{"as_of":"2026-08-07T11:41:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:aecd61cbcc1de23993c783bf63783b3973b14f9be4f4fec7b25d399f4cb7c101","coverage":[{"denominator":78,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":78,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-27T23:49:05.871422Z","state":"measured"},{"denominator":78,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":78,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2606.05911/citation-record","integrity":"/paper/2606.05911/integrity","json":"/paper/2606.05911/citation-record.json","paper":"/paper/2606.05911"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-27T23:49:05.871422Z","title":"Validity and robustness of denoisers: A proof of concept in speech denoising,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.05911","last_updated":"2026-06-04T09:16:26Z","snapshot_observed_at":"2026-08-06T13:48:13.385785Z","submitted_at":"2026-06-04T09:16:26Z","title":"DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-06-27T23:49:05.871422Z"},"links":{"citing_paper":"/paper/2606.05911"},"observation_digest":"sha256:188c2c470b9e6e0a5afec57a073646c8add3de300971609aab1000020643c19f","observation_id":"134aa10f-1665-417c-a0f3-443e2ca318f1","resolution":{"observed_at":"2026-06-27T23:49:05.871422Z","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":"2026-06-27T23:49:05.871422Z","title":"Dubbing movies via hierarchical phoneme modeling and acoustic diffusion denoising,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.05911","last_updated":"2026-06-04T09:16:26Z","snapshot_observed_at":"2026-08-06T13:48:13.385785Z","submitted_at":"2026-06-04T09:16:26Z","title":"DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-06-27T23:49:05.871422Z"},"links":{"citing_paper":"/paper/2606.05911"},"observation_digest":"sha256:a744d757422d9064b7ea660bbdbbe08f0c642be25a95ba8f12e23f1d1cb71916","observation_id":"4189d019-fe54-4b29-b796-72b339892e7d","resolution":{"observed_at":"2026-06-27T23:49:05.871422Z","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":"2026-06-27T23:49:05.871422Z","title":"Bsdb-net: Band-split dual-branch network with selective state spaces mechanism for monaural speech enhancement,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.05911","last_updated":"2026-06-04T09:16:26Z","snapshot_observed_at":"2026-08-06T13:48:13.385785Z","submitted_at":"2026-06-04T09:16:26Z","title":"DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-06-27T23:49:05.871422Z"},"links":{"citing_paper":"/paper/2606.05911"},"observation_digest":"sha256:66471b0107f0c4f2865143cb1c54f864abed264e4ce465de858cc9223748b78d","observation_id":"319db937-7e43-44af-9aed-f146b35e1446","resolution":{"observed_at":"2026-06-27T23:49:05.871422Z","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":"2026-06-27T23:49:05.871422Z","title":"Cross-modal knowledge distillation with multi-stage adaptive feature fusion for speech separa- tion,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.05911","last_updated":"2026-06-04T09:16:26Z","snapshot_observed_at":"2026-08-06T13:48:13.385785Z","submitted_at":"2026-06-04T09:16:26Z","title":"DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-06-27T23:49:05.871422Z"},"links":{"citing_paper":"/paper/2606.05911"},"observation_digest":"sha256:16d01ac936f5313c4f6082ed0d5dc208a1a114a70739bc9fce535ffb290b8662","observation_id":"02f6518d-a283-4de3-984e-4925582e8e61","resolution":{"observed_at":"2026-06-27T23:49:05.871422Z","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":"2026-06-27T23:49:05.871422Z","title":"Waveform-domain speech enhancement using spectrogram encoding for robust speech recognition,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.05911","last_updated":"2026-06-04T09:16:26Z","snapshot_observed_at":"2026-08-06T13:48:13.385785Z","submitted_at":"2026-06-04T09:16:26Z","title":"DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-06-27T23:49:05.871422Z"},"links":{"citing_paper":"/paper/2606.05911"},"observation_digest":"sha256:1427e968ffe42fa8a742454c8ad153dd14916fbd1afbf6e79b2f321a4c69ee64","observation_id":"9745b5ca-87e6-40f0-8cca-3b337a1c90bb","resolution":{"observed_at":"2026-06-27T23:49:05.871422Z","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":"2026-06-27T23:49:05.871422Z","title":"Automatic