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Paper Citation Record · LEDGER

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

As of 10 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 2026-08-09T06:31:02.800959+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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source=pdf_text observed=2026-08-01T14:47:14.238419Z digest=sha256:3d58f676bc9c8cac329670137e8235b679affbfb26fbaa25f6efd953622eaa54

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:ed3acd8c640b2a4e022ac0c2d3870f55011166c615f50109c068988bf885059d

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:b2921613a03214db304d583892d73cd154b89071dd68ce10e1557950f4a9bce9

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:ec1a61ac1b2fd6935da337b0736f0c460c67e24d9c51d6606b01f4c6702e04ee

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=sha256:8e753477030172a70e4f63fce6645f1208bf9780d939e0055142a42cfd012bdd

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=sha256:8ac2a63237052e82ce5b799c3f7950eb2fc49da1fe2663ac3b3c8abaad227ae0

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:d3c5fb79bc67985c699f26efe6a063cb0c0b49b29c1a48e60193751fd083d0f9

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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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T14:47:15.019958Z digest=sha256:fb8630d1b547c802edada447f7fe28033afbcec8ab8b8c5199baa5d644774c81

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:d07d580d1272574d41cdfce58acf074cf27b542a98a24a0f64bfb0629a2ed88f

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=sha256:25f1b7b200065d14110777f83dae1cea88258f71e4e91f67dbcc4f37a9408bff

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=sha256:332b71e2ebfd7ba92eb6a61ed50fc068a8215b4b30efd3bde421707eac4af091

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:ab8e84d72fb1504ee09c91b29faa0ed1aec18330823e2b948047d2cc7c17ec2a

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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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T14:47:15.211397Z digest=sha256:c0321a58952bd1957405c28be16b549c74e7c0191618f439865b3dbf271fdf8f

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=sha256:4aa73d91067ac58b4a95b92ba7432a44ec68d333cf7bd15eea209e07515576e8

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:ce0bc7aba44a2a01f47649338cf35a1c4c4c17cf63241c601f405168211099a6

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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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T14:47:15.359868Z digest=sha256:f273c40d600eb3673f1eb5773dd4319e2e7399287e3e38815f0219d7a3f878ed

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:efc9f1f607ce2c8cff4fb8301fd3e20e92676881dee037f18f4cef5263ab0920

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:a5cd946884185851e7fd4f4945afe82e84f630f62ba2cc54bf7b7ff240e021fa

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:f62476832996484d60c9b552ed1132b25dda940b2a1a6d77aeb74ce174e438f8

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=sha256:52989ff7a821f999128fdb3d48b3b2b4cd9f4ee86f4e55babce8efda36f918fb

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=sha256:630047820a43d046ce624bfc278652dfb21f6318df86edf51b0de446d4fc9f96

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:c97e65aa6c7f0711d733f1b2891ee39098f24bb31e688dfdcfa03010d187e4df

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:a40f132ec9c62c522fee6f9d97a53d356640afb910e5e64968d86652df60a031

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:e03be76a5e664eb2890f975f080ff43923ecaac6f4a845237f337dbb511c236d

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:e66ac67a7177230ca5e8ee979082bd149180a8082ed0d8934b3cb3efe6130dab

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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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T14:47:16.007853Z digest=sha256:a223a1770c31a24a4c0c34d15a2a2d90398d4901d8406d8330c0e9aab9a803c8

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=sha256:28a6ade5732bb569503e9e7861677a970a9e79d8b7b2d2a9c6a45070edaa0731

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=sha256:2ba7445ffb9d032dea75ff40f1a8ce0315ef8c2b3cbdc1710f06679139f5e149

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=sha256:6de84848d9115384cd44b37a4fe80ec640eba978257174563de16e81fe7c1ae4

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:ba82e46a139289a62f44c07d61c0344cf092cadc21b2fbfbb2c969a6a02bce79

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=sha256:3d58f676bc9c8cac329670137e8235b679affbfb26fbaa25f6efd953622eaa54