reference 30

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MetaPerch: Learning from metadata for bioacoustics foundation models Philosophical Transactions B , volume=

Reference 31

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MetaPerch: Learning from metadata for bioacoustics foundation models Proceedings of Meetings on Acoustics , volume=

Reference 32

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MetaPerch: Learning from metadata for bioacoustics foundation models Scientific data , volume=

Reference 33

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MetaPerch: Learning from metadata for bioacoustics foundation models 2020 , howpublished =

Reference 34

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MetaPerch: Learning from metadata for bioacoustics foundation models Animal behaviour , volume=

Reference 35

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MetaPerch: Learning from metadata for bioacoustics foundation models Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2) , year=

Reference 36

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MetaPerch: Learning from metadata for bioacoustics foundation models , author=

Reference 37

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MetaPerch: Learning from metadata for bioacoustics foundation models , author=

Reference 38

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MetaPerch: Learning from metadata for bioacoustics foundation models Unresolved cited work

Reference 39

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This paper cites Ecological Informatics , volume=.

MetaPerch: Learning from metadata for bioacoustics foundation models Ecological Informatics , volume=

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Observation 80edf948-029e-48ee-8a05-1c413a7fd34b · outbound

This paper cites Remote Sensing in Ecology and Conservation , year=.

MetaPerch: Learning from metadata for bioacoustics foundation models Remote Sensing in Ecology and Conservation , year=

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Observation f2c6980c-4362-4f78-91f0-300c7b97307a · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

MetaPerch: Learning from metadata for bioacoustics foundation models Advances in Neural Information Processing Systems , volume=

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Observation adea2a90-70fe-4929-a631-dd3dd7f60f99 · outbound

This paper cites Proceedings of the AAAI Conference on Artificial Intelligence , volume=.

MetaPerch: Learning from metadata for bioacoustics foundation models Proceedings of the AAAI Conference on Artificial Intelligence , volume=

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Observation 6a7bfe35-7ae8-4d4f-8efd-503dccbb84f7 · outbound

This paper cites The Thirteenth International Conference on Learning Representations , year=.

MetaPerch: Learning from metadata for bioacoustics foundation models The Thirteenth International Conference on Learning Representations , year=

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This paper cites ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , pages=.

MetaPerch: Learning from metadata for bioacoustics foundation models ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , pages=

Reference 45

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Observation d531c869-091b-47d3-86f8-a5da336a7c73 · outbound

This paper cites NatureLM-audio: an Audio-Language Foundation Model for Bioacoustics.

MetaPerch: Learning from metadata for bioacoustics foundation models NatureLM-audio: an Audio-Language Foundation Model for Bioacoustics

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Observation 31c720e9-ae3c-42aa-abe0-cd8e6256ccf6 · outbound

This paper cites The Thirty-ninth Annual Conference on Neural Information Processing Systems , year=.

MetaPerch: Learning from metadata for bioacoustics foundation models The Thirty-ninth Annual Conference on Neural Information Processing Systems , year=

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Observation 3827c5f7-0fa9-437b-8764-972f6bc8d048 · outbound

This paper cites Can Masked Autoencoders Also Listen to Birds?.

MetaPerch: Learning from metadata for bioacoustics foundation models Can Masked Autoencoders Also Listen to Birds?

Reference 48

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Observation d474a162-ffd1-4536-9a92-195bddd599ba · outbound

This paper cites Masked Autoencoders with Limited Data: Does It Work? A Fine-Grained Bioacoustics Case Study.

MetaPerch: Learning from metadata for bioacoustics foundation models Masked Autoencoders with Limited Data: Does It Work? A Fine-Grained Bioacoustics Case Study

Reference 49

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Observation a7e35764-3817-4ab9-b1de-29a2217cfb12 · outbound

This paper cites The Thirty-Ninth Annual Conference on Neural Information Processing Systems workshop: AI for non-human animal communication , year=.

MetaPerch: Learning from metadata for bioacoustics foundation models The Thirty-Ninth Annual Conference on Neural Information Processing Systems workshop: AI for non-human animal communication , year=

Reference 50

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Observation e58a9990-7626-4dec-9117-1370c0e07d03 · outbound

This paper cites ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , pages=.

MetaPerch: Learning from metadata for bioacoustics foundation models ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , pages=

Reference 51

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Observation 75cb602b-4017-449e-ab0b-4a74ef1ac698 · outbound

This paper cites animal2vec and MeerKAT: A self-supervised transformer for rare-event raw audio input and a large-scale reference dataset for bioacoustics.