speech recognition: A survey of deep learning techniques and approaches,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.05911","last_updated":"2026-06-04T09:16:26Z","snapshot_observed_at":"2026-08-06T13:48:13.385785Z","submitted_at":"2026-06-04T09:16:26Z","title":"DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-06-27T23:49:05.871422Z"},"links":{"citing_paper":"/paper/2606.05911"},"observation_digest":"sha256:f0f1e467ef5bbe7846b06256c3af1936a7ca12475c789e100211efb65f757bed","observation_id":"3786189b-1b84-4843-aa15-05f787c956b5","resolution":{"observed_at":"2026-06-27T23:49:05.871422Z","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":"2026-06-27T23:49:05.871422Z","title":"Seeing helps hearing: A multi-modal dataset and a mamba- based dual branch parallel network for auditory attention decoding,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.05911","last_updated":"2026-06-04T09:16:26Z","snapshot_observed_at":"2026-08-06T13:48:13.385785Z","submitted_at":"2026-06-04T09:16:26Z","title":"DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-06-27T23:49:05.871422Z"},"links":{"citing_paper":"/paper/2606.05911"},"observation_digest":"sha256:9b1f70dc318c0f105387023d5db84a0417e07eac965221c64d672d18d974d974","observation_id":"6e516322-ca1e-4a97-ab80-ce6e3d022ff3","resolution":{"observed_at":"2026-06-27T23:49:05.871422Z","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":"2026-06-27T23:49:05.871422Z","title":"An overview of deep-learning-based audio-visual speech en- hancement and separation,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.05911","last_updated":"2026-06-04T09:16:26Z","snapshot_observed_at":"2026-08-06T13:48:13.385785Z","submitted_at":"2026-06-04T09:16:26Z","title":"DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-06-27T23:49:05.871422Z"},"links":{"citing_paper":"/paper/2606.05911"},"observation_digest":"sha256:2cc30a9414cf04ccecbe5647e4774ad46e1f24cb265d7b04490e61e12fac490c","observation_id":"45f15fb9-904f-4f24-a781-e60484b8025e","resolution":{"observed_at":"2026-06-27T23:49:05.871422Z","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":"2026-06-27T23:49:05.871422Z","title":"Sse-net: Towards low-power-consumption spiking neural network for monaural speech enhancement,","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2606.05911","last_updated":"2026-06-04T09:16:26Z","snapshot_observed_at":"2026-08-06T13:48:13.385785Z","submitted_at":"2026-06-04T09:16:26Z","title":"DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-06-27T23:49:05.871422Z"},"links":{"citing_paper":"/paper/2606.05911"},"observation_digest":"sha256:b4dac13085a7b1f08b81f45e36a1d62421562ab3285f48a6fe6fae92f37421a5","observation_id":"5e192418-3f10-4cf0-b183-07d1b92c3e33","resolution":{"observed_at":"2026-06-27T23:49:05.871422Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2506.12479","last_updated":"2025-07-24T18:15:00Z","snapshot_observed_at":"2026-08-07T00:46:19.992557Z","submitted_at":"2025-06-14T12:43:07Z","title":"AI Flow: Perspectives, Scenarios, and Approaches","version":3},"cited_work":{"arxiv_id":"2506.12479","doi":"10.48550/arxiv.2506.12479","metadata_source":"arxiv_reference","pith_arxiv_id":"2506.12479","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"arXiv preprint arXiv:2506.12479 (2025)","venue":"ArXiv.org","work_id":"42dc61ce-8b4c-425e-80ee-39b5c927c89b","year":2025},"citing_paper":{"arxiv_id":"2606.05911","last_updated":"2026-06-04T09:16:26Z","snapshot_observed_at":"2026-08-06T13:48:13.385785Z","submitted_at":"2026-06-04T09:16:26Z","title":"DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-06-27T23:49:05.871422Z"},"links":{"cited_paper":"/paper/2506.12479","citing_paper":"/paper/2606.05911"},"observation_digest":"sha256:19d9897279d10f2f0b857be533feaff00fdaabef165fb98585eb2f5b7ac13411","observation_id":"3f639b45-7dac-40df-81c5-0932f9096acf","resolution":{"observed_at":"2026-07-02T15:37:06.108421Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-06-27T23:49:05.871422Z","title":"Compact deep neural networks for real-time speech enhancement on resource-limited