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Paper Citation Record · LEDGER

ICASSP 2023 Deep Noise Suppression Challenge

As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2303.11510.

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

pith.paper-citation-record.v1
2303.11510 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:42:39.748666Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T22:07:47.703675Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation bd69fe6a-349b-4f5f-be5e-4ca7af14ca70 · inbound

Model as Loss: A Self-Consistent Training Paradigm cites this paper.

Model as Loss: A Self-Consistent Training Paradigm ICASSP 2023 Deep Noise Suppression Challenge

Reference 2023

Resolution
unresolved
no resolver link, observed 2026-08-07T13:42:39.748666Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:42:39.748666Z digest=sha256:c30721ae00778927b593a3020498bb98944845b2292ae579e461cd559b35f6e5

Observation bc1785be-d6f9-4cf2-930e-ca73a330e33f · inbound

UniFlow: Unifying Speech Front-End Tasks via Continuous Generative Modeling cites this paper.

UniFlow: Unifying Speech Front-End Tasks via Continuous Generative Modeling ICASSP 2023 Deep Noise Suppression Challenge

Reference 10

Resolution
verified exact
local_arxiv, observed 2026-08-05T22:07:47.811588Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-05T22:07:46.078096Z digest=sha256:a6d163b15cb1c230255976983eaf43e634838ea15c959e3a29e97c327eb54448