Paper Citation Record · LEDGER

Lightweight DNN for Full-Band Speech Denoising on Mobile Devices: Exploiting Long and Short Temporal Patterns

As of 14 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 0 inbound Pith citation observations for arXiv:2509.05079.

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

pith.paper-citation-record.v1
2509.05079 v1

Coverage vector

measured 26 of 26 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T05:40:19.602723Z

measured 26 of 26 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+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

26 of 26 outbound references displayed

verified exact0
verified fuzzy22
unresolved4
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e5300c6e-4449-42a4-8c34-f7beff425c9a · outbound

This paper cites SEGAN: Speech Enhancement Generative Adversarial Network.

Lightweight DNN for Full-Band Speech Denoising on Mobile Devices: Exploiting Long and Short Temporal Patterns SEGAN: Speech Enhancement Generative Adversarial Network

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-05T05:40:17.349733Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b8d20299-2706-4e5a-8734-450ba73ae59c · outbound

This paper cites Deepfil- ternet: A low complexity speech enhancement framework for full-band audio based on deep filtering,.

Lightweight DNN for Full-Band Speech Denoising on Mobile Devices: Exploiting Long and Short Temporal Patterns Deepfil- ternet: A low complexity speech enhancement framework for full-band audio based on deep filtering,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:40:23.569599Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 814909be-12c9-4b7a-966a-7e48a743f53d · outbound

This paper cites Conv-TasNet: Surpassing Ideal Time–Frequency Magnitude Masking for Speech Separation,.

Lightweight DNN for Full-Band Speech Denoising on Mobile Devices: Exploiting Long and Short Temporal Patterns Conv-TasNet: Surpassing Ideal Time–Frequency Magnitude Masking for Speech Separation,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:40:23.408035Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 96bb146a-c275-4b84-95a1-35e7b58b9faa · outbound

This paper cites Ks-net: Multi-band joint speech restoration and enhance- ment network for 2024 icassp ssi challenge,.

Lightweight DNN for Full-Band Speech Denoising on Mobile Devices: Exploiting Long and Short Temporal Patterns Ks-net: Multi-band joint speech restoration and enhance- ment network for 2024 icassp ssi challenge,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:40:23.180578Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation be81f7ff-e968-418d-ad05-8041fda6aa01 · outbound

This paper cites Immersive voice and audio services (ivas) codec-the new 3gpp standard for immersive communication,.

Lightweight DNN for Full-Band Speech Denoising on Mobile Devices: Exploiting Long and Short Temporal Patterns Immersive voice and audio services (ivas) codec-the new 3gpp standard for immersive communication,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:40:22.991955Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 73f1e997-447e-41d6-931c-082b6169c72d · outbound

This paper cites Real- time denoising and dereverberation wtih tiny recurrent u-net,.

Lightweight DNN for Full-Band Speech Denoising on Mobile Devices: Exploiting Long and Short Temporal Patterns Real- time denoising and dereverberation wtih tiny recurrent u-net,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:40:22.777430Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 0aef914a-c7e8-4901-962d-92e3915f6900 · outbound

This paper cites Ultra low complexity deep learning based noise suppression,.

Lightweight DNN for Full-Band Speech Denoising on Mobile Devices: Exploiting Long and Short Temporal Patterns Ultra low complexity deep learning based noise suppression,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:40:22.573531Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation c914d6cc-5843-4310-bb03-c9eeeb31f110 · outbound

This paper cites Gtcrn: A speech enhancement model requiring ultralow computational resources,.

Lightweight DNN for Full-Band Speech Denoising on Mobile Devices: Exploiting Long and Short Temporal Patterns Gtcrn: A speech enhancement model requiring ultralow computational resources,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:40:22.391263Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 857ed560-10c3-405c-b81b-9600d2b2a54c · outbound

This paper cites U-Net: Convolutional Net- works for Biomedical Image Segmentation,.

Lightweight DNN for Full-Band Speech Denoising on Mobile Devices: Exploiting Long and Short Temporal Patterns U-Net: Convolutional Net- works for Biomedical Image Segmentation,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:40:22.232350Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 3bc18016-4dcd-49b1-8788-95bb47c023d9 · outbound

This paper cites A hybrid dsp/deep learning approach to real-time full-band speech enhancement,.

Lightweight DNN for Full-Band Speech Denoising on Mobile Devices: Exploiting Long and Short Temporal Patterns A hybrid dsp/deep learning approach to real-time full-band speech enhancement,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:40:22.024623Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 07462ba1-4ebe-438f-9040-b58d77453946 · outbound

This paper cites A perceptually-motivated approach for low-complexity, real-time enhancement of fullband speech,.

Lightweight DNN for Full-Band Speech Denoising on Mobile Devices: Exploiting Long and Short Temporal Patterns A perceptually-motivated approach for low-complexity, real-time enhancement of fullband speech,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:40:21.832917Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 58b63eeb-1dc4-48db-94e4-c9f8d62eb76c · outbound

This paper cites MobileNetV2: Inverted Residuals and Linear Bot- tlenecks,.