devices,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.05911","last_updated":"2026-06-04T09:16:26Z","snapshot_observed_at":"2026-08-06T13:48:13.385785Z","submitted_at":"2026-06-04T09:16:26Z","title":"DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-06-27T23:49:05.871422Z"},"links":{"citing_paper":"/paper/2606.05911"},"observation_digest":"sha256:34e39d119946a43f6552a1427b6bbf5096f3ddc9896e35ca648e5eeb08d590c6","observation_id":"ad392537-605b-44d1-ab08-7cf4bc5497b2","resolution":{"observed_at":"2026-06-27T23:49:05.871422Z","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":"2026-06-27T23:49:05.871422Z","title":"Dccrn: Deep complex convolution recurrent network for phase-aware speech enhancement,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.05911","last_updated":"2026-06-04T09:16:26Z","snapshot_observed_at":"2026-08-06T13:48:13.385785Z","submitted_at":"2026-06-04T09:16:26Z","title":"DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-06-27T23:49:05.871422Z"},"links":{"citing_paper":"/paper/2606.05911"},"observation_digest":"sha256:6884ab8adace32f2cf07791efc5818c13a9ac33e1431010537bd16717a276607","observation_id":"05530556-2df9-42f2-8283-e09fa46440be","resolution":{"observed_at":"2026-06-27T23:49:05.871422Z","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":"2026-06-27T23:49:05.871422Z","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":"2606.05911","last_updated":"2026-06-04T09:16:26Z","snapshot_observed_at":"2026-08-06T13:48:13.385785Z","submitted_at":"2026-06-04T09:16:26Z","title":"DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-06-27T23:49:05.871422Z"},"links":{"citing_paper":"/paper/2606.05911"},"observation_digest":"sha256:5de52e3f12073b6d327bcb5b5cb1cea485ad6a6e2b2a76e00081445388124391","observation_id":"777a0908-5226-4261-bc91-7bb3efc91ed0","resolution":{"observed_at":"2026-06-27T23:49:05.871422Z","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":"2026-06-27T23:49:05.871422Z","title":"Large-scale training to increase speech intelligibility for hearing-impaired listeners in novel noises,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2606.05911","last_updated":"2026-06-04T09:16:26Z","snapshot_observed_at":"2026-08-06T13:48:13.385785Z","submitted_at":"2026-06-04T09:16:26Z","title":"DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-06-27T23:49:05.871422Z"},"links":{"citing_paper":"/paper/2606.05911"},"observation_digest":"sha256:d60e1e82dc95238548f3f13c9cd7cccc1254f65fe43f30882bcf96fb0e0e2e70","observation_id":"7af71d61-3a68-49d2-a576-946cf5339cdc","resolution":{"observed_at":"2026-06-27T23:49:05.871422Z","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":"2026-06-27T23:49:05.871422Z","title":"Two heads are better than one: A two-stage complex spectral mapping approach for monaural speech enhancement,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.05911","last_updated":"2026-06-04T09:16:26Z","snapshot_observed_at":"2026-08-06T13:48:13.385785Z","submitted_at":"2026-06-04T09:16:26Z","title":"DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-06-27T23:49:05.871422Z"},"links":{"citing_paper":"/paper/2606.05911"},"observation_digest":"sha256:c320e50169152d67962955ac450e37e8fb28daf5a30d5c5482ed775e0e336e67","observation_id":"d0d1f2a3-f3da-43b6-986a-866625b54c00","resolution":{"observed_at":"2026-06-27T23:49:05.871422Z","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":"2026-06-27T23:49:05.871422Z","title":"Dbt-net: Dual-branch federative magnitude and phase estimation with attention- in-attention transformer for monaural speech enhancement,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.05911","last_updated":"2026-06-04T09:16:26Z","snapshot_observed_at":"2026-08-06T13:48:13.385785Z","submitted_at":"2026-06-04T09:16:26Z","title":"DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-06-27T23:49:05.871422Z"},"links":{"citing_paper":"/paper/2606.05911"},"observation_digest":"sha256:cd06bf47b9a9065178573813851e44100f7b5066a3e787b69e108b4aeb57e0ba","observation_id":"ea53361b-63a2-4e0a-a1da-1e58d4ad437c","resolution":{"observed_at":"2026-06-27T23:49:05.871422Z","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":"2026-06-27T23:49:05.871422Z","title":"A