{"as_of":"2026-08-21T04:11:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:0318e0ad65d79571cbf9e33d8b16fdc162f74b772d22d2f1e7b7887fb54d708b","coverage":[{"denominator":26,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":26,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T20:23:17.365827Z","state":"measured"},{"denominator":27,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":27,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-20T06:33:59.587034+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-27T14:58:27.176375Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-03T03:37:36.071768Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2505.13094","last_updated":"2025-05-19T13:25:51Z","snapshot_observed_at":"2026-08-18T03:26:46.185382Z","submitted_at":"2025-05-19T13:25:51Z","title":"Time-Frequency-Based Attention Cache Memory Model for Real-Time Speech Separation","version":1},"cited_work":{"arxiv_id":"2505.13094","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2505.13094","snapshot_observed_at":"2026-07-03T03:37:36.071768Z","title":"Time-frequency- based attention cache memory model for real-time speech separation,","venue":null,"work_id":"8d909788-21b6-4960-be96-cfaf2da874a9","year":2025},"citing_paper":{"arxiv_id":"2606.10046","last_updated":"2026-06-10T16:28:45Z","snapshot_observed_at":"2026-08-05T07:13:46.298081Z","submitted_at":"2026-06-08T18:18:28Z","title":"Inside the Latent Flow: Causal Deciphering of Attention Dynamics in Audio Separation Foundation Models","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-06-27T14:58:27.176375Z"},"links":{"cited_paper":"/paper/2505.13094","citing_paper":"/paper/2606.10046"},"observation_digest":"sha256:4ce3741b7c45f7309bceba3147c039b0490289b1f4b636764c81391fe271567b","observation_id":"49e5eb20-ed5d-4c9b-8dc9-2bd05efb229d","resolution":{"observed_at":"2026-07-03T03:37:36.073117Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2505.13094/citation-record","integrity":"/paper/2505.13094/integrity","json":"/paper/2505.13094/citation-record.json","paper":"/paper/2505.13094"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:23:17.816348Z","title":"Tasnet: time-domain audio separation net- work for real-time, single-channel speech separation,","venue":null,"work_id":"f4571f5c-4a0e-4d27-bea7-2faeb96fe01d","year":2018},"citing_paper":{"arxiv_id":"2505.13094","last_updated":"2025-05-19T13:25:51Z","snapshot_observed_at":"2026-08-18T03:26:46.185382Z","submitted_at":"2025-05-19T13:25:51Z","title":"Time-Frequency-Based Attention Cache Memory Model for Real-Time Speech Separation","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-15T20:23:17.265659Z"},"links":{"citing_paper":"/paper/2505.13094"},"observation_digest":"sha256:412b97910fea98b4da78032e8e3f632a68d2a4df334b1b4bf86b8119d1089e0f","observation_id":"1a30bbd3-876c-4e3f-8cca-12da0d2db43f","resolution":{"observed_at":"2026-08-15T20:23:17.820695Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:23:17.269642Z","title":"Dual-path rnn: efficient long sequence modeling for time-domain single-channel speech separation,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.13094","last_updated":"2025-05-19T13:25:51Z","snapshot_observed_at":"2026-08-18T03:26:46.185382Z","submitted_at":"2025-05-19T13:25:51Z","title":"Time-Frequency-Based Attention Cache Memory Model for Real-Time Speech Separation","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-15T20:23:17.269642Z"},"links":{"citing_paper":"/paper/2505.13094"},"observation_digest":"sha256:234f70e5ae79499dc6f3a5ad15b5e081d0541f3ba02e16cda8357066b664070a","observation_id":"ce5527fe-4463-4412-a8a4-1bf3eeb30795","resolution":{"observed_at":"2026-08-15T20:23:17.269642Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:23:17.791505Z","title":"An efficient encoder-decoder architecture with top-down attention for speech separation,","venue":null,"work_id":"6592bd95-ecc9-4131-99c4-7883812f1dc8","year":2022},"citing_paper":{"arxiv_id":"2505.13094","last_updated":"2025-05-19T13:25:51Z","snapshot_observed_at":"2026-08-18T03:26:46.185382Z","submitted_at":"2025-05-19T13:25:51Z","title":"Time-Frequency-Based Attention Cache Memory Model for