Paper Citation Record · LEDGER

SepPrune: Structured Pruning for Efficient Deep Speech Separation

As of 22 August 2026, this Paper Citation Record lists 69 of 69 outbound references and 7 inbound Pith citation observations for arXiv:2505.12079.

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

pith.paper-citation-record.v1
2505.12079 v1

Coverage vector

measured 69 of 69 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:46:07.714227Z

measured 76 of 76 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 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T17:09:21.790789Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-15T20:06:34.051162Z

Reference resolution

69 of 69 outbound references displayed

verified exact1
verified fuzzy31
unresolved37
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 05ae9eb7-fd9d-4396-a937-61b54a1a8156 · outbound

This paper cites GPT-4 Technical Report.

SepPrune: Structured Pruning for Efficient Deep Speech Separation GPT-4 Technical Report

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-15T20:46:07.465831Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0ec64aa4-c400-4902-8d1f-523edd053e8c · outbound

This paper cites Deep audio- visual speech recognition.IEEE transactions on pattern analysis and machine intelligence, 44(12):8717– 8727, 2018.

SepPrune: Structured Pruning for Efficient Deep Speech Separation Deep audio- visual speech recognition.IEEE transactions on pattern analysis and machine intelligence, 44(12):8717– 8727, 2018

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:46:08.563201Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 8b812589-5ab7-4b51-8068-2c530ec9171a · outbound

This paper cites Dual Lottery Ticket Hypothesis.

SepPrune: Structured Pruning for Efficient Deep Speech Separation Dual Lottery Ticket Hypothesis

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-15T20:46:07.474478Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5a0556f3-4533-48c7-a11c-96435f5582fd · outbound

This paper cites Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation.

SepPrune: Structured Pruning for Efficient Deep Speech Separation Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-15T20:46:07.478828Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 38e106ef-3cc0-44cc-b648-7a535517d8c6 · outbound

This paper cites Embedded auditory system for small mobile robots.

SepPrune: Structured Pruning for Efficient Deep Speech Separation Embedded auditory system for small mobile robots

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:46:08.553176Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 0440f3b7-e96e-498e-9a2e-550548eb2af6 · outbound

This paper cites Matterport3D: Learning from RGB-D Data in Indoor Environments.

SepPrune: Structured Pruning for Efficient Deep Speech Separation Matterport3D: Learning from RGB-D Data in Indoor Environments

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-15T20:46:07.485766Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 93d3e057-d931-43be-af83-dd4fe78412e7 · outbound

This paper cites Soundspaces 2.0: A simulation platform for visual-acoustic learning.Advances in Neural Information Processing Systems, 35:8896–8911, 2022.

SepPrune: Structured Pruning for Efficient Deep Speech Separation Soundspaces 2.0: A simulation platform for visual-acoustic learning.Advances in Neural Information Processing Systems, 35:8896–8911, 2022

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:46:08.543042Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 001ce124-85f3-4a58-a516-621f57fbcb9c · outbound

This paper cites Dual-Path Transformer Network: Direct Context-Aware Modeling for End-to-End Monaural Speech Separation.

SepPrune: Structured Pruning for Efficient Deep Speech Separation Dual-Path Transformer Network: Direct Context-Aware Modeling for End-to-End Monaural Speech Separation

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-15T20:46:07.493057Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ee5ae55d-a4f5-49e3-bda9-f8d0d8500c65 · outbound

This paper cites Shallowing deep networks: Layer-wise pruning based on feature representations.

SepPrune: Structured Pruning for Efficient Deep Speech Separation Shallowing deep networks: Layer-wise pruning based on feature representations

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-15T20:46:07.496876Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 371cae86-21de-4046-b041-ca12e123f491 · outbound

This paper cites Rgp: Neural network pruning through regular graph with edges swapping.IEEE Transactions on Neural Networks and Learning Systems, 2023.