convolutional recurrent neural network for real-time speech enhancement","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2606.05911","last_updated":"2026-06-04T09:16:26Z","snapshot_observed_at":"2026-08-06T13:48:13.385785Z","submitted_at":"2026-06-04T09:16:26Z","title":"DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-06-27T23:49:05.871422Z"},"links":{"citing_paper":"/paper/2606.05911"},"observation_digest":"sha256:420c3343031b9912dc2d7e2a9eb6f5f14806359299e8113f87e4c87143aa234d","observation_id":"6a4fb0a2-b783-4375-ab3c-c82f25d99fec","resolution":{"observed_at":"2026-06-27T23:49:05.871422Z","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":"2026-06-27T23:49:05.871422Z","title":"Learning complex spectral mapping with gated convolutional recurrent networks for monaural speech enhancement,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2606.05911","last_updated":"2026-06-04T09:16:26Z","snapshot_observed_at":"2026-08-06T13:48:13.385785Z","submitted_at":"2026-06-04T09:16:26Z","title":"DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-06-27T23:49:05.871422Z"},"links":{"citing_paper":"/paper/2606.05911"},"observation_digest":"sha256:80b303683a362933076686764f14cad77b570f5fbde9de8998dd92d4db91c4f7","observation_id":"7843751c-6a26-43e3-b0bb-fbf7ed16388d","resolution":{"observed_at":"2026-06-27T23:49:05.871422Z","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":"2026-06-27T23:49:05.871422Z","title":"On the compensation between magnitude and phase in speech separation,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2606.05911","last_updated":"2026-06-04T09:16:26Z","snapshot_observed_at":"2026-08-06T13:48:13.385785Z","submitted_at":"2026-06-04T09:16:26Z","title":"DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-06-27T23:49:05.871422Z"},"links":{"citing_paper":"/paper/2606.05911"},"observation_digest":"sha256:732749c71ddf0a2a03b377d2a185a740b741edd44b9b9cdc33f6eca664c31760","observation_id":"036c8a71-fef0-410e-b4c3-3d7ca9d483e6","resolution":{"observed_at":"2026-06-27T23:49:05.871422Z","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":"2026-06-27T23:49:05.871422Z","title":"Seeing helps hearing: A multi-modal dataset and a mamba- based dual branch parallel network for auditory attention decoding,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.05911","last_updated":"2026-06-04T09:16:26Z","snapshot_observed_at":"2026-08-06T13:48:13.385785Z","submitted_at":"2026-06-04T09:16:26Z","title":"DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-06-27T23:49:05.871422Z"},"links":{"citing_paper":"/paper/2606.05911"},"observation_digest":"sha256:aaf3ed08cc9436ebb3df62e68c75a83e3b4965a9da4e8bf54267905700c904b6","observation_id":"0c005e12-baa7-4a93-8c3d-5c79eb336b7b","resolution":{"observed_at":"2026-06-27T23:49:05.871422Z","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":"2026-06-27T23:49:05.871422Z","title":"Glance and gaze: A collaborative learning framework for single-channel speech enhancement,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.05911","last_updated":"2026-06-04T09:16:26Z","snapshot_observed_at":"2026-08-06T13:48:13.385785Z","submitted_at":"2026-06-04T09:16:26Z","title":"DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-06-27T23:49:05.871422Z"},"links":{"citing_paper":"/paper/2606.05911"},"observation_digest":"sha256:36dae78189d3d4e89917b909821b694b519a67bbc9228f28199762ebf046a08b","observation_id":"d50412e6-2493-4ea3-93b4-d4f6c523b389","resolution":{"observed_at":"2026-06-27T23:49:05.871422Z","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":"2026-06-27T23:49:05.871422Z","title":"Fullsubnet: A full-band and sub- band fusion model for real-time single-channel speech enhancement,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.05911","last_updated":"2026-06-04T09:16:26Z","snapshot_observed_at":"2026-08-06T13:48:13.385785Z","submitted_at":"2026-06-04T09:16:26Z","title":"DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-06-27T23:49:05.871422Z"},"links":{"citing_paper":"/paper/2606.05911"},"observation_digest":"sha256:24be461c50d19d3a223245a06fe1f8e69974503a6bb679867c669e7616163bda","observation_id":"8cc78dd9-19d4-49da-a229-d611329b2476","resolution":{"observed_at":"2026-06-27T23:49:05.871422Z","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":"2026-06-27T23:49:05.871422Z","title":"A