Real-Time Speech Separation","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-15T20:23:17.273512Z"},"links":{"citing_paper":"/paper/2505.13094"},"observation_digest":"sha256:b5ffa9ed6b662df9d74950e4fe21c6e20bf5110e2653a4ed4908e20b320db8a0","observation_id":"eb995a81-2bf3-4143-98bb-280e4b43d50d","resolution":{"observed_at":"2026-08-15T20:23:17.796505Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:23:17.778146Z","title":"Speech separation using an asynchronous fully recurrent convolutional neural network,","venue":null,"work_id":"64365f9f-f0bc-4795-bba5-fccb83a5dce0","year":2021},"citing_paper":{"arxiv_id":"2505.13094","last_updated":"2025-05-19T13:25:51Z","snapshot_observed_at":"2026-08-18T03:26:46.185382Z","submitted_at":"2025-05-19T13:25:51Z","title":"Time-Frequency-Based Attention Cache Memory Model for Real-Time Speech Separation","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-15T20:23:17.277731Z"},"links":{"citing_paper":"/paper/2505.13094"},"observation_digest":"sha256:19fc8824231cc6b11de9082847a513df8db093ac7c50a8d28cfcdd73226d06ee","observation_id":"0ee5853a-6a1b-4d57-a919-34b4df606410","resolution":{"observed_at":"2026-08-15T20:23:17.782593Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:23:17.765193Z","title":"On the Use of Deep Mask Estimation Module for Neural Source Separation Systems,","venue":null,"work_id":"f37fb4f8-40af-4861-8fd5-b0cf317134c8","year":2022},"citing_paper":{"arxiv_id":"2505.13094","last_updated":"2025-05-19T13:25:51Z","snapshot_observed_at":"2026-08-18T03:26:46.185382Z","submitted_at":"2025-05-19T13:25:51Z","title":"Time-Frequency-Based Attention Cache Memory Model for Real-Time Speech Separation","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-15T20:23:17.281709Z"},"links":{"citing_paper":"/paper/2505.13094"},"observation_digest":"sha256:8b23b2d59be8d78ed5e8d4568917c7e33b0ada07a80d867a4bd5fef325bed26d","observation_id":"21d503be-cbc5-44df-8393-2aa31c6258ac","resolution":{"observed_at":"2026-08-15T20:23:17.769457Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:23:17.749183Z","title":"Iianet: An intra-and inter-modality attention network for audio-visual speech separation,","venue":null,"work_id":"a47ecf50-d72a-4123-9903-93b5f84ae0e5","year":2024},"citing_paper":{"arxiv_id":"2505.13094","last_updated":"2025-05-19T13:25:51Z","snapshot_observed_at":"2026-08-18T03:26:46.185382Z","submitted_at":"2025-05-19T13:25:51Z","title":"Time-Frequency-Based Attention Cache Memory Model for Real-Time Speech Separation","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-15T20:23:17.285652Z"},"links":{"citing_paper":"/paper/2505.13094"},"observation_digest":"sha256:2a626f45c5dc7adedeb2ef0b09ff198f6751295b9db365264483ef2921ecf507","observation_id":"8055f04a-cb20-4837-9c51-2c6c195e6b3f","resolution":{"observed_at":"2026-08-15T20:23:17.753342Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:23:17.291926Z","title":"Tf-gridnet: Making time-frequency domain models great again for monaural speaker separation,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.13094","last_updated":"2025-05-19T13:25:51Z","snapshot_observed_at":"2026-08-18T03:26:46.185382Z","submitted_at":"2025-05-19T13:25:51Z","title":"Time-Frequency-Based Attention Cache Memory Model for Real-Time Speech Separation","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-15T20:23:17.291926Z"},"links":{"citing_paper":"/paper/2505.13094"},"observation_digest":"sha256:eec2f56c8fcc2b15142f93cb18649ed69f3fe6677c8db5d2e1f67555ac651ba1","observation_id":"d7f78144-1af0-4b43-97d3-55e9c6ab9803","resolution":{"observed_at":"2026-08-15T20:23:17.291926Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:23:17.295898Z","title":"Atten- tion is all you need in speech separation,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.13094","last_updated":"2025-05-19T13:25:51Z","snapshot_observed_at":"2026-08-18T03:26:46.185382Z","submitted_at":"2025-05-19T13:25:51Z","title":"Time-Frequency-Based Attention Cache Memory Model for Real-Time Speech