Paper Citation Record · LEDGER

The INTERSPEECH 2020 Deep Noise Suppression Challenge: Datasets, Subjective Testing Framework, and Challenge Results

As of 1 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2005.13981.

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

pith.paper-citation-record.v1
2005.13981 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-01T06:32:01.292127+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-14T22:43:13.611383Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T05:47:40.876145Z

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 211cea05-a20f-4056-a2a5-4a01c47a2ca6 · inbound

SenSE: Semantic-Aware High-Fidelity Universal Speech Enhancement cites this paper.

SenSE: Semantic-Aware High-Fidelity Universal Speech Enhancement The INTERSPEECH 2020 Deep Noise Suppression Challenge: Datasets, Subjective Testing Framework, and Challenge Results

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-18T12:36:22.334454Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 44f4ce3d-d9e7-42f1-9adf-4486b71db6e9 · inbound

Fast-ULCNet: A fast and ultra low complexity network for single-channel speech enhancement cites this paper.

Fast-ULCNet: A fast and ultra low complexity network for single-channel speech enhancement The INTERSPEECH 2020 Deep Noise Suppression Challenge: Datasets, Subjective Testing Framework, and Challenge Results

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-16T12:30:53.913390Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation c14b087d-210b-4b47-8cdf-5ba2e513b9b7 · inbound

SEMamba++: A General Speech Restoration Framework Leveraging Global, Local, and Periodic Spectral Patterns cites this paper.

SEMamba++: A General Speech Restoration Framework Leveraging Global, Local, and Periodic Spectral Patterns The INTERSPEECH 2020 Deep Noise Suppression Challenge: Datasets, Subjective Testing Framework, and Challenge Results

Reference 59

Resolution: unresolved; no resolver link, observed 2026-07-14T22:43:13.611383Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3af3e043-43c5-4a7d-b3b1-61065d9a0215 · inbound

Position-Aware Target Speaker Extraction for Long-Form Multi-Party Conversations: A Diarization-Free Framework for ASR cites this paper.

Position-Aware Target Speaker Extraction for Long-Form Multi-Party Conversations: A Diarization-Free Framework for ASR The INTERSPEECH 2020 Deep Noise Suppression Challenge: Datasets, Subjective Testing Framework, and Challenge Results

Reference 38

Resolution: verified exact; arxiv_id, observed 2026-06-30T02:54:10.768935Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation ce05b709-06ba-42cb-a9cf-b439530e8ef4 · inbound

SelectTSL: Prompt-Guided Selective Target Sound Localization in Complex Scenarios cites this paper.

SelectTSL: Prompt-Guided Selective Target Sound Localization in Complex Scenarios The INTERSPEECH 2020 Deep Noise Suppression Challenge: Datasets, Subjective Testing Framework, and Challenge Results

Reference 65

Resolution: verified exact; arxiv_id, observed 2026-07-03T05:47:40.877703Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-01T06:32:01.292127+00:00.

Observation 5c37857a-f9b1-4983-837f-244b52137e8b · inbound

Ranking the Impact of Contextual Specialization in Neural Speech Enhancement cites this paper.

Ranking the Impact of Contextual Specialization in Neural Speech Enhancement The INTERSPEECH 2020 Deep Noise Suppression Challenge: Datasets, Subjective Testing Framework, and Challenge Results

Reference 10

Resolution: unresolved; no resolver link, observed 2026-07-11T12:54:14.084815Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.