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Paper Citation Record · LEDGER

Towards Array-Invariant Speech Enhancement via Geometry-Aware Dynamic Convolution

As of 21 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 1 inbound Pith citation observation for arXiv:2607.18658.

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

pith.paper-citation-record.v1
2607.18658 v1

Coverage vector

measured 30 of 30 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T14:47:16.214424Z

measured 31 of 31 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-01T14:47:14.238419Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

30 of 30 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved29
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 098ec478-e828-4094-927b-0a434f2d313c · outbound

This paper cites Towards Array-Invariant Speech Enhancement via Geometry-Aware Dynamic Convolution.

Towards Array-Invariant Speech Enhancement via Geometry-Aware Dynamic Convolution Towards Array-Invariant Speech Enhancement via Geometry-Aware Dynamic Convolution

Reference 1

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T14:47:14.238419Z digest=sha256:3cc11f8b43be45569d4ed815fe47b8f24e872641b69b873e2565b4df15fde5c3

Observation e8946a9f-599c-4e39-9ca1-b97321429d58 · outbound

This paper cites Input features are stacked along the channel dimension, and inter-channel infor- mation is fused within the layer to form the output [10, 11].

Towards Array-Invariant Speech Enhancement via Geometry-Aware Dynamic Convolution Input features are stacked along the channel dimension, and inter-channel infor- mation is fused within the layer to form the output [10, 11]

Reference 2

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source=pdf_text observed=2026-08-01T14:47:14.352723Z digest=sha256:351661684c1df6af6c7dc6859736e708f5d7b0267556a3d572b3409ea869ad5d

Observation b7c58c4f-3c7b-4ef2-a909-b10a3e5f4752 · outbound

This paper cites an unresolved cited work.

Towards Array-Invariant Speech Enhancement via Geometry-Aware Dynamic Convolution Unresolved cited work

Reference 3

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source=pdf_text observed=2026-08-01T14:47:14.514726Z digest=sha256:f32f2c9775c14c894bb98b132f3e81ca44fdecdebf3657f843543c8e48907694

Observation 33da09ee-1385-4128-8cde-6f2716fe0b55 · outbound

This paper cites Impact of Fixed vs.

Towards Array-Invariant Speech Enhancement via Geometry-Aware Dynamic Convolution Impact of Fixed vs

Reference 4

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T14:47:14.612712Z digest=sha256:6abdfa01964f02117d0b83c15f26635ff96c08867567d959c4f0e76a3809bf55

Observation 6fc53ebb-4071-4bae-8c25-ec5b0461489d · outbound

This paper cites an unresolved cited work.

Towards Array-Invariant Speech Enhancement via Geometry-Aware Dynamic Convolution Unresolved cited work

Reference 5

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source=pdf_text observed=2026-08-01T14:47:14.681917Z digest=sha256:35c4961ac8a6a899176fcc352a5949adf4ee01d9bc8859577323bd896a4ec30c

Observation 24026e50-30ee-4d1b-8b3b-c5c9ff09940d · outbound

This paper cites U25A20409, and in part by SJTU Med-X (Medicine & Engineering) Translational Research Grant (YG2025LC09).

Towards Array-Invariant Speech Enhancement via Geometry-Aware Dynamic Convolution U25A20409, and in part by SJTU Med-X (Medicine & Engineering) Translational Research Grant (YG2025LC09)

Reference 6

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source=pdf_text observed=2026-08-01T14:47:14.834046Z digest=sha256:c8970555fd55555c2e15f5550fd96233d7cf276ebad80cd6bb9842451b39f6c9

Observation 52c68b56-89a5-475d-abdc-eee3a1c64aed · outbound

This paper cites All scientific content, ideas, analysis, and conclusions are original and fully authored by the researchers.

Towards Array-Invariant Speech Enhancement via Geometry-Aware Dynamic Convolution All scientific content, ideas, analysis, and conclusions are original and fully authored by the researchers

Reference 7

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source=pdf_text observed=2026-08-01T14:47:14.939236Z digest=sha256:af0c487355fb0e6925f2bedfadcaa2db993e2312d02e09afed0a62a4c021d7d2

Observation bf94a1e9-040c-43ae-b338-f2b2324bd4a8 · outbound

This paper cites SenSE: Semantic-aware high-fidelity universal speech enhancement,.

