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Paper Citation Record · LEDGER

Diffusion-based Frameworks for Unsupervised Speech Enhancement

As of 22 August 2026, this Paper Citation Record lists 52 of 52 outbound references and 1 inbound Pith citation observation for arXiv:2601.09931.

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

pith.paper-citation-record.v1
2601.09931 v4

Coverage vector

measured 52 of 52 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T10:29:52.007835Z

measured 53 of 53 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-26T23:17:45.299833Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-03T22:49:01.370716Z

Reference resolution

52 of 52 outbound references displayed

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Outbound references

Observation 663f5268-a6b4-454b-81d1-912b6864d606 · outbound

This paper cites Supervised speech separation based on deep learning: An overview,.

Diffusion-based Frameworks for Unsupervised Speech Enhancement Supervised speech separation based on deep learning: An overview,

Reference 1

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Observation c207fc2b-2b91-44d3-adc0-000d8310378a · outbound

This paper cites Conv-TasNet: Surpassing ideal time–frequency magnitude masking for speech separation,.

Diffusion-based Frameworks for Unsupervised Speech Enhancement Conv-TasNet: Surpassing ideal time–frequency magnitude masking for speech separation,

Reference 2

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source=pdf_text observed=2026-08-03T10:29:46.132548Z digest=sha256:297b1b9b297e0c60338e0acf9335122a2a7734ea9eba008146b87a6f08be3f84

Observation 9b82016b-28ee-4ecf-8133-42e5961d620b · outbound

This paper cites TF- GridNet: Making time-frequency domain models great again for monaural speaker separation,.

Diffusion-based Frameworks for Unsupervised Speech Enhancement TF- GridNet: Making time-frequency domain models great again for monaural speaker separation,

Reference 3

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Observation 9ef97742-9bbd-48cb-833d-37016d945a95 · outbound

This paper cites TF-CrossNet: Leveraging global, cross-band, narrow-band, and positional encoding for single- and multi-channel speaker separation,.

Diffusion-based Frameworks for Unsupervised Speech Enhancement TF-CrossNet: Leveraging global, cross-band, narrow-band, and positional encoding for single- and multi-channel speaker separation,

Reference 4

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source=pdf_text observed=2026-08-03T10:29:46.371321Z digest=sha256:d2c22e5523dd1a1968d7dbaba678c718cd8773c754ab492d4a641240f8122a0f

Observation adff0aca-9e62-4493-8b29-6c0872f2fa3a · outbound

This paper cites SEGAN: Speech enhancement generative adversarial network,.

Diffusion-based Frameworks for Unsupervised Speech Enhancement SEGAN: Speech enhancement generative adversarial network,

Reference 5

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source=pdf_text observed=2026-08-03T10:29:46.445021Z digest=sha256:0fd4c00ec8734b113f9cd8ffe392f900e2561cb0d1c583ef5e11230f3695cea4

Observation ac2cf64e-4f1b-4684-abf9-2f98264c5cf2 · outbound

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

Diffusion-based Frameworks for Unsupervised Speech Enhancement Conditional diffusion probabilistic model for speech enhancement,

Reference 6

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source=pdf_text observed=2026-08-03T10:29:46.522170Z digest=sha256:d8d25a10c94144ff8edd0ccdec5807d35b6301ddb669da2876b2b3c4ef0d31a0

Observation 36774dac-a60f-4dae-b6a7-89b965032054 · outbound

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

Diffusion-based Frameworks for Unsupervised Speech Enhancement StoRM: A diffusion-based stochastic regeneration model for speech enhancement and dereverberation,

Reference 7

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source=pdf_text observed=2026-08-03T10:29:46.590769Z digest=sha256:de9d2cd9ac4b358a12b07d77c65a957fe2f8b1440d0382aeecdb530952e7e709

Observation e3c93030-2603-42b8-b75f-5e35c1fce79c · outbound

This paper cites A composite predictive-generative approach to monaural universal speech enhancement,.

Diffusion-based Frameworks for Unsupervised Speech Enhancement A composite predictive-generative approach to monaural universal speech enhancement,

Reference 8

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source=pdf_text observed=2026-08-03T10:29:46.728488Z digest=sha256:398b9d52bfb71b8a504db7c21b425d3d45a3909e0d14091218bc12f2d980b9b4

Observation e4c051cc-0180-4ec5-a50e-6ab5323e1490 · outbound

This paper cites GAN-based speech enhancement for low snr using latent feature conditioning,.