Separation","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-15T20:23:17.295898Z"},"links":{"citing_paper":"/paper/2505.13094"},"observation_digest":"sha256:88867f946479a71b2d01b55a8dee996648ec44adbb312d35e5a250437f7b7841","observation_id":"fd2a6eef-6a7a-4554-b54d-c7ca8428bd4c","resolution":{"observed_at":"2026-08-15T20:23:17.295898Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:23:17.720425Z","title":"Dual-path transformer network: Direct context-aware modeling for end-to-end monaural speech separation,","venue":null,"work_id":"e502580c-c309-49ea-82da-b1397f4f8fb8","year":2020},"citing_paper":{"arxiv_id":"2505.13094","last_updated":"2025-05-19T13:25:51Z","snapshot_observed_at":"2026-08-18T03:26:46.185382Z","submitted_at":"2025-05-19T13:25:51Z","title":"Time-Frequency-Based Attention Cache Memory Model for Real-Time Speech Separation","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-15T20:23:17.300470Z"},"links":{"citing_paper":"/paper/2505.13094"},"observation_digest":"sha256:268af7043b841677e6141bb2938ef95e25e77da7b3b812d5a3ea5aa5d7f9394c","observation_id":"a4232277-5b18-4112-afd5-99bf82ea569d","resolution":{"observed_at":"2026-08-15T20:23:17.724878Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.02063","last_updated":"2024-09-10T14:02:58Z","snapshot_observed_at":"2026-08-16T14:04:14.953751Z","submitted_at":"2024-04-02T16:04:31Z","title":"SPMamba: State-space model is all you need in speech separation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.02063","snapshot_observed_at":"2026-08-15T20:23:17.304136Z","title":"Spmamba: State-space model is all you need in speech separation,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.13094","last_updated":"2025-05-19T13:25:51Z","snapshot_observed_at":"2026-08-18T03:26:46.185382Z","submitted_at":"2025-05-19T13:25:51Z","title":"Time-Frequency-Based Attention Cache Memory Model for Real-Time Speech Separation","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-15T20:23:17.304136Z"},"links":{"cited_paper":"/paper/2404.02063","citing_paper":"/paper/2505.13094"},"observation_digest":"sha256:b692e4a45f9a2493f866585f6bc4403fed72fa2a916e26b17f37ecb18e3644f7","observation_id":"c64cae30-066a-4cae-b9ea-5be9effa4ab8","resolution":{"observed_at":"2026-08-15T20:23:17.304136Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:23:17.707442Z","title":"Advances in online audio-visual meeting transcription,","venue":null,"work_id":"d00baba7-4188-437b-a02d-74dc602a428e","year":2019},"citing_paper":{"arxiv_id":"2505.13094","last_updated":"2025-05-19T13:25:51Z","snapshot_observed_at":"2026-08-18T03:26:46.185382Z","submitted_at":"2025-05-19T13:25:51Z","title":"Time-Frequency-Based Attention Cache Memory Model for Real-Time Speech Separation","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-15T20:23:17.308706Z"},"links":{"citing_paper":"/paper/2505.13094"},"observation_digest":"sha256:f793d55b7959f14277a1a4965124dc6d7b2c7f4c795764d30fecd5742ff9421f","observation_id":"c44ddce1-38f9-443d-97c9-d58e224546fd","resolution":{"observed_at":"2026-08-15T20:23:17.711456Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2009.03141","last_updated":"2020-09-07T14:53:27Z","snapshot_observed_at":"2026-07-06T09:53:30.600300Z","submitted_at":"2020-09-07T14:53:27Z","title":"An End-to-end Architecture of Online Multi-channel Speech Separation","version":1},"cited_work":{"arxiv_id":"2009.03141","doi":null,"metadata_source":"pith","pith_arxiv_id":"2009.03141","snapshot_observed_at":"2026-08-15T20:23:17.428915Z","title":"An End-to-end Architecture of Online Multi-channel Speech Separation","venue":"eess.AS","work_id":"b852d4c2-c5cf-499e-8f85-05c58c84457d","year":2020},"citing_paper":{"arxiv_id":"2505.13094","last_updated":"2025-05-19T13:25:51Z","snapshot_observed_at":"2026-08-18T03:26:46.185382Z","submitted_at":"2025-05-19T13:25:51Z","title":"Time-Frequency-Based Attention Cache Memory Model for Real-Time Speech Separation","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-15T20:23:17.312580Z"},"links":{"cited_paper":"/paper/2009.03141","citing_paper":"/paper/2505.13094"},"observation_digest":"sha256:74779665507db4d9cbf9646638685286c945c18331ae5e52206440159fe96572","observation_id":"6367b7d7-c5e7-4370-bc6a-ef6c50e32b2a","resolution":{"observed_at":"2026-08-15T20:23:17.435694Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:23:17.694456Z","title":"Skim: Skipping memory lstm for low-latency real-time continuous speech separation,","venue":null,"work_id":"ac7ab6d0-4af3-4d23-82e6-c1b3049b396a","year":2022},"citing_paper":{"arxiv_id":"2505.13094","last_updated":"2025-05-19T13:25:51Z","snapshot_observed_at":"2026-08-18T03:26:46.185382Z","submitted_at":"2025-05-19T13:25:51Z","title":"Time-Frequency-Based Attention Cache Memory Model for Real-Time Speech Separation","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-15T20:23:17.317606Z"},"links":{"citing_paper":"/paper/2505.13094"},"observation_digest":"sha256:72f19861610aa519304b8e40475fdf53be367076ad60c9f1a6f1906397718812","observation_id":"d84e012b-50c0-48de-883a-2be3ee77d932","resolution":{"observed_at":"2026-08-15T20:23:17.698801Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:23:17.675170Z","title":"Resource-efficient separation transformer,","venue":null,"work_id":"81541be6-c511-43b4-94b6-1015fa7ba99e","year":2024},"citing_paper":{"arxiv_id":"2505.13094","last_updated":"2025-05-19T13:25:51Z","snapshot_observed_at":"2026-08-18T03:26:46.185382Z","submitted_at":"2025-05-19T13:25:51Z","title":"Time-Frequency-Based Attention Cache Memory Model for Real-Time Speech Separation","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-15T20:23:17.321500Z"},"links":{"citing_paper":"/paper/2505.13094"},"observation_digest":"sha256:735ee940e41591c77514e51e834f5e94bd9ff59760a3719d61d7a2a482aaaec2","observation_id":"3d80f379-2c4e-49ba-8a5d-3772ab349dd4","resolution":{"observed_at":"2026-08-15T20:23:17.681711Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:23:17.324960Z","title":"Conv-tasnet: Surpassing ideal time– frequency magnitude masking for speech separation,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.13094","last_updated":"2025-05-19T13:25:51Z","snapshot_observed_at":"2026-08-18T03:26:46.185382Z","submitted_at":"2025-05-19T13:25:51Z","title":"Time-Frequency-Based Attention Cache Memory Model for Real-Time Speech Separation","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-15T20:23:17.324960Z"},"links":{"citing_paper":"/paper/2505.13094"},"observation_digest":"sha256:81ffd1cf2689afe608cf065012522da54c699583e3eed1dd722400f2a6bfe950","observation_id":"e62befec-a83a-4f2f-a594-9b9cc51e4a62","resolution":{"observed_at":"2026-08-15T20:23:17.324960Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:23:17.652973Z","title":"Low latency speech enhancement for hearing aids using deep filtering,","venue":null,"work_id":"0647cad2-fdd9-4450-80c0-3ec4885e6ea9","year":2022},"citing_paper":{"arxiv_id":"2505.13094","last_updated":"2025-05-19T13:25:51Z","snapshot_observed_at":"2026-08-18T03:26:46.185382Z","submitted_at":"2025-05-19T13:25:51Z","title":"Time-Frequency-Based Attention Cache Memory Model for Real-Time Speech Separation","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-15T20:23:17.328600Z"},"links":{"citing_paper":"/paper/2505.13094"},"observation_digest":"sha256:8875b4c706f918419bff0c69222ada5fcab9d8599ff0933f369a5423fcf92c85","observation_id":"d3d683b7-7d54-4ca7-95db-f8bcd3b65d18","resolution":{"observed_at":"2026-08-15T20:23:17.657244Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:23:17.639507Z","title":"On the design and training strategies for rnn-based online neural speech separation systems,","venue":null,"work_id":"e9dbc78d-f957-42f5-a5da-a812529c9fb1","year":2023},"citing_paper":{"arxiv_id":"2505.13094","last_updated":"2025-05-19T13:25:51Z","snapshot_observed_at":"2026-08-18T03:26:46.185382Z","submitted_at":"2025-05-19T13:25:51Z","title":"Time-Frequency-Based Attention Cache Memory Model for Real-Time Speech Separation","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-15T20:23:17.332276Z"},"links":{"citing_paper":"/paper/2505.13094"},"observation_digest":"sha256:b9bf49282ed76601e7bfb7a194bafe05aedc704459ebd9c5589a5c5129d7adf8","observation_id":"5663135f-4793-4195-a8ab-fe3412792e86","resolution":{"observed_at":"2026-08-15T20:23:17.643796Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:23:17.625880Z","title":"Predictive skim: Contrastive predictive coding for low-latency online speech