Towards Array-Invariant Speech Enhancement via Geometry-Aware Dynamic Convolution SenSE: Semantic-aware high-fidelity universal speech enhancement,

Reference 8

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source=pdf_text observed=2026-08-01T14:47:15.019958Z digest=sha256:c8eb77a90b0d824288bbc50ca3e24fbdad15bb0892ce3310118c7a36d83506f7

Observation f0119d4b-071c-4600-936b-06704a5ae9f2 · outbound

This paper cites DNN-based geometry- invariant DOA estimation with microphone positional encoding and complexity gradual training,.

Towards Array-Invariant Speech Enhancement via Geometry-Aware Dynamic Convolution DNN-based geometry- invariant DOA estimation with microphone positional encoding and complexity gradual training,

Reference 9

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source=pdf_text observed=2026-08-01T14:47:15.390874Z digest=sha256:5cf1f86f1f1d30b496be35f2863762290bad1d8dbe017771b57fb2c069c3cb14

Observation a464dd8f-b77b-4de0-ab51-d6efcb5eb7f6 · outbound

This paper cites AnyEnhance: A unified generative model with prompt-guidance and self-critic for voice enhancement,.

Towards Array-Invariant Speech Enhancement via Geometry-Aware Dynamic Convolution AnyEnhance: A unified generative model with prompt-guidance and self-critic for voice enhancement,

Reference 10

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source=pdf_text observed=2026-08-01T14:47:15.086359Z digest=sha256:c9acaad5bace22549e39175d273281f877b382fe581ed6ecd578ceb0a3078420

Observation ef4b40e5-5d89-49fc-b7e3-c9eddc8c7901 · outbound

This paper cites End-to- end microphone permutation and number invariant multi-channel speech separation,.

Towards Array-Invariant Speech Enhancement via Geometry-Aware Dynamic Convolution End-to- end microphone permutation and number invariant multi-channel speech separation,

Reference 11

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source=pdf_text observed=2026-08-01T14:47:15.122307Z digest=sha256:3718360f9d3b1bd8f1dd718ab3a50220c9733f659f7fb0d49d6411dd685d29d1

Observation 6f1eb3e5-6f50-4b62-9055-b8ff512afca5 · outbound

This paper cites TPARN: Triple-path attentive recurrent network for time-domain multichannel speech enhancement,.

Towards Array-Invariant Speech Enhancement via Geometry-Aware Dynamic Convolution TPARN: Triple-path attentive recurrent network for time-domain multichannel speech enhancement,

Reference 12

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T14:47:15.164002Z digest=sha256:e96363672ad805b75ca8a728328f6d04da37408b05b37ea265bce6a387b6b0ce

Observation bc288d78-d7a0-4a3a-8079-e20bad8b2841 · outbound

This paper cites Improving design of in- put condition invariant speech enhancement,.

Towards Array-Invariant Speech Enhancement via Geometry-Aware Dynamic Convolution Improving design of in- put condition invariant speech enhancement,

Reference 13

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source=pdf_text observed=2026-08-01T14:47:15.211397Z digest=sha256:b088ee8938bee70f321c974d4f720c5edc25e49d3ee49965c0d465d0a10ac0d4

Observation be6a1abf-efc3-44d1-8af0-8d8d993f8aba · outbound

This paper cites AmbiDrop: Array-Agnostic Speech Enhancement Using Ambisonics Encoding and Dropout-Based Learning.

Towards Array-Invariant Speech Enhancement via Geometry-Aware Dynamic Convolution AmbiDrop: Array-Agnostic Speech Enhancement Using Ambisonics Encoding and Dropout-Based Learning

Reference 14

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source=pdf_text observed=2026-08-01T14:47:15.259938Z digest=sha256:fc62f4d00b70ea710443fa8f52f8a01607cf93b808300a81a70f6d83d9095842

Observation 63cb14ad-d358-44fe-9541-771eef592f21 · outbound

This paper cites UniArray: Unified spectral-spatial modeling for array-geometry-agnostic speech separation,.

Towards Array-Invariant Speech Enhancement via Geometry-Aware Dynamic Convolution UniArray: Unified spectral-spatial modeling for array-geometry-agnostic speech separation,

Reference 15

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source=pdf_text observed=2026-08-01T14:47:15.309007Z digest=sha256:54bd1b69be95b186b1b4a90e33e5de2cec23f53132e365979d045b46dbd3ed4a

Observation cc8cdf1d-f9fb-4cd8-b90f-c15c70256534 · outbound

This paper cites A memory-based gravitational search al- gorithm for enhancing minimum variance distortionless response beamforming,.