Diffusion-based Frameworks for Unsupervised Speech Enhancement GAN-based speech enhancement for low snr using latent feature conditioning,

Reference 9

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Observation c22645ca-d22d-4f47-be5b-b35f29500dde · outbound

This paper cites Improving deep speech denoising by noisy2noisy signal mapping,.

Diffusion-based Frameworks for Unsupervised Speech Enhancement Improving deep speech denoising by noisy2noisy signal mapping,

Reference 10

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source=pdf_text observed=2026-08-03T10:29:46.992303Z digest=sha256:abfe0b4d0b5f0c8a3fc34be99811ea8b206c0842469be8b5dd362a47e87641aa

Observation 011dba21-2477-4b49-a131-b74a14bb064f · outbound

This paper cites Analysis of noisy-target training for DNN-based speech enhancement,.

Diffusion-based Frameworks for Unsupervised Speech Enhancement Analysis of noisy-target training for DNN-based speech enhancement,

Reference 11

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source=pdf_text observed=2026-08-03T10:29:47.156804Z digest=sha256:ff9b6517b9ff501d6bb6d2fb12b581725ccb08910eb193820dc3581d82701f05

Observation af3a3e24-d2d9-4867-8468-99ee421d294c · outbound

This paper cites Unsupervised sound separation using mixture invariant training,.

Diffusion-based Frameworks for Unsupervised Speech Enhancement Unsupervised sound separation using mixture invariant training,

Reference 12

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source=pdf_text observed=2026-08-03T10:29:47.271580Z digest=sha256:0473985e7401cf3d20815a989f64d2c0d51d98a8813f2dd9ba89547ca93a3bf1

Observation da5f590a-37d2-43d0-8343-09d445295ccc · outbound

This paper cites RemixIT: Continual self-training of speech enhancement models via boot- strapped remixing,.

Diffusion-based Frameworks for Unsupervised Speech Enhancement RemixIT: Continual self-training of speech enhancement models via boot- strapped remixing,

Reference 13

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source=pdf_text observed=2026-08-03T10:29:47.429312Z digest=sha256:7af7a2888f339f3504121db2b63e7f6d6b699de262609fe0e9a8497f96d5ad05

Observation 7f7e4077-549a-4fd1-8e2c-0eb7f92026cb · outbound

This paper cites Self-supervised speech denoising using only noisy audio signals,.

Diffusion-based Frameworks for Unsupervised Speech Enhancement Self-supervised speech denoising using only noisy audio signals,

Reference 14

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source=pdf_text observed=2026-08-03T10:29:47.540467Z digest=sha256:20a757cd311495356fbf3e3499cafff2ec605eb1b19984181d6ecca2195715e1

Observation 56535acc-bb6b-4359-b83d-9f1f4d71d65f · outbound

This paper cites A parallel-data-free speech enhancement method using multi-objective learning cycle-consistent generative adversarial network,.

Diffusion-based Frameworks for Unsupervised Speech Enhancement A parallel-data-free speech enhancement method using multi-objective learning cycle-consistent generative adversarial network,

Reference 15

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source=pdf_text observed=2026-08-03T10:29:47.662628Z digest=sha256:cf67307966415e906f67cb5528658dd6f7d9a25931e9dbe9c5ba8ed3c86296e8

Observation 925b11ee-bdb6-4650-95d6-186aa947331a · outbound

This paper cites MetricGAN- U: Unsupervised speech enhancement/dereverberation based only on noisy/reverberated speech,.

Diffusion-based Frameworks for Unsupervised Speech Enhancement MetricGAN- U: Unsupervised speech enhancement/dereverberation based only on noisy/reverberated speech,

Reference 16

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source=pdf_text observed=2026-08-03T10:29:47.820490Z digest=sha256:ea9e461a7c8f94acdf445ef102b764d7d6b0179a1f17509ec5533d6adfa98c9c

Observation 130569a8-bb11-4c82-9a56-1fc9ac960322 · outbound

This paper cites Statistical speech enhancement based on probabilistic integration of variational autoen- coder and non-negative matrix factorization,.

Diffusion-based Frameworks for Unsupervised Speech Enhancement Statistical speech enhancement based on probabilistic integration of variational autoen- coder and non-negative matrix factorization,

Reference 17

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source=pdf_text observed=2026-08-03T10:29:47.969825Z digest=sha256:1fb74953e8fba9b9af803e986f66aa66700c9b01d3334d9d4fa8b130897da8ab

Observation d848a438-bd05-4e98-ae11-21aaa2cadfaf · outbound

This paper cites Unsupervised speech enhancement using dynamical variational autoencoders,.