SepPrune: Structured Pruning for Efficient Deep Speech Separation Rgp: Neural network pruning through regular graph with edges swapping.IEEE Transactions on Neural Networks and Learning Systems, 2023

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:46:08.526078Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 28cd140d-fb11-42d0-9430-6f8f5f4c2bbc · outbound

This paper cites LibriMix: An Open-Source Dataset for Generalizable Speech Separation.

SepPrune: Structured Pruning for Efficient Deep Speech Separation LibriMix: An Open-Source Dataset for Generalizable Speech Separation

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-15T20:46:07.504201Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7ba479ec-6afc-4ee5-a4b8-8f61ac87e924 · outbound

This paper cites Do you hear what i hear? fingerprinting smart devices through embedded acoustic components.

SepPrune: Structured Pruning for Efficient Deep Speech Separation Do you hear what i hear? fingerprinting smart devices through embedded acoustic components

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:46:08.514917Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 16861a3d-b121-421b-ac3d-32ae5051a97b · outbound

This paper cites TinyFusion: Diffusion Transformers Learned Shallow.

SepPrune: Structured Pruning for Efficient Deep Speech Separation TinyFusion: Diffusion Transformers Learned Shallow

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-15T20:46:07.513494Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7e5591ce-c7e3-4af3-ba51-b2e441d914a4 · outbound

This paper cites MaskLLM: Learnable Semi-Structured Sparsity for Large Language Models.

SepPrune: Structured Pruning for Efficient Deep Speech Separation MaskLLM: Learnable Semi-Structured Sparsity for Large Language Models

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-15T20:46:07.517212Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2c849afb-c180-4bca-abd4-4c7a489bbe2a · outbound

This paper cites The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks.

SepPrune: Structured Pruning for Efficient Deep Speech Separation The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-15T20:46:07.520816Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dd4ac9a4-b423-47c6-9bf9-f7ff34998a99 · outbound

This paper cites Bilevelpruning: unified dynamic and static channel pruning for convolutional neural networks.

SepPrune: Structured Pruning for Efficient Deep Speech Separation Bilevelpruning: unified dynamic and static channel pruning for convolutional neural networks

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:46:08.504788Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 71d1348b-1768-4583-b895-2d08cd7f16cf · outbound

This paper cites US Government Printing Office, 1954.

SepPrune: Structured Pruning for Efficient Deep Speech Separation US Government Printing Office, 1954

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-15T20:46:07.528731Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 92f27ad0-7436-41fe-b173-371d6d81fb8f · outbound

This paper cites Dmcp: Differentiable markov channel pruning for neural networks.

SepPrune: Structured Pruning for Efficient Deep Speech Separation Dmcp: Differentiable markov channel pruning for neural networks

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:46:08.487662Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation be262665-6408-4d1a-a5e9-60ab631b76fe · outbound

This paper cites Eie: Efficient inference engine on compressed deep neural network.ACM SIGARCH Computer Architecture News, 44(3):243–254, 2016.

SepPrune: Structured Pruning for Efficient Deep Speech Separation Eie: Efficient inference engine on compressed deep neural network.ACM SIGARCH Computer Architecture News, 44(3):243–254, 2016

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:46:08.477371Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 09399e93-79cb-418a-aea6-ad960d2f491f · outbound

This paper cites Deep residual learning for image recognition.

SepPrune: Structured Pruning for Efficient Deep Speech Separation Deep residual learning for image recognition

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-15T20:46:07.537918Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4ede7b18-ed21-4feb-83a7-896fe627d9fd · outbound

This paper cites Channel pruning for accelerating very deep neural networks.

SepPrune: Structured Pruning for Efficient Deep Speech Separation Channel pruning for accelerating very deep neural networks

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-15T20:46:07.542148Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3f438afa-1017-4a4d-8bcd-a4b06b7af96e · outbound

This paper cites Revisiting pruning at initialization through the lens of ramanujan graph.

