Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
ICASSP 2023 Deep Noise Suppression Challenge
As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2303.11510.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-16T04:25:22.143745Z
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 f0698681-126e-4b33-95a7-5c47b6626268 · inbound
How much to Dereverberate? Low-Latency Single-Channel Speech Enhancement in Distant Microphone Scenarios cites this paper.
How much to Dereverberate? Low-Latency Single-Channel Speech Enhancement in Distant Microphone Scenarios ICASSP 2023 Deep Noise Suppression Challenge
Reference 13
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T04:25:22.143745Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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.
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 2026-08-20T06:33:59.587034+00:00.