Paper Citation Record · LEDGER

FLASepformer: Efficient Speech Separation with Gated Focused Linear Attention Transformer

As of 16 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 1 inbound Pith citation observation for arXiv:2508.19528.

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

pith.paper-citation-record.v1
2508.19528 v1

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T16:54:58.364510Z

measured 40 of 40 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T16:54:58.198856Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-15T16:54:58.429613Z

Reference resolution

39 of 39 outbound references displayed

verified exact1
verified fuzzy32
unresolved6
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ea797d80-d26f-4d82-914f-33a1798655b0 · outbound

This paper cites an unresolved cited work.

FLASepformer: Efficient Speech Separation with Gated Focused Linear Attention Transformer Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-08-15T16:54:58.957311Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 12e02a36-ccc5-4eaf-a8a4-8935b858b96a · outbound

This paper cites FLASepformer: Efficient Speech Separation with Gated Focused Linear Attention Transformer.

FLASepformer: Efficient Speech Separation with Gated Focused Linear Attention Transformer FLASepformer: Efficient Speech Separation with Gated Focused Linear Attention Transformer

Reference 2

Resolution: verified exact; local_arxiv, observed 2026-08-15T16:54:58.436921Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 4cefdc36-616b-4ddc-9389-326458bc72d3 · outbound

This paper cites Dataset We validate our model’s performance using four popular speech separation datasets: WSJ0-2Mix [3], WHAM! [28], WHAMR! [29], and Libri2Mix [30].

FLASepformer: Efficient Speech Separation with Gated Focused Linear Attention Transformer Dataset We validate our model’s performance using four popular speech separation datasets: WSJ0-2Mix [3], WHAM! [28], WHAMR! [29], and Libri2Mix [30]

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:54:58.942593Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation a418bfc5-f90e-4351-8141-48cf8bd730d4 · outbound

This paper cites Comparison with previous models We use SI-SNR improvement (SI-SNRi) and SDR improve- ment (SDRi) [33] to evaluate model performance.

FLASepformer: Efficient Speech Separation with Gated Focused Linear Attention Transformer Comparison with previous models We use SI-SNR improvement (SI-SNRi) and SDR improve- ment (SDRi) [33] to evaluate model performance

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:54:58.927582Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 18982a38-3d91-44cd-8c3f-bedd5809382a · outbound

This paper cites Although previous meth- ods use STFT and downsampling to reduce speech sequence length, the attention module in those still has O(N 2) time complexity.

FLASepformer: Efficient Speech Separation with Gated Focused Linear Attention Transformer Although previous meth- ods use STFT and downsampling to reduce speech sequence length, the attention module in those still has O(N 2) time complexity

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:54:58.912027Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 4d2147b0-c582-4586-b718-ec14f38904b1 · outbound

This paper cites Some experiments on the recognition of speech, with one and with two ears,.

FLASepformer: Efficient Speech Separation with Gated Focused Linear Attention Transformer Some experiments on the recognition of speech, with one and with two ears,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:54:58.896074Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation ddf76e5c-3b96-4c8d-bb5e-3828714608bf · outbound

This paper cites an unresolved cited work.

FLASepformer: Efficient Speech Separation with Gated Focused Linear Attention Transformer Unresolved cited work

Reference 7

Resolution: unresolved; raw_fallback, observed 2026-08-15T16:54:58.880958Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b330cdec-5eed-4331-a68d-24d90eef39e3 · outbound

This paper cites Deep clus- tering: Discriminative embeddings for segmentation and separa- tion,.

FLASepformer: Efficient Speech Separation with Gated Focused Linear Attention Transformer Deep clus- tering: Discriminative embeddings for segmentation and separa- tion,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:54:58.866280Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 22ea9c41-9e1c-4b52-a553-9f14027f4920 · outbound

This paper cites Multitalker speech separation with utterance-level permutation invariant training of deep recurrent neural networks,.

FLASepformer: Efficient Speech Separation with Gated Focused Linear Attention Transformer Multitalker speech separation with utterance-level permutation invariant training of deep recurrent neural networks,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:54:58.850838Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 43b3e2a2-3248-44f9-a8ce-f43c77a05e99 · outbound

This paper cites Attention Is All You Need In Speech Separation,.

FLASepformer: Efficient Speech Separation with Gated Focused Linear Attention Transformer Attention Is All You Need In Speech Separation,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:54:58.834306Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation aecafd82-c8c9-4637-9e02-3eacf2eb4021 · outbound

This paper cites TF-GRIDNET: Making Time-Frequency Domain Models Great Again for Monaural Speaker Separation,.

FLASepformer: Efficient Speech Separation with Gated Focused Linear Attention Transformer TF-GRIDNET: Making Time-Frequency Domain Models Great Again for Monaural Speaker Separation,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:54:58.819780Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 528127be-dbbd-438d-8ce5-fef6a08500bb · outbound

This paper cites TF-Locoformer: Transformer with Local Modeling by Convo- lution for Speech Separation and Enhancement,.