Diffusion-based Frameworks for Unsupervised Speech Enhancement Unsupervised speech enhancement using dynamical variational autoencoders,

Reference 18

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source=pdf_text observed=2026-08-03T10:29:48.122680Z digest=sha256:03da439e13829f41b7acc781b4e81ec45baac8850bec2e064e5cfcd499d41646

Observation 92487dda-0993-4a8b-aad5-54a19f995015 · outbound

This paper cites Unsupervised speech enhancement with diffusion-based generative models,.

Diffusion-based Frameworks for Unsupervised Speech Enhancement Unsupervised speech enhancement with diffusion-based generative models,

Reference 19

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source=pdf_text observed=2026-08-03T10:29:48.240363Z digest=sha256:6253e85d197be5aefe35370a938c8c4ddf5c7fcf7922bcba95e47f2a0d718e1d

Observation 650304c2-52d1-434f-a032-328b912af4a9 · outbound

This paper cites Diffusion- based unsupervised audio-visual speech enhancement,.

Diffusion-based Frameworks for Unsupervised Speech Enhancement Diffusion- based unsupervised audio-visual speech enhancement,

Reference 20

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source=pdf_text observed=2026-08-03T10:29:48.346958Z digest=sha256:12d2d4d298901eb6ad7106e69828aa3306d815bcefe6b8985911fbe1d062737e

Observation db0e63bd-7d6b-4141-b73d-4f247d3cb185 · outbound

This paper cites Posterior transition modeling for unsupervised diffusion-based speech enhancement,.

Diffusion-based Frameworks for Unsupervised Speech Enhancement Posterior transition modeling for unsupervised diffusion-based speech enhancement,

Reference 21

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source=pdf_text observed=2026-08-03T10:29:48.460810Z digest=sha256:4a0463bc9c2f54720aeca372175a571521d6fd27e87e515c0b1c1fdeb664d7ff

Observation 76084337-5199-4bae-918b-e76cb10caa34 · outbound

This paper cites Diffusion models for audio restoration: A review,.

Diffusion-based Frameworks for Unsupervised Speech Enhancement Diffusion models for audio restoration: A review,

Reference 22

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source=pdf_text observed=2026-08-03T10:29:48.601764Z digest=sha256:37eee27112837500409ccb415c4b0f1c01e0cb45ebd562c159dc58582af2d9c2

Observation 4b8cd1ba-e0bb-45de-8662-38b41a5e48b1 · outbound

This paper cites A Survey on Diffusion Models for Inverse Problems.

Diffusion-based Frameworks for Unsupervised Speech Enhancement A Survey on Diffusion Models for Inverse Problems

Reference 23

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source=pdf_text observed=2026-08-03T10:29:48.698417Z digest=sha256:2cb918397eff5cb1230080f91677f8a339a651f9be3276b0f80a852d1cd28364

Observation 56b9794a-7f86-4357-a04a-9da25366a5c0 · outbound

This paper cites Parallel diffusion models of operator and image for blind inverse problems,.

Diffusion-based Frameworks for Unsupervised Speech Enhancement Parallel diffusion models of operator and image for blind inverse problems,

Reference 24

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source=pdf_text observed=2026-08-03T10:29:48.868529Z digest=sha256:48f879730d801ada2ad42864939c64de2555a0765f1029c207fd77e505b2e85e

Observation f4ac0303-e0e9-4585-a3c1-5b658f73f6fc · outbound

This paper cites Diffusion-based unsuper- vised audio-visual speech separation in noisy environments with noise prior,.

Diffusion-based Frameworks for Unsupervised Speech Enhancement Diffusion-based unsuper- vised audio-visual speech separation in noisy environments with noise prior,

Reference 25

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source=pdf_text observed=2026-08-03T10:29:48.979821Z digest=sha256:1e95b183e3b26a1200ce1906fc1906c437482e8f7a7a63712b035c373dfe0cf7

Observation 095e644e-1538-4fe9-ae95-bb20dde71a81 · outbound

This paper cites Multi-source diffusion models for simultaneous music generation and sepa- ration,.

Diffusion-based Frameworks for Unsupervised Speech Enhancement Multi-source diffusion models for simultaneous music generation and sepa- ration,

Reference 26

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source=pdf_text observed=2026-08-03T10:29:49.074014Z digest=sha256:d4ce967629bf8ddca422b043f40161d9ba715863cf70a0d01e1da59e364a9bb4

Observation fe0f2543-2b10-48c5-9a02-4beff7c76f74 · outbound

This paper cites A recurrent variational autoencoder for speech enhancement,.