SepPrune: Structured Pruning for Efficient Deep Speech Separation Revisiting pruning at initialization through the lens of ramanujan graph

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-15T20:46:07.545498Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aefad73b-3f87-4a83-83f2-537f8cbf6527 · outbound

This paper cites Speech separation using an asynchronous fully recurrent convolutional neural network.Advances in Neural Information Processing Systems, 34:22509–22522, 2021.

SepPrune: Structured Pruning for Efficient Deep Speech Separation Speech separation using an asynchronous fully recurrent convolutional neural network.Advances in Neural Information Processing Systems, 34:22509–22522, 2021

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:46:08.447644Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation bfb2697e-8525-438f-8364-ee1689777de2 · outbound

This paper cites Categorical Reparameterization with Gumbel-Softmax.

SepPrune: Structured Pruning for Efficient Deep Speech Separation Categorical Reparameterization with Gumbel-Softmax

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-15T20:46:07.554689Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2e52cef6-2808-4ba1-8d2d-3589d8840504 · outbound

This paper cites Dual-path mamba: Short and long-term bidirectional selective structured state space models for speech separation.

SepPrune: Structured Pruning for Efficient Deep Speech Separation Dual-path mamba: Short and long-term bidirectional selective structured state space models for speech separation

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:46:08.436013Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 25a12461-f702-4ef4-b39a-bc87e751b2da · outbound

This paper cites Adam: A Method for Stochastic Optimization.

SepPrune: Structured Pruning for Efficient Deep Speech Separation Adam: A Method for Stochastic Optimization

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-15T20:46:07.562032Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 17169b09-a040-4e27-8fec-d3f77dcd4894 · outbound

This paper cites Sdr–half-baked or well done? In ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 626–630.

SepPrune: Structured Pruning for Efficient Deep Speech Separation Sdr–half-baked or well done? In ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 626–630

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:46:08.425726Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b6a2d904-6529-4e60-872e-843286c030b9 · outbound

This paper cites Pruning Filters for Efficient ConvNets.

SepPrune: Structured Pruning for Efficient Deep Speech Separation Pruning Filters for Efficient ConvNets

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-15T20:46:07.569269Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9c2bb386-686f-4069-af4f-92e95e37a867 · outbound

This paper cites SPMamba: State-space model is all you need in speech separation.

SepPrune: Structured Pruning for Efficient Deep Speech Separation SPMamba: State-space model is all you need in speech separation

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-15T20:46:07.573130Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b3d5b98c-f892-4391-8a08-0764985bf57f · outbound

This paper cites Subnetwork-to-go: Elastic neural network with dynamic training and customizable in- ference.

SepPrune: Structured Pruning for Efficient Deep Speech Separation Subnetwork-to-go: Elastic neural network with dynamic training and customizable in- ference

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:46:08.415954Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 469b797e-4aa6-4fb6-afae-ba9802d3743e · outbound

This paper cites An efficient encoder-decoder architecture with top-down attention for speech separation.

SepPrune: Structured Pruning for Efficient Deep Speech Separation An efficient encoder-decoder architecture with top-down attention for speech separation

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-15T20:46:07.579756Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0f1b0376-8d02-4043-893a-2ff1fdc998d7 · outbound

This paper cites Sglp: A similarity guided fast layer partition pruning for compressing large deep models.arXiv preprint arXiv:2410.14720, 2024.

SepPrune: Structured Pruning for Efficient Deep Speech Separation Sglp: A similarity guided fast layer partition pruning for compressing large deep models.arXiv preprint arXiv:2410.14720, 2024

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-15T20:46:07.582806Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9993f964-b69a-42a3-993a-9a3482ec7cfb · outbound

This paper cites Hrank: Filter pruning using high-rank feature map.

SepPrune: Structured Pruning for Efficient Deep Speech Separation Hrank: Filter pruning using high-rank feature map

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:46:08.404899Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 90b80ac3-905f-465b-a2b2-974e37cf1467 · outbound

This paper cites Channel Pruning via Automatic Structure Search.

