Paper Citation Record · LEDGER

On the Application of Diffusion Models for Simultaneous Denoising and Dereverberation

As of 18 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 0 inbound Pith citation observations for arXiv:2508.18833.

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

pith.paper-citation-record.v1
2508.18833 v1

Coverage vector

measured 29 of 29 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T16:13:27.468426Z

measured 29 of 29 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

29 of 29 outbound references displayed

verified exact1
verified fuzzy26
unresolved2
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e4b4ca21-10e2-44c6-a71b-267e5127ae36 · outbound

This paper cites Deep neural network techniques for monaural speech enhancement and separation: state of the art anal- ysis,.

On the Application of Diffusion Models for Simultaneous Denoising and Dereverberation Deep neural network techniques for monaural speech enhancement and separation: state of the art anal- ysis,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:13:33.224824Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ba135620-3189-4812-b33a-8d600278a8d4 · outbound

This paper cites Microphone array signal processing and deep learning for speech enhancement: Combining model- based and data-driven approaches to parameter estimation and filtering,.

On the Application of Diffusion Models for Simultaneous Denoising and Dereverberation Microphone array signal processing and deep learning for speech enhancement: Combining model- based and data-driven approaches to parameter estimation and filtering,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:13:33.019997Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 0f53e706-4825-4e58-82ce-e7bb85c0ce25 · outbound

This paper cites Conditional diffusion probabilistic model for speech enhancement,.

On the Application of Diffusion Models for Simultaneous Denoising and Dereverberation Conditional diffusion probabilistic model for speech enhancement,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:13:32.806386Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 69694474-0d42-4351-9ec8-1d0004430999 · outbound

This paper cites Speech enhance- ment with score-based generative models in the complex stft domain,.

On the Application of Diffusion Models for Simultaneous Denoising and Dereverberation Speech enhance- ment with score-based generative models in the complex stft domain,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:13:32.603397Z

Source-reported events for the cited work

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Observation 1e68daad-dfa7-44c0-a84a-01a41d8166f2 · outbound

This paper cites Diffusion-based speech enhancement in matched and mismatched conditions using a heun-based sampler,.

On the Application of Diffusion Models for Simultaneous Denoising and Dereverberation Diffusion-based speech enhancement in matched and mismatched conditions using a heun-based sampler,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:13:32.415511Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d7d57d5d-712d-4bd8-833c-d940d8fbb641 · outbound

This paper cites Schrödinger bridge for generative speech enhancement,.

On the Application of Diffusion Models for Simultaneous Denoising and Dereverberation Schrödinger bridge for generative speech enhancement,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:13:32.222362Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 19193950-dc6f-4126-bb1c-0f36ec7bfc29 · outbound

This paper cites Analysing diffusion-based generative approaches versus discriminative approaches for speech restoration,.

On the Application of Diffusion Models for Simultaneous Denoising and Dereverberation Analysing diffusion-based generative approaches versus discriminative approaches for speech restoration,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:13:32.047160Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 500ffda8-6ee3-4e7d-b38f-603c2765dde5 · outbound

This paper cites Unsupervised Blind Joint Dereverberation and Room Acoustics Estimation with Diffusion Models.

On the Application of Diffusion Models for Simultaneous Denoising and Dereverberation Unsupervised Blind Joint Dereverberation and Room Acoustics Estimation with Diffusion Models

Reference 8

Resolution: verified exact; local_arxiv, observed 2026-08-05T16:13:27.891191Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 28bd6234-df5c-4cf9-bd2a-ba94499e49c9 · outbound

This paper cites Conditional diffusion probabilistic model for speech enhancement,.

On the Application of Diffusion Models for Simultaneous Denoising and Dereverberation Conditional diffusion probabilistic model for speech enhancement,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:13:31.859021Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e3543c83-8f94-4773-8e2d-cedee4943709 · outbound

This paper cites Speech enhancement and dereverberation with diffusion-based generative models,.

On the Application of Diffusion Models for Simultaneous Denoising and Dereverberation Speech enhancement and dereverberation with diffusion-based generative models,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:13:31.594702Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e92bb9e3-fb41-4d06-b403-af0b0a170e0b · outbound

This paper cites Storm: A diffusion-based stochastic regeneration model for speech enhancement and dereverberation,.

On the Application of Diffusion Models for Simultaneous Denoising and Dereverberation Storm: A diffusion-based stochastic regeneration model for speech enhancement and dereverberation,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:13:31.382997Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 73d66c76-5ac5-4aba-86e0-cb4655a136c5 · outbound

This paper cites Uni- versal score-based speech enhancement with high content preservation,.

On the Application of Diffusion Models for Simultaneous Denoising and Dereverberation Uni- versal score-based speech enhancement with high content preservation,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:13:31.145914Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 293b02cd-cbb2-429e-934e-a8f108b2bdac · outbound

This paper cites Diffusion posterior sampling for informed single-channel dereverber- ation,.

On the Application of Diffusion Models for Simultaneous Denoising and Dereverberation Diffusion posterior sampling for informed single-channel dereverber- ation,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:13:30.923975Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 6b20e353-691c-4a4b-9dcc-321a5d48cdb0 · outbound

This paper cites Interspeech 2025 urgent speech enhance- ment challenge,.