Diffusion-based Frameworks for Unsupervised Speech Enhancement A recurrent variational autoencoder for speech enhancement,

Reference 27

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source=pdf_text observed=2026-08-03T10:29:49.195659Z digest=sha256:deabe72193c49f91aedf79db314c698955154f0bac95358f129ebb50d2ed5c3d

Observation 52341dd8-0735-422a-8bd1-0a2393ac90b5 · outbound

This paper cites Investigating RNN- based speech enhancement methods for noise-robust text-to-speech,.

Diffusion-based Frameworks for Unsupervised Speech Enhancement Investigating RNN- based speech enhancement methods for noise-robust text-to-speech,

Reference 28

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source=pdf_text observed=2026-08-03T10:29:49.396160Z digest=sha256:f418e6958c4e86e28f28db514e0d93c856a81b3dff2fba78c86cb008c7e76739

Observation 97e3003c-536f-4034-81f9-17d93022d75c · outbound

This paper cites A connection between score matching and denoising autoen- coders,.

Diffusion-based Frameworks for Unsupervised Speech Enhancement A connection between score matching and denoising autoen- coders,

Reference 29

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source=pdf_text observed=2026-08-03T10:29:49.521935Z digest=sha256:9cd83d991bed5116d9bfae53578d74443cd1d8883944e20acc9eb0313593ef78

Observation c3dcaa5d-eeb0-48ac-a830-d7f8c9639337 · outbound

This paper cites Generative modeling by estimating gradients of the data distribution,.

Diffusion-based Frameworks for Unsupervised Speech Enhancement Generative modeling by estimating gradients of the data distribution,

Reference 30

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source=pdf_text observed=2026-08-03T10:29:49.632171Z digest=sha256:3768e5e402a1754c0812cef288a4444444908ecd7082123a2802bb1a80852390

Observation a6c6e3fc-53a2-4265-8b3a-73cacfe352b4 · outbound

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

Diffusion-based Frameworks for Unsupervised Speech Enhancement Score-based generative modeling through stochastic differential equations,

Reference 31

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source=pdf_text observed=2026-08-03T10:29:49.758395Z digest=sha256:b6cd653a40298a8d15afec67662ecb86be3e0a236e828ebb4f01cb74dac1bc62

Observation e62547e6-42a2-48cd-bf2d-18de358f272f · outbound

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

Diffusion-based Frameworks for Unsupervised Speech Enhancement Speech enhancement and dereverberation with diffusion-based generative models,

Reference 32

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source=pdf_text observed=2026-08-03T10:29:49.881354Z digest=sha256:a4daaafc7cdd5e547bc938fb62eb24a8cf0361c34e32e939ee88fbbe5053e11f

Observation db330aaa-0ff0-4b3b-b124-3dc3391f1a1d · outbound

This paper cites Solving inverse problems in medical imaging with score-based generative models,.

Diffusion-based Frameworks for Unsupervised Speech Enhancement Solving inverse problems in medical imaging with score-based generative models,

Reference 33

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source=pdf_text observed=2026-08-03T10:29:49.970531Z digest=sha256:7c1cc9a2ef3eb67dcec99ab59e966c7d0ca96abcafc39e3dd1a078f1781540ec

Observation 6b2c3c18-90f0-456f-924f-3d1b2eeee005 · outbound

This paper cites Tweedie’s formula and selection bias,.

Diffusion-based Frameworks for Unsupervised Speech Enhancement Tweedie’s formula and selection bias,

Reference 34

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source=pdf_text observed=2026-08-03T10:29:50.050617Z digest=sha256:b099068bddd48d2502d448fb9155d34bcc9581e660d80db2976941d97cc86b23

Observation 28419408-5dd8-4caa-b65b-e91771f0b0c0 · outbound

This paper cites Vincent, T.

Diffusion-based Frameworks for Unsupervised Speech Enhancement Vincent, T

Reference 35

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source=pdf_text observed=2026-08-03T10:29:50.105514Z digest=sha256:e794be1c1979fec9719731b8bdee5791cc85cd64d340df6838c6fe65f9699a5c

Observation c1f444a2-72dc-4d33-b116-0ff0e2287849 · outbound

This paper cites Nonnegative matrix factorization with the itakura-saito divergence: With application to music analysis,.