low-power streaming speech enhance- ment accelerator for edge devices,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.05911","last_updated":"2026-06-04T09:16:26Z","snapshot_observed_at":"2026-08-06T13:48:13.385785Z","submitted_at":"2026-06-04T09:16:26Z","title":"DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-06-27T23:49:05.871422Z"},"links":{"citing_paper":"/paper/2606.05911"},"observation_digest":"sha256:949668a6bb9e24f510fe388fd51793f0985c1d6d41f3479c321a1718bca3763a","observation_id":"2007b627-20c7-4504-96fd-8d505ff42fd1","resolution":{"observed_at":"2026-06-27T23:49:05.871422Z","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":"2026-06-27T23:49:05.871422Z","title":"Flowse: Flow matching- based speech enhancement,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.05911","last_updated":"2026-06-04T09:16:26Z","snapshot_observed_at":"2026-08-06T13:48:13.385785Z","submitted_at":"2026-06-04T09:16:26Z","title":"DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-06-27T23:49:05.871422Z"},"links":{"citing_paper":"/paper/2606.05911"},"observation_digest":"sha256:1bfcb58f1b83a2e62b35978591ca8b10a3cd5867c86042a2fd283cbb3bd90103","observation_id":"ba34d46b-7907-4812-a2bd-6454d64099f7","resolution":{"observed_at":"2026-06-27T23:49:05.871422Z","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":"2026-06-27T23:49:05.871422Z","title":"Toward ultralow- power neuromorphic speech enhancement with spiking-fullsubnet,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.05911","last_updated":"2026-06-04T09:16:26Z","snapshot_observed_at":"2026-08-06T13:48:13.385785Z","submitted_at":"2026-06-04T09:16:26Z","title":"DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-06-27T23:49:05.871422Z"},"links":{"citing_paper":"/paper/2606.05911"},"observation_digest":"sha256:bfe8b1ce09830c6bfe33bc11994e8f0ba7e88e4ae0c3955cc7c92d7d6a183554","observation_id":"14dbc579-e443-4156-9f97-75241cc8d9a5","resolution":{"observed_at":"2026-06-27T23:49:05.871422Z","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":"2026-06-27T23:49:05.871422Z","title":"Speech emotion recognition based on spiking neural network and convolutional neural network,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.05911","last_updated":"2026-06-04T09:16:26Z","snapshot_observed_at":"2026-08-06T13:48:13.385785Z","submitted_at":"2026-06-04T09:16:26Z","title":"DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-06-27T23:49:05.871422Z"},"links":{"citing_paper":"/paper/2606.05911"},"observation_digest":"sha256:8855c0696820bc71c380d95293b946c946be7d0d785fb75f9b456faec0c4e69d","observation_id":"0814e5cf-8f3b-444e-ab2d-940a32dfa586","resolution":{"observed_at":"2026-06-27T23:49:05.871422Z","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":"2026-06-27T23:49:05.871422Z","title":"A hybrid ann- snn architecture for low-power and low-latency visual perception,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.05911","last_updated":"2026-06-04T09:16:26Z","snapshot_observed_at":"2026-08-06T13:48:13.385785Z","submitted_at":"2026-06-04T09:16:26Z","title":"DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-06-27T23:49:05.871422Z"},"links":{"citing_paper":"/paper/2606.05911"},"observation_digest":"sha256:fa1e738f4357b03649af341a6456974a38a63357b8dcc38094dd5bf9776d87ed","observation_id":"d6006c71-9d1f-47af-b37f-b491f1cf3408","resolution":{"observed_at":"2026-06-27T23:49:05.871422Z","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":"2026-06-27T23:49:05.871422Z","title":"Spiking neural networks on fpga: A survey of methodologies and recent advancements,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.05911","last_updated":"2026-06-04T09:16:26Z","snapshot_observed_at":"2026-08-06T13:48:13.385785Z","submitted_at":"2026-06-04T09:16:26Z","title":"DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-06-27T23:49:05.871422Z"},"links":{"citing_paper":"/paper/2606.05911"},"observation_digest":"sha256:8725aa4ce5edad9f71a999fa97fa76be1ffc61d6c12784e442c1f8f050446468","observation_id":"ce11a76c-82e7-42a7-ad7e-ecb35b159553","resolution":{"observed_at":"2026-06-27T23:49:05.871422Z","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":"2026-06-27T23:49:05.871422Z","title":"The