Towards Array-Invariant Speech Enhancement via Geometry-Aware Dynamic Convolution A memory-based gravitational search al- gorithm for enhancing minimum variance distortionless response beamforming,

Reference 16

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source=pdf_text observed=2026-08-01T14:47:15.359868Z digest=sha256:382eeb3f66aaf8738a8724e8cf6f7cc896c291afe7e43c7cfcf3421a900e0ca0

Observation 072a0c16-3ecf-4eaa-89e4-925c094e5186 · outbound

This paper cites TF-GridNet: Integrating full- and sub-band modeling for speech separation,.

Towards Array-Invariant Speech Enhancement via Geometry-Aware Dynamic Convolution TF-GridNet: Integrating full- and sub-band modeling for speech separation,

Reference 17

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source=pdf_text observed=2026-08-01T14:47:15.855365Z digest=sha256:ddd3c3e41a30942b8009ca79f54229a935475a40d48b8c548d8404e69abd1c72

Observation 40156162-b37a-49e7-8b50-685bfab7869b · outbound

This paper cites Beam-TasNet: Time-domain audio separation net- work meets frequency-domain beamformer,.

Towards Array-Invariant Speech Enhancement via Geometry-Aware Dynamic Convolution Beam-TasNet: Time-domain audio separation net- work meets frequency-domain beamformer,

Reference 18

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source=pdf_text observed=2026-08-01T14:47:15.438047Z digest=sha256:80427cd1681d2d0208ec4b6018ee13785654a58140b3d5f55a38339463957caa

Observation 9e9a9017-c9e1-4178-9e8c-ca7181aa7e4e · outbound

This paper cites Closing the gap between time-domain multi-channel speech enhancement on real and simulation conditions,.

Towards Array-Invariant Speech Enhancement via Geometry-Aware Dynamic Convolution Closing the gap between time-domain multi-channel speech enhancement on real and simulation conditions,

Reference 19

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source=pdf_text observed=2026-08-01T14:47:15.485664Z digest=sha256:7da970301f160727d1922ec8e5f5dce2f4cce160bcdc23745f757633a1c503c6

Observation 331ef254-5a55-4017-8117-a10402d06f1f · outbound

This paper cites NeRF: Representing scenes as neural radiance fields for view synthesis,.

Towards Array-Invariant Speech Enhancement via Geometry-Aware Dynamic Convolution NeRF: Representing scenes as neural radiance fields for view synthesis,

Reference 20

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source=pdf_text observed=2026-08-01T14:47:15.534881Z digest=sha256:df44c0edc9494ac460288840c9e7d193ecdc244227341f9dcc1732392af34a56

Observation 1a38d113-4b79-4837-b8d7-6aa3718ee59a · outbound

This paper cites Learning neural acoustic fields,.

Towards Array-Invariant Speech Enhancement via Geometry-Aware Dynamic Convolution Learning neural acoustic fields,

Reference 21

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source=pdf_text observed=2026-08-01T14:47:15.583084Z digest=sha256:0c7674a25fc41d83369e21770ee0366552cfd5cd46d47f60911d7448c5a29e1e

Observation c8299703-9838-4b1d-a987-bb6a06621bdd · outbound

This paper cites RealMAN: A real-recorded and anno- tated microphone array dataset for dynamic speech enhancement and localization,.

Towards Array-Invariant Speech Enhancement via Geometry-Aware Dynamic Convolution RealMAN: A real-recorded and anno- tated microphone array dataset for dynamic speech enhancement and localization,

Reference 22

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source=pdf_text observed=2026-08-01T14:47:15.630613Z digest=sha256:7915be84e9a6461f242852d10cd2afb157dbe37db231984d90bd814e39fa1e8e

Observation 977a3356-433a-417d-91bc-b68b2e2b5161 · outbound

This paper cites Music source separation with band-split RNN,.

Towards Array-Invariant Speech Enhancement via Geometry-Aware Dynamic Convolution Music source separation with band-split RNN,

Reference 23

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source=pdf_text observed=2026-08-01T14:47:15.652676Z digest=sha256:0b655f413b8ace893dade3e4297055e1686044974551d32d497a2e2bdd370482

Observation af068860-f786-4829-a3f4-2b8b79e39dd7 · outbound

This paper cites SpatialNet: Extensively learning spatial in- formation for multichannel joint speech separation, denoising and dereverberation,.