MetaPerch: Learning from metadata for bioacoustics foundation models animal2vec and MeerKAT: A self-supervised transformer for rare-event raw audio input and a large-scale reference dataset for bioacoustics

Reference 52

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Observation 3683bc3a-c7fe-4e48-9342-f96cb5cb42a3 · outbound

This paper cites an unresolved cited work.

MetaPerch: Learning from metadata for bioacoustics foundation models Unresolved cited work

Reference 53

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Observation ec1aaec1-02f4-49ac-80d1-5ec5330f9aa1 · outbound

This paper cites International conference on machine learning , pages=.

MetaPerch: Learning from metadata for bioacoustics foundation models International conference on machine learning , pages=

Reference 54

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Observation 70934763-8b74-438b-a112-62642bea0c7c · outbound

This paper cites ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , pages=.

MetaPerch: Learning from metadata for bioacoustics foundation models ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , pages=

Reference 55

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Observation cbbaf9ae-3877-440c-b677-9e4801261e87 · outbound

This paper cites Ecology letters , volume=.

MetaPerch: Learning from metadata for bioacoustics foundation models Ecology letters , volume=

Reference 56

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source=arxiv_source observed=2026-08-02T02:54:07.482070Z digest=sha256:065afdb7115d5b0582b861858ff41974feda27646540fbd8e8d5cf265d74d632

Observation 8c0d271e-b191-4272-ba5a-ce54b4ddf540 · outbound

This paper cites Global ecology and biogeography , volume=.

MetaPerch: Learning from metadata for bioacoustics foundation models Global ecology and biogeography , volume=

Reference 57

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Observation df36c4de-c43b-47bc-9bd5-02bcdeb2ac64 · outbound

This paper cites ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , pages=.

MetaPerch: Learning from metadata for bioacoustics foundation models ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , pages=

Reference 58

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source=arxiv_source observed=2026-08-02T02:54:07.489222Z digest=sha256:ff7d92584f0b9b987f6ce4dc146d0798c1efb4c0c2c99ef9b3d002eb480066e9

Observation 26f59d2b-1942-45b9-8b9e-71ed950aac77 · outbound

This paper cites Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=.

MetaPerch: Learning from metadata for bioacoustics foundation models Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=

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Observation 4c20323b-62e8-47af-9b2d-1aea277aa48c · outbound

This paper cites Ecological Informatics , volume=.

MetaPerch: Learning from metadata for bioacoustics foundation models Ecological Informatics , volume=

Reference 60

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Observation 106d78db-996b-455c-a069-153d58824e9f · outbound

This paper cites Expert Systems with Applications , volume=.

MetaPerch: Learning from metadata for bioacoustics foundation models Expert Systems with Applications , volume=

Reference 61

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Observation 0a82e3ec-c3cc-4f1c-ac22-78b728bbb9b6 · outbound

This paper cites The Thirty-Ninth Annual Conference on Neural Information Processing Systems workshop: AI for non-human animal communication , year=.

MetaPerch: Learning from metadata for bioacoustics foundation models The Thirty-Ninth Annual Conference on Neural Information Processing Systems workshop: AI for non-human animal communication , year=

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Observation ce59c058-285a-4344-a831-30d99058d925 · outbound

This paper cites Parsing Birdsong with Deep Audio Embeddings.

MetaPerch: Learning from metadata for bioacoustics foundation models Parsing Birdsong with Deep Audio Embeddings

Reference 63

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Observation 88b91e9c-c913-45d8-bec4-a72474520484 · outbound

This paper cites Interspeech 2024 , pages=.

MetaPerch: Learning from metadata for bioacoustics foundation models Interspeech 2024 , pages=

Reference 64

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Observation 723de795-e98c-49cf-a9f3-c7494efdf76d · outbound

This paper cites BioRxiv , pages=.

MetaPerch: Learning from metadata for bioacoustics foundation models BioRxiv , pages=

Reference 65

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Observation f34175c6-703e-4bd8-8ab3-19970766cf21 · outbound

This paper cites ICASSP 2025-2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , pages=.

MetaPerch: Learning from metadata for bioacoustics foundation models ICASSP 2025-2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , pages=

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Observation cd96b735-1e41-4655-9af9-8bd6d1dd81c0 · outbound

This paper cites ICASSP 2025-2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , pages=.

MetaPerch: Learning from metadata for bioacoustics foundation models ICASSP 2025-2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , pages=

Reference 67

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source=arxiv_source observed=2026-08-02T02:54:07.522161Z digest=sha256:b8e5333a634328d52225133b102d658bbaf90a7152110464c59c19f45683e548

Observation 2ea8dc4d-2a9d-4232-997c-64fe21d0ebfd · outbound

This paper cites Methods in Ecology and Evolution , volume=.