separation,","venue":null,"work_id":"3345034d-f55b-4d54-a2d5-99a60a4b2cc4","year":2023},"citing_paper":{"arxiv_id":"2505.13094","last_updated":"2025-05-19T13:25:51Z","snapshot_observed_at":"2026-08-18T03:26:46.185382Z","submitted_at":"2025-05-19T13:25:51Z","title":"Time-Frequency-Based Attention Cache Memory Model for Real-Time Speech Separation","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-15T20:23:17.335636Z"},"links":{"citing_paper":"/paper/2505.13094"},"observation_digest":"sha256:2e13baa40f03d85f4aea2c25c50c46225155b0262fc0a03c2e0cb2b7ff952a19","observation_id":"c2154272-6a3f-4e7f-b4b3-4b9ba681a9b5","resolution":{"observed_at":"2026-08-15T20:23:17.630354Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:23:17.500068Z","title":"Attention is all you need,","venue":null,"work_id":"951cfedf-2838-4930-954d-1dde1f2fd115","year":2017},"citing_paper":{"arxiv_id":"2505.13094","last_updated":"2025-05-19T13:25:51Z","snapshot_observed_at":"2026-08-18T03:26:46.185382Z","submitted_at":"2025-05-19T13:25:51Z","title":"Time-Frequency-Based Attention Cache Memory Model for Real-Time Speech Separation","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-15T20:23:17.339054Z"},"links":{"citing_paper":"/paper/2505.13094"},"observation_digest":"sha256:701d953666acd3ef6c12f4e73e433b9e7db4bc686edc8e56b89c7cf097324614","observation_id":"f16cb438-6d2b-472d-b928-b60c1d830f1c","resolution":{"observed_at":"2026-08-15T20:23:17.503712Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1607.06450","last_updated":"2016-07-21T19:57:52Z","snapshot_observed_at":"2026-08-15T04:53:45.483331Z","submitted_at":"2016-07-21T19:57:52Z","title":"Layer Normalization","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1607.06450","snapshot_observed_at":"2026-08-15T20:23:17.343304Z","title":"Layer normalization,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2505.13094","last_updated":"2025-05-19T13:25:51Z","snapshot_observed_at":"2026-08-18T03:26:46.185382Z","submitted_at":"2025-05-19T13:25:51Z","title":"Time-Frequency-Based Attention Cache Memory Model for Real-Time Speech Separation","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-15T20:23:17.343304Z"},"links":{"cited_paper":"/paper/1607.06450","citing_paper":"/paper/2505.13094"},"observation_digest":"sha256:54a1a952c660cdd8ed0036ba4e27ed9c03307b0b7dc3fa41b1fe3e7d65ed2ddc","observation_id":"6496d905-b12c-4dbe-96c1-326a3970beaa","resolution":{"observed_at":"2026-08-15T20:23:17.343304Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1907.01160","last_updated":"2019-07-02T04:27:55Z","snapshot_observed_at":"2026-07-06T08:04:17.909965Z","submitted_at":"2019-07-02T04:27:55Z","title":"WHAM!: Extending Speech Separation to Noisy Environments","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1907.01160","snapshot_observed_at":"2026-08-15T20:23:17.347263Z","title":"Wham!: Extending speech separation to noisy environments,","venue":null,"work_id":null,"year":1907},"citing_paper":{"arxiv_id":"2505.13094","last_updated":"2025-05-19T13:25:51Z","snapshot_observed_at":"2026-08-18T03:26:46.185382Z","submitted_at":"2025-05-19T13:25:51Z","title":"Time-Frequency-Based Attention Cache Memory Model for Real-Time Speech Separation","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-15T20:23:17.347263Z"},"links":{"cited_paper":"/paper/1907.01160","citing_paper":"/paper/2505.13094"},"observation_digest":"sha256:7bf1c1aafdf1a005cfedcc04f2c7167ca9374ac4d6396069bd24151aacfedaef","observation_id":"773a7c24-b10c-4f36-a737-cb3d9a6dc289","resolution":{"observed_at":"2026-08-15T20:23:17.347263Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:23:17.351404Z","title":"Whamr!: Noisy and reverberant single-channel speech separation,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.13094","last_updated":"2025-05-19T13:25:51Z","snapshot_observed_at":"2026-08-18T03:26:46.185382Z","submitted_at":"2025-05-19T13:25:51Z","title":"Time-Frequency-Based Attention Cache Memory Model for Real-Time Speech