SepPrune: Structured Pruning for Efficient Deep Speech Separation Channel Pruning via Automatic Structure Search

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-15T20:46:07.589854Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ef7ec38a-6afe-4572-bbb4-54f022c4a466 · outbound

This paper cites Slimgpt: Layer-wise structured pruning for large language models.Advances in Neural Information Processing Systems, 37:107112–107137, 2024.

SepPrune: Structured Pruning for Efficient Deep Speech Separation Slimgpt: Layer-wise structured pruning for large language models.Advances in Neural Information Processing Systems, 37:107112–107137, 2024

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:46:08.393903Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 5f630cf9-0069-4010-871f-3fb571f08327 · outbound

This paper cites The Unreasonable Effectiveness of Random Pruning: Return of the Most Naive Baseline for Sparse Training.

SepPrune: Structured Pruning for Efficient Deep Speech Separation The Unreasonable Effectiveness of Random Pruning: Return of the Most Naive Baseline for Sparse Training

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-15T20:46:07.597290Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8462a64d-a85e-4ae8-a134-d4411692cc80 · outbound

This paper cites Swin transformer: Hierarchical vision transformer using shifted windows.

SepPrune: Structured Pruning for Efficient Deep Speech Separation Swin transformer: Hierarchical vision transformer using shifted windows

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-15T20:46:07.600806Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f6448a14-2731-45aa-82b4-1bbeca766b30 · outbound

This paper cites Metapruning: Meta learning for automatic neural network channel pruning.

SepPrune: Structured Pruning for Efficient Deep Speech Separation Metapruning: Meta learning for automatic neural network channel pruning

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:46:08.373517Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 8839922d-e68a-41dc-98a3-683ed70b5b0d · outbound

This paper cites Reassessing Layer Pruning in LLMs: New Insights and Methods.

SepPrune: Structured Pruning for Efficient Deep Speech Separation Reassessing Layer Pruning in LLMs: New Insights and Methods

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-15T20:46:07.607945Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 21913e76-86bf-46f6-ad62-c9abee63789a · outbound

This paper cites Understanding the dynamics of dnns using graph modularity.

SepPrune: Structured Pruning for Efficient Deep Speech Separation Understanding the dynamics of dnns using graph modularity

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:46:08.359972Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 485fcc01-fc0b-4f2a-8329-5e5ecdc18669 · outbound

This paper cites A generic layer pruning method for signal modulation recognition deep learning models.IEEE Transactions on Cognitive Communications and Networking, 2024.

SepPrune: Structured Pruning for Efficient Deep Speech Separation A generic layer pruning method for signal modulation recognition deep learning models.IEEE Transactions on Cognitive Communications and Networking, 2024

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:46:08.348782Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 4b8f31a1-dd4e-4c8d-b25a-76c0efdf5463 · outbound

This paper cites Conv-tasnet: Surpassing ideal time–frequency magnitude masking for speech separation.IEEE/ACM transactions on audio, speech, and language processing, 27(8):1256–1266, 2019.

SepPrune: Structured Pruning for Efficient Deep Speech Separation Conv-tasnet: Surpassing ideal time–frequency magnitude masking for speech separation.IEEE/ACM transactions on audio, speech, and language processing, 27(8):1256–1266, 2019

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:46:08.338509Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b7a4a1ce-2dfb-4953-bdd6-daebaccee506 · outbound

This paper cites Music source separation with band-split rnn.IEEE/ACM Transactions on Audio, Speech, and Language Processing, 31:1893–1901, 2023.

SepPrune: Structured Pruning for Efficient Deep Speech Separation Music source separation with band-split rnn.IEEE/ACM Transactions on Audio, Speech, and Language Processing, 31:1893–1901, 2023

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:46:08.328847Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation eefb74f9-a1c1-4ea1-ae5a-d0f545be5f96 · outbound

This paper cites Llm-pruner: On the structural pruning of large language models.Advances in neural information processing systems, 36:21702–21720, 2023.