MetaPerch: Learning from metadata for bioacoustics foundation models Methods in Ecology and Evolution , volume=

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Observation 579974ef-f5ca-435e-a2f2-540a9a53b6f8 · outbound

This paper cites arXiv preprint arXiv:2409.08589 , year=.

MetaPerch: Learning from metadata for bioacoustics foundation models arXiv preprint arXiv:2409.08589 , year=

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Observation 1fa56011-7d1d-429f-9160-951168c1bd60 · outbound

This paper cites Ecological Informatics , volume=.

MetaPerch: Learning from metadata for bioacoustics foundation models Ecological Informatics , volume=

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Observation 4a68e7e7-d5ac-4236-a14a-57fc2948f387 · outbound

This paper cites Proceedings of the 2024 International Conference on Information Technology for Social Good , pages=.

MetaPerch: Learning from metadata for bioacoustics foundation models Proceedings of the 2024 International Conference on Information Technology for Social Good , pages=

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Observation 27589cd9-6b69-446f-ab22-a1ad38ba6395 · outbound

This paper cites Proceedings of the IEEE/CVF winter conference on applications of computer vision , pages=.

MetaPerch: Learning from metadata for bioacoustics foundation models Proceedings of the IEEE/CVF winter conference on applications of computer vision , pages=

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Observation 355b556b-95b6-43f0-be4e-b67878d72736 · outbound

This paper cites Proceedings of the IEEE conference on computer vision and pattern recognition , pages=.

MetaPerch: Learning from metadata for bioacoustics foundation models Proceedings of the IEEE conference on computer vision and pattern recognition , pages=

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Observation 2eab566c-6c7d-4cf5-8a0e-2885eebd8157 · outbound

This paper cites Proceedings of the International Conference on Learning Representations (ICLR) , year=.

MetaPerch: Learning from metadata for bioacoustics foundation models Proceedings of the International Conference on Learning Representations (ICLR) , year=

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Observation 5372ad63-3353-4c8b-8ed9-2edfb37f26a3 · outbound

This paper cites Advances in neural information processing systems , volume=.

MetaPerch: Learning from metadata for bioacoustics foundation models Advances in neural information processing systems , volume=

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Observation 3bc22cbd-899d-4917-a9f4-23229233cce2 · outbound

This paper cites Deep Multimodal Learning with Missing Modality: A Survey.

MetaPerch: Learning from metadata for bioacoustics foundation models Deep Multimodal Learning with Missing Modality: A Survey

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Observation b21c856c-524b-43b1-9159-166bf2650b5a · outbound

This paper cites ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , pages=.

MetaPerch: Learning from metadata for bioacoustics foundation models ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , pages=

Reference 77

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Observation 1f44d703-014d-4ba6-9dad-c528fbe67b21 · outbound

This paper cites Advances in neural information processing systems , volume=.

MetaPerch: Learning from metadata for bioacoustics foundation models Advances in neural information processing systems , volume=

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This paper cites Ecological Informatics , volume=.

MetaPerch: Learning from metadata for bioacoustics foundation models Ecological Informatics , volume=

Reference 79

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This paper cites 2025 , author =.

MetaPerch: Learning from metadata for bioacoustics foundation models 2025 , author =

Reference 80

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MetaPerch: Learning from metadata for bioacoustics foundation models Coding strategies in vertebrate acoustic communication , pages=

Reference 81

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MetaPerch: Learning from metadata for bioacoustics foundation models Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining , pages=

Reference 82

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MetaPerch: Learning from metadata for bioacoustics foundation models PeerJ , volume=

Reference 83

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This paper cites Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=.

MetaPerch: Learning from metadata for bioacoustics foundation models Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=

Reference 84

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This paper cites International Conference on Learning Representations , year=.

MetaPerch: Learning from metadata for bioacoustics foundation models International Conference on Learning Representations , year=

Reference 85

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Observation d8ef5192-7c9d-4ff0-82ab-8ee770ef4307 · outbound

This paper cites Proceedings of the 32nd ACM International Conference on Multimedia , pages=.

MetaPerch: Learning from metadata for bioacoustics foundation models Proceedings of the 32nd ACM International Conference on Multimedia , pages=

Reference 86

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Observation 6f5489e2-8279-404c-a554-26556aa39936 · outbound

This paper cites AlphaEarth Foundations: An embedding field model for accurate and efficient global mapping from sparse label data.

MetaPerch: Learning from metadata for bioacoustics foundation models AlphaEarth Foundations: An embedding field model for accurate and efficient global mapping from sparse label data

Reference 87

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Observation ce43bdf8-e7cb-4078-814d-eb912a1ad7c9 · outbound

This paper cites Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=.

MetaPerch: Learning from metadata for bioacoustics foundation models Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=

Reference 88

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Pith citing papers

No inbound Pith citation observations are available.