Diffusion-based Frameworks for Unsupervised Speech Enhancement Nonnegative matrix factorization with the itakura-saito divergence: With application to music analysis,

Reference 36

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source=pdf_text observed=2026-08-03T10:29:50.264726Z digest=sha256:310dbe9da34b998ec68721be2a2ef18242447063a987d6d28cde6a9c64cf8ed9

Observation 88e4633c-241d-4c1d-826e-eb67a5dcfa4c · outbound

This paper cites Diffusion model based posterior sampling for noisy linear inverse problems,.

Diffusion-based Frameworks for Unsupervised Speech Enhancement Diffusion model based posterior sampling for noisy linear inverse problems,

Reference 37

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source=pdf_text observed=2026-08-03T10:29:50.482659Z digest=sha256:5b936e6ab8418fccf6a64f0716fd5b4e5b61e23a699a3633c170ed169fe8b1fe

Observation 348150bd-6ab7-4eba-8cc4-65abda5c80f8 · outbound

This paper cites Diffusion posterior sampling for general noisy inverse problems,.

Diffusion-based Frameworks for Unsupervised Speech Enhancement Diffusion posterior sampling for general noisy inverse problems,

Reference 38

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source=pdf_text observed=2026-08-03T10:29:50.641902Z digest=sha256:71d8180feb5c357d6ce09971379ddd5b94285a3163af32529d75444d6452c0c5

Observation 803f51d3-1508-481f-8167-6e996c426a39 · outbound

This paper cites CSR-I (WSJ0) complete LDC93S6B,.

Diffusion-based Frameworks for Unsupervised Speech Enhancement CSR-I (WSJ0) complete LDC93S6B,

Reference 39

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source=pdf_text observed=2026-08-03T10:29:50.817436Z digest=sha256:e809df37c3353768537883f2a7836705ec39e4885aa12bd260c5f567f089cfc1

Observation cd7e3209-06c4-496f-aa44-42e22c04ed0e · outbound

This paper cites The voice bank corpus: Design, collection and data analysis of a large regional accent speech database,.

Diffusion-based Frameworks for Unsupervised Speech Enhancement The voice bank corpus: Design, collection and data analysis of a large regional accent speech database,

Reference 40

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source=pdf_text observed=2026-08-03T10:29:50.981106Z digest=sha256:5646bdef12705314fde0a147ca71960f0f1a9c1e63c17f16025f2947467fd3c7

Observation fe4d8429-0cf1-4eea-881d-70e6d3692bde · outbound

This paper cites The QUT-NOISE-SRE protocol for the evaluation of noisy speaker recognition,.

Diffusion-based Frameworks for Unsupervised Speech Enhancement The QUT-NOISE-SRE protocol for the evaluation of noisy speaker recognition,

Reference 41

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source=pdf_text observed=2026-08-03T10:29:51.150702Z digest=sha256:2f149597c57d006ccac7e54c5478aaf82cf583292d9da9c903387208d821d1b7

Observation 8589b014-cd9f-43b5-bbb3-5d82c617e1ab · outbound

This paper cites The diverse environments multi- channel acoustic noise database (DEMAND): A database of multichannel environmental noise recordings,.

Diffusion-based Frameworks for Unsupervised Speech Enhancement The diverse environments multi- channel acoustic noise database (DEMAND): A database of multichannel environmental noise recordings,

Reference 42

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source=pdf_text observed=2026-08-03T10:29:51.245684Z digest=sha256:4fcee5f74d3d8d3e54538f676c2e343e5ebc0b2d21b95af1e0ff0832db89c4c0

Observation 075b66f7-79ab-4396-8503-6a72eec231d7 · outbound

This paper cites Objective measurement of active speech level,.

Diffusion-based Frameworks for Unsupervised Speech Enhancement Objective measurement of active speech level,

Reference 43

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-03T10:29:51.316446Z digest=sha256:b197e1c1f45e075fa4b8e8404bf1d3dbf9728ca2434ec95b6c456af97ab93520

Observation 951fff6c-87a9-4907-a15f-12c04856cd1a · outbound

This paper cites Algorithms to measure audio pro- gramme loudness and true-peak audio level,.

Diffusion-based Frameworks for Unsupervised Speech Enhancement Algorithms to measure audio pro- gramme loudness and true-peak audio level,

Reference 44

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-03T10:29:51.385846Z digest=sha256:18abca6e492a9dfb2d4aeffb456aca395630414ed7c03c754d65c6e58293b532

Observation eccd053b-a2a5-4a37-8d77-b269cb16895c · outbound

This paper cites SDR–half-baked or well done?,.