SepPrune: Structured Pruning for Efficient Deep Speech Separation Llm-pruner: On the structural pruning of large language models.Advances in neural information processing systems, 36:21702–21720, 2023

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-15T20:46:07.625305Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 215e3a45-4789-43f3-9525-93003bf70352 · outbound

This paper cites Mobile communication devices, ambient noise, and acoustic voice measures.Journal of V oice, 31(2):248–e11, 2017.

SepPrune: Structured Pruning for Efficient Deep Speech Separation Mobile communication devices, ambient noise, and acoustic voice measures.Journal of V oice, 31(2):248–e11, 2017

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:46:08.312683Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation be5e79a5-b91b-437e-9a34-ad83aa1ebfe0 · outbound

This paper cites Librispeech: an asr corpus based on public domain audio books.

SepPrune: Structured Pruning for Efficient Deep Speech Separation Librispeech: an asr corpus based on public domain audio books

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-15T20:46:07.631919Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f8e65e2c-ca42-4267-8c14-3351abd96d79 · outbound

This paper cites Faster CNNs with Direct Sparse Convolutions and Guided Pruning.

SepPrune: Structured Pruning for Efficient Deep Speech Separation Faster CNNs with Direct Sparse Convolutions and Guided Pruning

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-15T20:46:07.635050Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 011195d4-f48b-48a7-8785-608ca1fd5da3 · outbound

This paper cites Reasoning to attend: Try to understand how< seg> token works.arXiv preprint arXiv:2412.17741, 2024.

SepPrune: Structured Pruning for Efficient Deep Speech Separation Reasoning to attend: Try to understand how< seg> token works.arXiv preprint arXiv:2412.17741, 2024

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-15T20:46:07.637909Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d253fa0f-384b-492b-84a5-96247960ce5e · outbound

This paper cites Algorithms to measure audio programme loudness and true-peak audio level.International Telecommunication Union Radiocommunication Assembly, 2011.

SepPrune: Structured Pruning for Efficient Deep Speech Separation Algorithms to measure audio programme loudness and true-peak audio level.International Telecommunication Union Radiocommunication Assembly, 2011

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:46:08.295519Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 75a6d1bd-76b8-46b1-9ec5-403abe238afa · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

SepPrune: Structured Pruning for Efficient Deep Speech Separation Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-15T20:46:07.645013Z

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Unavailable: canonical work link unavailable.

Observation ef12517a-fed7-4830-9c2b-e4188f88ec09 · outbound

This paper cites Attention is all you need in speech separation.

SepPrune: Structured Pruning for Efficient Deep Speech Separation Attention is all you need in speech separation

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-15T20:46:07.649510Z

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Observation 06cc49d7-b2c1-4f80-bfc7-6268cf53c4fd · outbound

This paper cites Real-m: Towards speech separation on real mixtures.

SepPrune: Structured Pruning for Efficient Deep Speech Separation Real-m: Towards speech separation on real mixtures

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:46:08.279007Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation a7aa930d-46f5-4db3-ab66-3bd3b9608a2e · outbound

This paper cites A Simple and Effective Pruning Approach for Large Language Models.

SepPrune: Structured Pruning for Efficient Deep Speech Separation A Simple and Effective Pruning Approach for Large Language Models

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-15T20:46:07.656474Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation db8e9114-5064-4012-a27e-b4f5bffb581d · outbound

This paper cites Sr-init: An interpretable layer pruning method.

SepPrune: Structured Pruning for Efficient Deep Speech Separation Sr-init: An interpretable layer pruning method

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:46:08.269309Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation ab4aa5e1-260d-4617-be5a-35dc9cbf32f8 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

SepPrune: Structured Pruning for Efficient Deep Speech Separation LLaMA: Open and Efficient Foundation Language Models

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-15T20:46:07.664125Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 691e1e62-8177-4197-8b3c-bb2e18aec0bf · outbound

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

SepPrune: Structured Pruning for Efficient Deep Speech Separation Sudo rm-rf: Efficient networks for universal audio source separation

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:46:08.259357Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 6f5b51db-a5e4-46cc-82b8-85d87b1b20b8 · outbound

This paper cites Performance measurement in blind audio source separation.IEEE transactions on audio, speech, and language processing, 14(4):1462–1469, 2006.