Towards Array-Invariant Speech Enhancement via Geometry-Aware Dynamic Convolution SpatialNet: Extensively learning spatial in- formation for multichannel joint speech separation, denoising and dereverberation,

Reference 24

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source=pdf_text observed=2026-08-01T14:47:15.752642Z digest=sha256:995f9b8eb1a779ce36493412b749f1800749c805f3b254787851e2665ec176a8

Observation 4c04689f-25dd-40f7-bdd7-cb111c83085d · outbound

This paper cites A Study of the Scale Invariant Signal to Distortion Ratio in Speech Separation with Noisy References.

Towards Array-Invariant Speech Enhancement via Geometry-Aware Dynamic Convolution A Study of the Scale Invariant Signal to Distortion Ratio in Speech Separation with Noisy References

Reference 26

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source=pdf_text observed=2026-08-01T14:47:15.959730Z digest=sha256:c96c7fd7bd7c43ffb57da7dc2e42fd66761a82aebef56e30177e2f20ac07a2e8

Observation 76cf895f-face-4ba1-9885-1ff2a040aa81 · outbound

This paper cites Perceptual eval- uation of speech quality (PESQ)-a new method for speech qual- ity assessment of telephone networks and codecs,.

Towards Array-Invariant Speech Enhancement via Geometry-Aware Dynamic Convolution Perceptual eval- uation of speech quality (PESQ)-a new method for speech qual- ity assessment of telephone networks and codecs,

Reference 27

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source=pdf_text observed=2026-08-01T14:47:16.007853Z digest=sha256:dbcd29255eab52d04f47cdc249e241a1b64dfc9a81c6407d06fcd3cd42137643

Observation 57113d2b-aaf9-4e63-b171-e60f8e85d21a · outbound

This paper cites A short- time objective intelligibility measure for time-frequency weighted noisy speech,.

Towards Array-Invariant Speech Enhancement via Geometry-Aware Dynamic Convolution A short- time objective intelligibility measure for time-frequency weighted noisy speech,

Reference 28

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T14:47:16.088061Z digest=sha256:f8e8c052406a71eb3552fcfcffd75100029b741a45bee81c9154b5e0f8b95013

Observation 253f46ee-39c4-4ef6-9434-9587487874d2 · outbound

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

Towards Array-Invariant Speech Enhancement via Geometry-Aware Dynamic Convolution DNSMOS P. 835: A non- intrusive perceptual objective speech quality metric to evaluate noise suppressors,

Reference 29

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source=pdf_text observed=2026-08-01T14:47:16.154364Z digest=sha256:4c31cb763856fcf7c93ff0d559215d3eb2295edab2a3d2287456c741937d6fa9

Observation e124e135-f9e1-4c42-9816-27b2d9e4b232 · outbound

This paper cites An analysis of environment, microphone and data simulation mismatches in robust speech recognition,.

Towards Array-Invariant Speech Enhancement via Geometry-Aware Dynamic Convolution An analysis of environment, microphone and data simulation mismatches in robust speech recognition,

Reference 30

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source=pdf_text observed=2026-08-01T14:47:16.214424Z digest=sha256:5f762c8457e7c4e9024e113b1029e6b3165272096e607fc7f2cafb60e5b5e349

Observation ee406369-01f1-4329-a644-908167f022c2 · outbound

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

Towards Array-Invariant Speech Enhancement via Geometry-Aware Dynamic Convolution SenSE: Semantic-Aware High-Fidelity Universal Speech Enhancement

Reference 2025

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source=pdf_text observed=2026-08-01T14:47:15.050474Z digest=sha256:7c3f234af47a1567da892a7a77e8e70be7fe5dde99f21b6c6faf7f4286be30bf

Pith citing papers

Observation 098ec478-e828-4094-927b-0a434f2d313c · inbound

Towards Array-Invariant Speech Enhancement via Geometry-Aware Dynamic Convolution cites this paper.

Towards Array-Invariant Speech Enhancement via Geometry-Aware Dynamic Convolution Towards Array-Invariant Speech Enhancement via Geometry-Aware Dynamic Convolution

Reference 1

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source=pdf_text observed=2026-08-01T14:47:14.238419Z digest=sha256:3cc11f8b43be45569d4ed815fe47b8f24e872641b69b873e2565b4df15fde5c3