Pith Number

pith:RX2VMUWN

pith:2026:RX2VMUWNDTK3BQLI3LYLOU5SKG

not attested not anchored not stored refs resolved

A Survey of Advancing Audio Super-Resolution and Bandwidth Extension from Discriminative to Generative Models

Andrew C. Singer, Diego A. Cuji, Ningyuan Yang, Pu Zhao, Ryan M. Corey, Xue Lin, Yize Li

Audio super-resolution is shifting from deterministic neural mappings that over-smooth high frequencies to generative models that sample plausible missing content.

arxiv:2605.16681 v1 · 2026-05-15 · eess.AS · eess.SP

Open paper page JSON Open Graph Bundle Merged state Verified badge What is a Pith Number?

Add to your LaTeX paper

\usepackage{pith}
\pithnumber{RX2VMUWNDTK3BQLI3LYLOU5SKG}

Prints a linked badge after your title and injects PDF metadata. Compiles on arXiv. Learn more · Embed verified badge

Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

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

By providing a structured taxonomy and unified perspective, this survey establishes a comprehensive foundation and offers a practical roadmap for advancing BWE/SR from deterministic point estimation toward distribution-aware generative modeling.

C2weakest assumption

The survey assumes that its selection of literature and proposed taxonomy accurately and comprehensively capture the key developments and trade-offs in the field without significant omissions or bias in coverage.

C3one line summary

A structured survey of audio bandwidth extension that organizes the transition from deterministic discriminative DNNs to generative approaches including GANs, diffusion models, and flow-based methods.

References

72 extracted · 72 resolved · 20 Pith anchors

[1] Hiﬁ++: a uniﬁed framework for neural vocoding, bandwidth extension and speech enhancement.arXiv preprint arXiv:2203.13086

[2] An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling · arXiv:1803.01271

[3] Hi-fi multi- speaker english tts dataset

[4] Frequency-domain enhanced extreme bandwidth extension network with iccrn for superior speech quality 2025

[5] CMGAN: Conformer- based metric gan for speech enhancement

Receipt and verification

First computed	2026-05-20T00:02:36.329936Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

8df55652cd1cd5b0c168daf0b753b2518854ecf2c7a1de76f9b6d1b0fd2c52f9

Aliases

arxiv: 2605.16681 · arxiv_version: 2605.16681v1 · doi: 10.48550/arxiv.2605.16681 · pith_short_12: RX2VMUWNDTK3 · pith_short_16: RX2VMUWNDTK3BQLI · pith_short_8: RX2VMUWN

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/RX2VMUWNDTK3BQLI3LYLOU5SKG \
  | 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: 8df55652cd1cd5b0c168daf0b753b2518854ecf2c7a1de76f9b6d1b0fd2c52f9

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "f0a5f45969da4b534a4d4f2196c68cb993d11540aeb76df850b87f26a4d81370",
    "cross_cats_sorted": [
      "eess.SP"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "eess.AS",
    "submitted_at": "2026-05-15T22:34:52Z",
    "title_canon_sha256": "d518c121750b4e537c1a138c7d2b724b3b4afe0fc192ec794551b0b035838b4c"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.16681",
    "kind": "arxiv",
    "version": 1
  }
}