SepPrune: Structured Pruning for Efficient Deep Speech Separation Performance measurement in blind audio source separation.IEEE transactions on audio, speech, and language processing, 14(4):1462–1469, 2006

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:46:08.250599Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 1b89d182-d351-4226-ad24-0f1d8f5b27fc · outbound

This paper cites RL-Pruner: Structured Pruning Using Reinforcement Learning for CNN Compression and Acceleration.

SepPrune: Structured Pruning for Efficient Deep Speech Separation RL-Pruner: Structured Pruning Using Reinforcement Learning for CNN Compression and Acceleration

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-15T20:46:07.675172Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7d5d6a51-dd71-4f33-9ef1-b32960e0bbb6 · outbound

This paper cites Recent Advances on Neural Network Pruning at Initialization.

SepPrune: Structured Pruning for Efficient Deep Speech Separation Recent Advances on Neural Network Pruning at Initialization

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-15T20:46:07.679703Z

Source-reported events for the cited work

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Observation defc568e-ae9a-4aa7-a219-2d15aebcd4d3 · outbound

This paper cites NTK-SAP: Improving neural network pruning by aligning training dynamics.

SepPrune: Structured Pruning for Efficient Deep Speech Separation NTK-SAP: Improving neural network pruning by aligning training dynamics

Reference 60

Resolution: verified exact; local_arxiv, observed 2026-08-15T20:46:07.835429Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation bc87bd91-ab41-45e8-9cf5-86944fdae49f · outbound

This paper cites Tf-gridnet: Making time-frequency domain models great again for monaural speaker separation.

SepPrune: Structured Pruning for Efficient Deep Speech Separation Tf-gridnet: Making time-frequency domain models great again for monaural speaker separation

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:46:08.240981Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 372610fc-5cb3-47b5-9f2b-bcce038e9924 · outbound

This paper cites WHAM!: Extending Speech Separation to Noisy Environments.

SepPrune: Structured Pruning for Efficient Deep Speech Separation WHAM!: Extending Speech Separation to Noisy Environments

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-15T20:46:07.689263Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4110da50-9a23-4c33-a50d-9e9ad81e2a24 · outbound

This paper cites Efficient layer compression without pruning.IEEE Transactions on Image Processing, 32:4689–4700, 2023.

SepPrune: Structured Pruning for Efficient Deep Speech Separation Efficient layer compression without pruning.IEEE Transactions on Image Processing, 32:4689–4700, 2023

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:46:08.229518Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 15ddf324-aca4-4735-8b32-c46840f55c72 · outbound

This paper cites Tiger: Time-frequency interleaved gain extraction and reconstruction for efficient speech separation.arXiv preprint arXiv:2410.01469, 2024.

SepPrune: Structured Pruning for Efficient Deep Speech Separation Tiger: Time-frequency interleaved gain extraction and reconstruction for efficient speech separation.arXiv preprint arXiv:2410.01469, 2024

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-15T20:46:07.696397Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c44e6d01-d5bd-40d2-8c92-1c843c97345b · outbound

This paper cites Tfpsnet: Time-frequency domain path scanning network for speech separation.

SepPrune: Structured Pruning for Efficient Deep Speech Separation Tfpsnet: Time-frequency domain path scanning network for speech separation

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:46:08.219168Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation dbb3e0a1-ccc6-4cff-8278-ae71b90eb5be · outbound

This paper cites Wanda++: Pruning Large Language Models via Regional Gradients.