Separation","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-15T20:23:17.351404Z"},"links":{"citing_paper":"/paper/2505.13094"},"observation_digest":"sha256:59327d2ad05543bc258fbab3207af321fc7d199ebdabcf00acf864ba37b17923","observation_id":"dea52403-9dd1-469a-ae8f-c715b7049cac","resolution":{"observed_at":"2026-08-15T20:23:17.351404Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2005.11262","last_updated":"2020-05-22T16:26:54Z","snapshot_observed_at":"2026-08-15T22:07:29.787968Z","submitted_at":"2020-05-22T16:26:54Z","title":"LibriMix: An Open-Source Dataset for Generalizable Speech Separation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.11262","snapshot_observed_at":"2026-08-15T20:23:17.354936Z","title":"Librimix: An open-source dataset for generalizable speech separation,","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2505.13094","last_updated":"2025-05-19T13:25:51Z","snapshot_observed_at":"2026-08-18T03:26:46.185382Z","submitted_at":"2025-05-19T13:25:51Z","title":"Time-Frequency-Based Attention Cache Memory Model for Real-Time Speech Separation","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-15T20:23:17.354936Z"},"links":{"cited_paper":"/paper/2005.11262","citing_paper":"/paper/2505.13094"},"observation_digest":"sha256:40fe60b31b59b9f2fdb668a7a22303e4b400fe82098f197fbe6292040be4f6cb","observation_id":"991ebbc0-4aa7-47a7-8982-912d1144ade2","resolution":{"observed_at":"2026-08-15T20:23:17.354936Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:23:17.478750Z","title":"Permutation invariant training of deep models for speaker-independent multi-talker speech separation,","venue":null,"work_id":"2080a324-361c-4dc3-be24-2b26f302ca1d","year":2017},"citing_paper":{"arxiv_id":"2505.13094","last_updated":"2025-05-19T13:25:51Z","snapshot_observed_at":"2026-08-18T03:26:46.185382Z","submitted_at":"2025-05-19T13:25:51Z","title":"Time-Frequency-Based Attention Cache Memory Model for Real-Time Speech Separation","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-15T20:23:17.358778Z"},"links":{"citing_paper":"/paper/2505.13094"},"observation_digest":"sha256:bb6efb84bb6c4bcea6d788a8f9349557004674c5becef650b3f62c18416387e7","observation_id":"0bcbebdf-8232-43b0-90fd-0e299fab40e7","resolution":{"observed_at":"2026-08-15T20:23:17.483306Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:23:17.362250Z","title":"Sdr–half-baked or well done?","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.13094","last_updated":"2025-05-19T13:25:51Z","snapshot_observed_at":"2026-08-18T03:26:46.185382Z","submitted_at":"2025-05-19T13:25:51Z","title":"Time-Frequency-Based Attention Cache Memory Model for Real-Time Speech Separation","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-15T20:23:17.362250Z"},"links":{"citing_paper":"/paper/2505.13094"},"observation_digest":"sha256:ff5d8154d9ac117144966d7b7d1d7f13c49a8db8942be52a9fc2d880ccb209f5","observation_id":"78af1e41-a59a-4beb-9ecb-bb40b6e3c9f2","resolution":{"observed_at":"2026-08-15T20:23:17.362250Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:23:17.365827Z","title":"Performance measurement in blind audio source separation,","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2505.13094","last_updated":"2025-05-19T13:25:51Z","snapshot_observed_at":"2026-08-18T03:26:46.185382Z","submitted_at":"2025-05-19T13:25:51Z","title":"Time-Frequency-Based Attention Cache Memory Model for Real-Time Speech Separation","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-15T20:23:17.365827Z"},"links":{"citing_paper":"/paper/2505.13094"},"observation_digest":"sha256:69ab28916beebeb3bab03b91f43e9d432a8d2997f1f78baf96592f4318eec0f9","observation_id":"61929ceb-3d3f-4a85-b2d3-65d74862bf75","resolution":{"observed_at":"2026-08-15T20:23:17.365827Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2505.13094","last_updated":"2025-05-19T13:25:51Z","latest_version":1,"primary_category":"cs.SD","snapshot_observed_at":"2026-08-18T03:26:46.185382Z","submitted_at":"2025-05-19T13:25:51Z","title":"Time-Frequency-Based Attention Cache Memory Model for Real-Time Speech Separation"},"reference_resolution":{"displayed":26,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":11,"verified_exact":1,"verified_fuzzy":14},"total_outbound_references":26},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"thesis":"As of 21 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 1 inbound Pith citation observation for arXiv:2505.13094."}