Diffusion-based Frameworks for Unsupervised Speech Enhancement SDR–half-baked or well done?,

Reference 45

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T10:29:51.439659Z digest=sha256:f386252417afc36f2d4e000422113917131169736897007b4ae92925db92b325

Observation 22093f00-df50-4d20-b345-bc502ee47f8a · outbound

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

Diffusion-based Frameworks for Unsupervised Speech Enhancement An algorithm for predicting the intelligibility of speech masked by modulated noise maskers,

Reference 46

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-03T10:29:51.531823Z digest=sha256:7d9fd9a0c4d8ee947b38103922f9688ee4c0abbb7d992c3e624d4f591a629713

Observation 78b34de6-cd4f-4344-808f-a67941941195 · outbound

This paper cites Perceptual evaluation of speech quality (PESQ)-a new method for speech quality assess- ment of telephone networks and codecs,.

Diffusion-based Frameworks for Unsupervised Speech Enhancement Perceptual evaluation of speech quality (PESQ)-a new method for speech quality assess- ment of telephone networks and codecs,

Reference 47

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-03T10:29:51.597060Z digest=sha256:50c36aa6abb53e2abe9bd871f82d44b73df2f7e31667a4d3775ccf918df7e7d4

Observation b23c5985-f460-47a0-8158-81db09813da8 · outbound

This paper cites DNSMOS P. 835: A non-intrusive perceptual objective speech quality metric to evaluate noise suppressors,.

Diffusion-based Frameworks for Unsupervised Speech Enhancement DNSMOS P. 835: A non-intrusive perceptual objective speech quality metric to evaluate noise suppressors,

Reference 48

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source=pdf_text observed=2026-08-03T10:29:51.692602Z digest=sha256:ac2ff44f141f5501b53fc892481ee370695659156288d1edac838a9c9f8caf70

Observation 31d5a461-73c6-4389-8a82-6a14ef078a8a · outbound

This paper cites FiLM: Visual reasoning with a general conditioning layer,.

Diffusion-based Frameworks for Unsupervised Speech Enhancement FiLM: Visual reasoning with a general conditioning layer,

Reference 49

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-03T10:29:51.748863Z digest=sha256:a646beb76138cccfc7cbe7f7257321b6a10e59f2045b70e60ea900f35a758203

Observation 32ba2505-f2de-455a-a58e-9ce1a77a3cbb · outbound

This paper cites Objective and subjective evaluation of speech enhancement methods in the udase task of the 7th chime challenge,.

Diffusion-based Frameworks for Unsupervised Speech Enhancement Objective and subjective evaluation of speech enhancement methods in the udase task of the 7th chime challenge,

Reference 50

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source=pdf_text observed=2026-08-03T10:29:51.823762Z digest=sha256:2365f40afabc57e6a4e7a13078ec1b640dff37f8f206b48e646740fed071fc76

Observation 3cf71137-459b-4206-9faa-0b66e020b862 · outbound

This paper cites Sudo rm-rf: Efficient networks for universal audio source separation,.

Diffusion-based Frameworks for Unsupervised Speech Enhancement Sudo rm-rf: Efficient networks for universal audio source separation,

Reference 51

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source=pdf_text observed=2026-08-03T10:29:51.936584Z digest=sha256:0901dee6d9c688029f76dbc17f52369e97ecee18aabe28c638bf44668dbdbcad

Observation 45030ebc-d7e8-4426-b424-ca67ffac0dfb · outbound

This paper cites Masked spectrogram prediction for unsupervised domain adaptation in speech enhancement,.

Diffusion-based Frameworks for Unsupervised Speech Enhancement Masked spectrogram prediction for unsupervised domain adaptation in speech enhancement,

Reference 52

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Pith citing papers

Observation f9107a07-13aa-4db5-aaa1-dd0767c09fdb · inbound

Audio-visual Contrastive Alignment for Diffusion-based Visual-conditioned Speech Enhancement cites this paper.

Audio-visual Contrastive Alignment for Diffusion-based Visual-conditioned Speech Enhancement Diffusion-based Frameworks for Unsupervised Speech Enhancement

Reference 21

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source=pdf_text observed=2026-06-26T23:17:45.299833Z digest=sha256:4d3fdb82b1ec1c3359e6e34f16c309e6d14e238e843ef79fdb7714923be6f67c