FLASepformer: Efficient Speech Separation with Gated Focused Linear Attention Transformer TF-Locoformer: Transformer with Local Modeling by Convo- lution for Speech Separation and Enhancement,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:54:58.804947Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 5f427243-2999-4976-9673-a9a1d93a37c4 · outbound

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

FLASepformer: Efficient Speech Separation with Gated Focused Linear Attention Transformer Conv-tasnet: Surpassing ideal time– frequency magnitude masking for speech separation,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:54:58.790142Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 3d0488c6-864e-471d-aad9-0eae342379da · outbound

This paper cites Dual-Path RNN: Effi- cient Long Sequence Modeling for Time-Domain Single-Channel Speech Separation,.

FLASepformer: Efficient Speech Separation with Gated Focused Linear Attention Transformer Dual-Path RNN: Effi- cient Long Sequence Modeling for Time-Domain Single-Channel Speech Separation,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:54:58.775646Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b3ffafa9-a8b0-409c-9f4e-ff436faa18d8 · outbound

This paper cites Long Short-term Memory,.

FLASepformer: Efficient Speech Separation with Gated Focused Linear Attention Transformer Long Short-term Memory,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-15T16:54:58.259476Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ad8cbc50-fba6-4ad4-a4c5-42016dca1dcb · outbound

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

FLASepformer: Efficient Speech Separation with Gated Focused Linear Attention Transformer Dual-Path Transformer Net- work: Direct Context-Aware Modeling for End-to-End Monaural Speech Separation,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:54:58.751359Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 2b112eac-8883-41e9-ba7c-c98cf5c5a3cf · outbound

This paper cites Attention Is All You Need,.

FLASepformer: Efficient Speech Separation with Gated Focused Linear Attention Transformer Attention Is All You Need,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:54:58.737610Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 6a119972-82c4-4c93-aefe-ca524615d8b4 · outbound

This paper cites Sudo RM -RF: Effi- cient Networks for Universal Audio Source Separation,.

FLASepformer: Efficient Speech Separation with Gated Focused Linear Attention Transformer Sudo RM -RF: Effi- cient Networks for Universal Audio Source Separation,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:54:58.723433Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation a20c7ec9-fef7-4c78-afeb-0529fec98603 · outbound

This paper cites Sandglasset: A Light Multi-Granularity Self-Attentive Network for Time- Domain Speech Separation,.

FLASepformer: Efficient Speech Separation with Gated Focused Linear Attention Transformer Sandglasset: A Light Multi-Granularity Self-Attentive Network for Time- Domain Speech Separation,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:54:58.709829Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 40506afc-3fb9-41d5-bb5e-188ae755ebe5 · outbound

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

FLASepformer: Efficient Speech Separation with Gated Focused Linear Attention Transformer An efficient encoder-decoder archi- tecture with top-down attention for speech separation,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:54:58.696033Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation a0b061fb-0f5c-41c5-8113-3b1ffae07187 · outbound

This paper cites TIGER: Time-frequency In- terleaved Gain Extraction and Reconstruction for Efficient Speech Separation,.

FLASepformer: Efficient Speech Separation with Gated Focused Linear Attention Transformer TIGER: Time-frequency In- terleaved Gain Extraction and Reconstruction for Efficient Speech Separation,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:54:58.682253Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 47a5c179-95ae-4f24-a45b-9b5dfa35addc · outbound

This paper cites ZipEnhancer: Dual-Path Down-Up Sampling-based Zipformer for Monaural Speech Enhancement,.

FLASepformer: Efficient Speech Separation with Gated Focused Linear Attention Transformer ZipEnhancer: Dual-Path Down-Up Sampling-based Zipformer for Monaural Speech Enhancement,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:54:58.668088Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 021ef93d-9609-4899-80d2-a90e355dc113 · outbound

This paper cites MossFormer: Pushing the Performance Limit of Monaural Speech Separation Using Gated Single-Head Transformer with Convolution-Augmented Joint Self-Attentions,.

FLASepformer: Efficient Speech Separation with Gated Focused Linear Attention Transformer MossFormer: Pushing the Performance Limit of Monaural Speech Separation Using Gated Single-Head Transformer with Convolution-Augmented Joint Self-Attentions,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:54:58.653699Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation c9f02299-3837-4b44-941e-0ec5f2cafb5e · outbound

This paper cites MossFormer2: Combin- ing Transformer and RNN-Free Recurrent Network for Enhanced Time-Domain Monaural Speech Separation,.

FLASepformer: Efficient Speech Separation with Gated Focused Linear Attention Transformer MossFormer2: Combin- ing Transformer and RNN-Free Recurrent Network for Enhanced Time-Domain Monaural Speech Separation,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:54:58.639408Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 9a72620f-9b1c-4cda-8066-b4c5d7a8a205 · outbound

This paper cites Transformer Quality in Linear Time,.

