Pith Number

pith:TOTP2MAW

pith:2026:TOTP2MAW3DMHQUEUZUTB5CQIVL

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

Grant Van Horn, Mustafa Chasmai, Subhransu Maji, Wuao Liu

For fine-grained bioacoustic classification with limited labels, pretraining on large general audio datasets beats additional domain-specific masked autoencoder training.

arxiv:2605.14031 v1 · 2026-05-13 · cs.SD · cs.CV · cs.LG

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\pithnumber{TOTP2MAW3DMHQUEUZUTB5CQIVL}

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2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

In moderate-sized fine-grained bioacoustic settings, pretraining scale dominates objective design.

C2weakest assumption

That performance differences observed on iNatSounds are driven primarily by pretraining data scale rather than uncontrolled factors such as exact model capacity, optimizer choices, or dataset-specific biases in the weakly labeled recordings.

C3one line summary

In moderate-sized fine-grained bioacoustics, pretraining scale of masked autoencoders on diverse general audio dominates over domain-specific objectives or data curation for transfer performance.

References

58 extracted · 58 resolved · 2 Pith anchors

[1] Mae-ast: Masked autoencoding audio spectrogram transformer 2022

[2] Entropy-based analysis of influential factors for underwater acoustic target recognition in passive sonar data.Ocean Engineering, 342: 122908, 2025 2025

[3] wav2vec 2.0: A framework for self-supervised learning of speech representations.Advances in neural infor- mation processing systems, 33:12449–12460, 2020 2020

[4] Global biodiversity: indicators of recent declines 2010

[5] The inaturalist sounds dataset.Advances in Neural Information Processing Systems, 37:132524–132544,

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-17T23:39:12.838495Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

9ba6fd3016d8d8785094cd261e8a08aaed18d0ccac79abba5122a5f2cdf77b49

Aliases

arxiv: 2605.14031 · arxiv_version: 2605.14031v1 · doi: 10.48550/arxiv.2605.14031 · pith_short_12: TOTP2MAW3DMH · pith_short_16: TOTP2MAW3DMHQUEU · pith_short_8: TOTP2MAW

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/TOTP2MAW3DMHQUEUZUTB5CQIVL \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 9ba6fd3016d8d8785094cd261e8a08aaed18d0ccac79abba5122a5f2cdf77b49

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "42dc3db315ac627d4cade94f7cc56df8742745e2d8f8a167717e51550d200d99",
    "cross_cats_sorted": [
      "cs.CV",
      "cs.LG"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.SD",
    "submitted_at": "2026-05-13T18:45:08Z",
    "title_canon_sha256": "186c2adbbec291fd3e7dd526ac77c2403107e69e48aaa22b405bd270520fa84b"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.14031",
    "kind": "arxiv",
    "version": 1
  }
}