Lightweight DNN for Full-Band Speech Denoising on Mobile Devices: Exploiting Long and Short Temporal Patterns MobileNetV2: Inverted Residuals and Linear Bot- tlenecks,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:40:21.678797Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f36ffa26-3658-4251-8843-359b0789c4a8 · outbound

This paper cites ICASSP 2023 Deep Noise Suppression Challenge,.

Lightweight DNN for Full-Band Speech Denoising on Mobile Devices: Exploiting Long and Short Temporal Patterns ICASSP 2023 Deep Noise Suppression Challenge,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:40:21.494425Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation cbc8cfd9-4d03-44aa-8f02-beaabb4a3ae0 · outbound

This paper cites CSTR VCTK Corpus: English Multi-speaker Corpus for CSTR V oice Cloning Toolkit,.

Lightweight DNN for Full-Band Speech Denoising on Mobile Devices: Exploiting Long and Short Temporal Patterns CSTR VCTK Corpus: English Multi-speaker Corpus for CSTR V oice Cloning Toolkit,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:40:21.347361Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 7ea8fc17-b881-4ed7-be17-82008147916b · outbound

This paper cites Investi- gating rnn-based speech enhancement methods for noise-robust text-to- speech,.

Lightweight DNN for Full-Band Speech Denoising on Mobile Devices: Exploiting Long and Short Temporal Patterns Investi- gating rnn-based speech enhancement methods for noise-robust text-to- speech,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:40:21.158114Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 30afd9a5-7ccf-4887-a786-6c3367ca7099 · outbound

This paper cites DNSMOS: A Non-Intrusive Perceptual Objective Speech Quality metric to evaluate Noise Suppres- sors,.

Lightweight DNN for Full-Band Speech Denoising on Mobile Devices: Exploiting Long and Short Temporal Patterns DNSMOS: A Non-Intrusive Perceptual Objective Speech Quality metric to evaluate Noise Suppres- sors,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:40:21.009481Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 87bec5a0-a056-4dba-9375-7addc5285fbf · outbound

This paper cites Searching for MobileNetV3,.

Lightweight DNN for Full-Band Speech Denoising on Mobile Devices: Exploiting Long and Short Temporal Patterns Searching for MobileNetV3,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:40:20.866281Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 3d1770dd-6293-4fbf-be7c-c08b99a448cc · outbound

This paper cites On the properties of neural machine translation: Encoder–decoder approaches,.

Lightweight DNN for Full-Band Speech Denoising on Mobile Devices: Exploiting Long and Short Temporal Patterns On the properties of neural machine translation: Encoder–decoder approaches,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:40:20.668407Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ca1f2130-a566-499f-af55-d15cfa4bcbfa · outbound

This paper cites Adam: A method for stochastic optimization,.

Lightweight DNN for Full-Band Speech Denoising on Mobile Devices: Exploiting Long and Short Temporal Patterns Adam: A method for stochastic optimization,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-05T05:40:18.691793Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dae7637b-2eb1-49b2-9ee7-573b28511b34 · outbound

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

Lightweight DNN for Full-Band Speech Denoising on Mobile Devices: Exploiting Long and Short Temporal Patterns SDR – Half- baked or Well Done?

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:40:20.450684Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 9857e6b2-b138-4bad-8743-3a28793f332b · outbound

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

Lightweight DNN for Full-Band Speech Denoising on Mobile Devices: Exploiting Long and Short Temporal Patterns A short- time objective intelligibility measure for time-frequency weighted noisy speech,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:40:20.279984Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 42abee67-75aa-47ae-89d6-50e05234846b · outbound

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

Lightweight DNN for Full-Band Speech Denoising on Mobile Devices: Exploiting Long and Short Temporal Patterns Perceptual evaluation of speech quality (PESQ)-a new method for speech quality assessment of telephone networks and codecs,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:40:20.115883Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 1eef7e75-2568-4f2c-8e33-8149d5cbb9f2 · outbound

This paper cites Deepfilternet: Perceptually motivated real-time speech enhancement,.

Lightweight DNN for Full-Band Speech Denoising on Mobile Devices: Exploiting Long and Short Temporal Patterns Deepfilternet: Perceptually motivated real-time speech enhancement,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:40:19.926207Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation d9aeb510-e1cb-41b6-9e20-e2dc9f66b908 · outbound

This paper cites Deepfil- ternet2: Towards Real-Time Speech Enhancement on Embedded Devices for Full-Band Audio,.

Lightweight DNN for Full-Band Speech Denoising on Mobile Devices: Exploiting Long and Short Temporal Patterns Deepfil- ternet2: Towards Real-Time Speech Enhancement on Embedded Devices for Full-Band Audio,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:40:19.788397Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 3f5a0123-5964-442a-bbe6-e0bab9d2475d · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Lightweight DNN for Full-Band Speech Denoising on Mobile Devices: Exploiting Long and Short Temporal Patterns Adam: A Method for Stochastic Optimization

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-05T05:40:18.768360Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 93f595f9-2e83-43bb-b334-24ffcd62f62e · outbound

This paper cites DeepFilterNet: Perceptually Motivated Real-Time Speech Enhancement.

Lightweight DNN for Full-Band Speech Denoising on Mobile Devices: Exploiting Long and Short Temporal Patterns DeepFilterNet: Perceptually Motivated Real-Time Speech Enhancement

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-05T05:40:19.470357Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.