FLASepformer: Efficient Speech Separation with Gated Focused Linear Attention Transformer Transformer Quality in Linear Time,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:54:58.625296Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b8f77adc-a200-4ea8-91bb-2d76088b8a5c · outbound

This paper cites Trans- formers are RNNs: Fast Autoregressive Transformers with Linear Attention,.

FLASepformer: Efficient Speech Separation with Gated Focused Linear Attention Transformer Trans- formers are RNNs: Fast Autoregressive Transformers with Linear Attention,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:54:58.610412Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 4d1b3817-a6d0-4997-82bb-9f8596be92c0 · outbound

This paper cites FLatten Trans- former: Vision Transformer using Focused Linear Attention,.

FLASepformer: Efficient Speech Separation with Gated Focused Linear Attention Transformer FLatten Trans- former: Vision Transformer using Focused Linear Attention,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:54:58.595946Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 02ef7c37-7747-46f1-b220-26dbde15d2ae · outbound

This paper cites Separate and Reconstruct: Asymmetric Encoder-Decoder for Speech Separation,.

FLASepformer: Efficient Speech Separation with Gated Focused Linear Attention Transformer Separate and Reconstruct: Asymmetric Encoder-Decoder for Speech Separation,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:54:58.582199Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 3d5369c3-f721-430a-8e46-87f92048504c · outbound

This paper cites EfficientViT: Lightweight Multi-Scale Attention for High-Resolution Dense Prediction,.

FLASepformer: Efficient Speech Separation with Gated Focused Linear Attention Transformer EfficientViT: Lightweight Multi-Scale Attention for High-Resolution Dense Prediction,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:54:58.567050Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 84f0e625-1e05-430f-8c1b-8e3f80cf51dc · outbound

This paper cites Rethink- ing Attention with Performers,.

FLASepformer: Efficient Speech Separation with Gated Focused Linear Attention Transformer Rethink- ing Attention with Performers,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:54:58.550957Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation bc822c87-c3c2-4ae3-8cc1-087e2e936a2d · outbound

This paper cites A Neural State-Space Modeling Approach to Efficient Speech Separation,.

FLASepformer: Efficient Speech Separation with Gated Focused Linear Attention Transformer A Neural State-Space Modeling Approach to Efficient Speech Separation,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:54:58.535785Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 329d0df9-0a19-4e1a-b777-719c675facc6 · outbound

This paper cites Speech Slytherin: Examining the Performance and Efficiency of Mamba for Speech Separation, Recognition, and Synthesis.

FLASepformer: Efficient Speech Separation with Gated Focused Linear Attention Transformer Speech Slytherin: Examining the Performance and Efficiency of Mamba for Speech Separation, Recognition, and Synthesis

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-15T16:54:58.333703Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5ce1d1de-2730-4ce9-84be-8cd86ccd0d7d · outbound

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

FLASepformer: Efficient Speech Separation with Gated Focused Linear Attention Transformer WHAM!: Extending Speech Separation to Noisy Environments,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:54:58.520759Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b36f8ee8-a4a5-4bca-a23c-dad1f5c0a84c · outbound

This paper cites WHAMR!: Noisy and Reverberant Single-Channel Speech Sep- aration,.

FLASepformer: Efficient Speech Separation with Gated Focused Linear Attention Transformer WHAMR!: Noisy and Reverberant Single-Channel Speech Sep- aration,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:54:58.507070Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation befb543d-3f02-4973-8908-cbba79a5d595 · outbound

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

FLASepformer: Efficient Speech Separation with Gated Focused Linear Attention Transformer LibriMix: An Open-Source Dataset for Generalizable Speech Separation,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:54:58.492699Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 8bd4d01e-91b0-415d-90e3-29d0acfd55de · outbound

This paper cites Lib- rispeech: An ASR corpus based on public domain audio books,.

FLASepformer: Efficient Speech Separation with Gated Focused Linear Attention Transformer Lib- rispeech: An ASR corpus based on public domain audio books,

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-15T16:54:58.350954Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 644b40f6-7e5a-4f95-b2e8-edca56eeeb70 · outbound

This paper cites ESPnet-SE++: Speech Enhancement for Robust Speech Recognition, Translation, and Understanding,.

FLASepformer: Efficient Speech Separation with Gated Focused Linear Attention Transformer ESPnet-SE++: Speech Enhancement for Robust Speech Recognition, Translation, and Understanding,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:54:58.468333Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation dda4ec6f-4397-45e8-aa41-c336bc277205 · outbound

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

FLASepformer: Efficient Speech Separation with Gated Focused Linear Attention Transformer SDR – Half-baked or Well Done?

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:54:58.452564Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation db3ac104-9294-442e-b71d-195739111adc · outbound

This paper cites Self-attention Does Not Need $O(n^2)$ Memory.

FLASepformer: Efficient Speech Separation with Gated Focused Linear Attention Transformer Self-attention Does Not Need $O(n^2)$ Memory

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-15T16:54:58.364510Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 12e02a36-ccc5-4eaf-a8a4-8935b858b96a · inbound