SepPrune: Structured Pruning for Efficient Deep Speech Separation Wanda++: Pruning Large Language Models via Regional Gradients

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-15T20:46:07.703817Z

Source-reported events for the cited work

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Observation 07821254-2243-47e5-81f4-1d58bc1b1267 · outbound

This paper cites Wavesplit: End-to-end speech separation by speaker clustering.

SepPrune: Structured Pruning for Efficient Deep Speech Separation Wavesplit: End-to-end speech separation by speaker clustering

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:46:08.208376Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation c1fe0aa1-dbd6-4757-b11a-33f78d9d0ce3 · outbound

This paper cites Transmask: A compact and fast speech separation model based on transformer.

SepPrune: Structured Pruning for Efficient Deep Speech Separation Transmask: A compact and fast speech separation model based on transformer

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:46:08.197660Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 44a88e02-8518-4cf6-be5d-f9b3165c05ad · outbound

This paper cites Discrimination-aware channel pruning for deep neural networks.Advances in neural information processing systems, 31, 2018.

SepPrune: Structured Pruning for Efficient Deep Speech Separation Discrimination-aware channel pruning for deep neural networks.Advances in neural information processing systems, 31, 2018

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:46:08.187160Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Pith citing papers

Observation 3e6dc251-eea9-49d5-8281-32332f1a2b18 · inbound

ReStNet: A Reusable & Stitchable Network for Dynamic Adaptation on IoT Devices cites this paper.

ReStNet: A Reusable & Stitchable Network for Dynamic Adaptation on IoT Devices SepPrune: Structured Pruning for Efficient Deep Speech Separation

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T05:44:05.581815Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1004845b-7c3e-432e-9344-c944c2896e8b · inbound

Modular MeanFlow: Towards Stable and Scalable One-Step Generative Modeling cites this paper.

Modular MeanFlow: Towards Stable and Scalable One-Step Generative Modeling SepPrune: Structured Pruning for Efficient Deep Speech Separation

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-15T17:09:21.790789Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1d0c9116-0d51-443a-8b20-1abde923527f · inbound

Multi-Modal Machine Learning Framework for Predicting Early Recurrence of Brain Tumors Using MRI and Clinical Biomarkers cites this paper.

Multi-Modal Machine Learning Framework for Predicting Early Recurrence of Brain Tumors Using MRI and Clinical Biomarkers SepPrune: Structured Pruning for Efficient Deep Speech Separation

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-05T12:53:04.290527Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 84004124-ab35-437e-9df4-96728f49904d · inbound

A Multimodal Deep Learning Framework for Early Diagnosis of Liver Cancer via Optimized BiLSTM-AM-VMD Architecture cites this paper.

A Multimodal Deep Learning Framework for Early Diagnosis of Liver Cancer via Optimized BiLSTM-AM-VMD Architecture SepPrune: Structured Pruning for Efficient Deep Speech Separation

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-05T12:53:14.885841Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 67fbccff-3515-48f2-a62c-8b650db8c896 · inbound

Train Large, Deploy Compact: Structured Compression for Compact Low-Rank Adaptation cites this paper.

Train Large, Deploy Compact: Structured Compression for Compact Low-Rank Adaptation SepPrune: Structured Pruning for Efficient Deep Speech Separation

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-04T13:31:01.902086Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eaa293aa-7d36-439d-9914-5050ee41b255 · inbound

DP-FedAdamW: An Efficient Optimizer for Differentially Private Federated Large Models cites this paper.

DP-FedAdamW: An Efficient Optimizer for Differentially Private Federated Large Models SepPrune: Structured Pruning for Efficient Deep Speech Separation

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-05-15T20:06:34.055168Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 2f0d4bd9-5334-45a2-a885-147dd47d22ca · inbound

RAM: Recover Any 3D Human Motion in-the-Wild cites this paper.

RAM: Recover Any 3D Human Motion in-the-Wild SepPrune: Structured Pruning for Efficient Deep Speech Separation

Reference 49

Resolution: verified exact; arxiv_id, observed 2026-05-15T08:45:19.129030Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.