On the Application of Diffusion Models for Simultaneous Denoising and Dereverberation Interspeech 2025 urgent speech enhance- ment challenge,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:13:30.775272Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 19a43f21-06f1-474d-a786-d284557366cc · outbound

This paper cites Diffusion model-based mimo speech denoising and dereverberation,.

On the Application of Diffusion Models for Simultaneous Denoising and Dereverberation Diffusion model-based mimo speech denoising and dereverberation,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:13:30.558203Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 38a15264-9195-4c72-97e5-78a6bd47b8ef · outbound

This paper cites Multi- stream diffusion model for probabilistic integration of model-based and data-driven speech enhancement,.

On the Application of Diffusion Models for Simultaneous Denoising and Dereverberation Multi- stream diffusion model for probabilistic integration of model-based and data-driven speech enhancement,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:13:30.330392Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 6fbf3fd1-ca40-4024-ab84-ac9e77fcc9f8 · outbound

This paper cites usee: Unified speech enhancement and editing with conditional diffusion models,.

On the Application of Diffusion Models for Simultaneous Denoising and Dereverberation usee: Unified speech enhancement and editing with conditional diffusion models,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:13:30.128693Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 346b00d5-5c10-45c4-bde7-a709753d5f6f · outbound

This paper cites Ears: An anechoic full- band speech dataset benchmarked for speech enhancement and dereverberation,.

On the Application of Diffusion Models for Simultaneous Denoising and Dereverberation Ears: An anechoic full- band speech dataset benchmarked for speech enhancement and dereverberation,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:13:29.949256Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d007000f-2df0-46cb-8cde-5070161ee977 · outbound

This paper cites The REVERB challenge: A common evaluation framework for derever- beration and recognition of reverberant speech,.

On the Application of Diffusion Models for Simultaneous Denoising and Dereverberation The REVERB challenge: A common evaluation framework for derever- beration and recognition of reverberant speech,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:13:29.692986Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 9ceeed1d-604e-427a-8676-28ac91b22b84 · outbound

This paper cites A connection between score matching and de- noising autoencoders,.

On the Application of Diffusion Models for Simultaneous Denoising and Dereverberation A connection between score matching and de- noising autoencoders,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:13:29.481936Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation bd6f7902-264f-4ecf-b660-6da8e86b3aac · outbound

This paper cites Score-based generative modeling through stochastic differential equations,.

On the Application of Diffusion Models for Simultaneous Denoising and Dereverberation Score-based generative modeling through stochastic differential equations,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:13:29.264095Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 8d6969f9-e905-4eda-bc63-975e3496d61e · outbound

This paper cites Speech En- hancement and Dereverberation with Diffusion-based Gen- erative Models.

On the Application of Diffusion Models for Simultaneous Denoising and Dereverberation Speech En- hancement and Dereverberation with Diffusion-based Gen- erative Models

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:13:29.076115Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation be4750e1-6387-4924-b2e8-5102de1564a0 · outbound

This paper cites CSR-I (WSJ0) Complete LDC93S6A,.

On the Application of Diffusion Models for Simultaneous Denoising and Dereverberation CSR-I (WSJ0) Complete LDC93S6A,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:13:28.814250Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation da13b4bb-bceb-402b-95f9-f62c60d5ff07 · outbound

This paper cites Dnsmos: A non-intrusive perceptual objective speech quality metric to evaluate noise suppressors,.

On the Application of Diffusion Models for Simultaneous Denoising and Dereverberation Dnsmos: A non-intrusive perceptual objective speech quality metric to evaluate noise suppressors,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:13:28.624574Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c8932e85-ea7a-44b3-8511-4fd0775f83c0 · outbound

This paper cites A non-intrusive quality and intelligibility measure of reverberant and dere- verberated speech,.

On the Application of Diffusion Models for Simultaneous Denoising and Dereverberation A non-intrusive quality and intelligibility measure of reverberant and dere- verberated speech,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:13:28.448898Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f72cc56b-187f-425b-b498-0948c6365999 · outbound

This paper cites An algorithm for predicting the intelligibility of speech masked by modulated noise maskers,.

On the Application of Diffusion Models for Simultaneous Denoising and Dereverberation An algorithm for predicting the intelligibility of speech masked by modulated noise maskers,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:13:28.233596Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 80ab4674-44bc-41bf-9330-980904246246 · outbound

This paper cites SDR - Half-baked or Well Done?,.

On the Application of Diffusion Models for Simultaneous Denoising and Dereverberation SDR - Half-baked or Well Done?,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:13:28.066767Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c88720ba-fc93-4ef4-aad5-dce1b504befd · outbound

This paper cites SMS-WSJ: Database, performance measures, and baseline recipe for multi-channel source separation and recognition.

On the Application of Diffusion Models for Simultaneous Denoising and Dereverberation SMS-WSJ: Database, performance measures, and baseline recipe for multi-channel source separation and recognition

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-05T16:13:27.371847Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0d643a15-f6e3-417d-94bc-46073ad08729 · outbound

This paper cites NeMo: a toolkit for building AI applications using Neural Modules.

On the Application of Diffusion Models for Simultaneous Denoising and Dereverberation NeMo: a toolkit for building AI applications using Neural Modules

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-05T16:13:27.468426Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.