Pith Number

pith:3PIWSOG2

pith:2026:3PIWSOG2JRJERNIOGLBQ7N52PT

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BioSEN: A Bio-acoustic Signal Enhancement Network for Animal Vocalizations

Hisako Nomura, Linh Thi Hoai Nguyen, Ngamta Thamwattana, Tianyu Song, Ton Viet Ta

BioSEN adapts speech enhancement methods into a lighter network that cleans animal vocalization recordings as well as or better than existing models.

arxiv:2605.12534 v2 · 2026-05-02 · cs.SD · cs.LG · q-bio.NC

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Record completeness

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2 Internet Archive

3 Author claim open · sign in to claim

4 Citations 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

Tests on three bioacoustic datasets show that BioSEN matches or exceeds state-of-the-art speech enhancement models while using far less computation.

C2weakest assumption

That adaptations of speech enhancement methods with the described modules will generalize across diverse animal species and recording conditions without requiring extensive species-specific retraining or validation.

C3one line summary

BioSEN enhances bioacoustic signals with specialized modules and matches or exceeds speech enhancement models on animal datasets while using less computation.

References

23 extracted · 23 resolved · 0 Pith anchors

[1] Kohlberg, A. B., Myers, C. R., Figueroa, L. L. (2024). Fro m buzzes to bytes: A sys- tematic review of automated bioacoustics models used to det ect, classify and monitor insects. J. Appl. Ecol. , 61( 2024

[2] Navine, A. K., Camp, R. J., Weldy, M. J., Denton, T., Hart, P. J. (2024). Counting the chorus: A bioacoustic indicator of population density. Ecological Indicators, 169, 112930 2024

[3] H., Stowell, D., Briefer, E 2024

[4] Sharma, S., Sato, K., Gautam, B. P. (2023). A methodologi cal literature review of acoustic wildlife monitoring using artiﬁcial intelligenc e tools and techniques. Sustain- ability, 15(9), 7128 2023

[5] Gajecki, T., Nogueira, W. (2025). Adversarial learning for end-to-end cochlear speech denoising using lightweight deep learning models. Proc. IEEE Int. Conf. Acoust., Speech, Signal Process. (ICASSP) 2025

Receipt and verification

First computed	2026-05-18T03:10:02.509064Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

dbd16938da4c5248b50e32c30fb7ba7cf0a2328cb77c631b05786bad10b378cd

Aliases

arxiv: 2605.12534 · arxiv_version: 2605.12534v2 · doi: 10.48550/arxiv.2605.12534 · pith_short_12: 3PIWSOG2JRJE · pith_short_16: 3PIWSOG2JRJERNIO · pith_short_8: 3PIWSOG2

Agent API

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/3PIWSOG2JRJERNIOGLBQ7N52PT \
  | 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: dbd16938da4c5248b50e32c30fb7ba7cf0a2328cb77c631b05786bad10b378cd

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "a062de3f79ff94f9e7387e2736e183ab9545b8696d63e8cee58db2426da18de0",
    "cross_cats_sorted": [
      "cs.LG",
      "q-bio.NC"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.SD",
    "submitted_at": "2026-05-02T00:19:24Z",
    "title_canon_sha256": "c1219de9e4d7a6b943fb886bb69443b6c71699b63cbf5f191f6c6e32dd8af7df"
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  "source": {
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    "kind": "arxiv",
    "version": 2
  }
}