intel neuromorphic dns challenge,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.05911","last_updated":"2026-06-04T09:16:26Z","snapshot_observed_at":"2026-08-06T13:48:13.385785Z","submitted_at":"2026-06-04T09:16:26Z","title":"DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-06-27T23:49:05.871422Z"},"links":{"citing_paper":"/paper/2606.05911"},"observation_digest":"sha256:a457340a4a3c439a2520d171a4dfca4bcd6191af06018ca23cbfce6ff45d19f5","observation_id":"40954ba3-3c7c-437a-b076-aa765e4d7171","resolution":{"observed_at":"2026-06-27T23:49:05.871422Z","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":"2026-06-27T23:49:05.871422Z","title":"A hybrid ann- snn architecture for low-power and low-latency visual perception,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.05911","last_updated":"2026-06-04T09:16:26Z","snapshot_observed_at":"2026-08-06T13:48:13.385785Z","submitted_at":"2026-06-04T09:16:26Z","title":"DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-06-27T23:49:05.871422Z"},"links":{"citing_paper":"/paper/2606.05911"},"observation_digest":"sha256:f515fb8233b6fd7221b2d820f714fa1ef0ad37e43375eef7eb545564636d468f","observation_id":"7cec81a8-6767-4b22-ac76-432a32b8639e","resolution":{"observed_at":"2026-06-27T23:49:05.871422Z","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":"2026-06-27T23:49:05.871422Z","title":"Hynita: A neuromorphic inference and training accelerator for hybrid ann-snn fusion models,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.05911","last_updated":"2026-06-04T09:16:26Z","snapshot_observed_at":"2026-08-06T13:48:13.385785Z","submitted_at":"2026-06-04T09:16:26Z","title":"DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-06-27T23:49:05.871422Z"},"links":{"citing_paper":"/paper/2606.05911"},"observation_digest":"sha256:6fb83d7bd2672bc970bf01b6f1a5d8c92fd7bdf64c8ca736fe2d4372395d0d04","observation_id":"e9847b36-0592-430c-9477-1dc29ea47be3","resolution":{"observed_at":"2026-06-27T23:49:05.871422Z","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":"2026-06-27T23:49:05.871422Z","title":"Minimizing informa- tion loss reduces spiking neuronal networks to differential equations,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.05911","last_updated":"2026-06-04T09:16:26Z","snapshot_observed_at":"2026-08-06T13:48:13.385785Z","submitted_at":"2026-06-04T09:16:26Z","title":"DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-06-27T23:49:05.871422Z"},"links":{"citing_paper":"/paper/2606.05911"},"observation_digest":"sha256:149cd1e9614d55ba76317b82dda8bd670a17d1fa1e4c1001f6fa8b093c5f64c6","observation_id":"9130e35c-0610-4781-8c6d-ddc26cc3cf1d","resolution":{"observed_at":"2026-06-27T23:49:05.871422Z","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":"2026-06-27T23:49:05.871422Z","title":"Ai flow at the network edge,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.05911","last_updated":"2026-06-04T09:16:26Z","snapshot_observed_at":"2026-08-06T13:48:13.385785Z","submitted_at":"2026-06-04T09:16:26Z","title":"DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-06-27T23:49:05.871422Z"},"links":{"citing_paper":"/paper/2606.05911"},"observation_digest":"sha256:b2ba91c809e51964ea322685906aef27dc658d486da9b33d01feb66a5dd8d2fb","observation_id":"980a60c8-bdb2-45c1-bed8-e704d69539f8","resolution":{"observed_at":"2026-06-27T23:49:05.871422Z","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":"2026-06-27T23:49:05.871422Z","title":"Naturalspeech: End-to-end text-to-speech synthesis with human-level quality,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.05911","last_updated":"2026-06-04T09:16:26Z","snapshot_observed_at":"2026-08-06T13:48:13.385785Z","submitted_at":"2026-06-04T09:16:26Z","title":"DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-06-27T23:49:05.871422Z"},"links":{"citing_paper":"/paper/2606.05911"},"observation_digest":"sha256:4cbaebb6b2a16915e8c354f24ef4b31d2a53a38f5fd36fc7ef2d75d584c63864","observation_id":"dea79e77-07bb-4a4a-98f1-0c9e90bda308","resolution":{"observed_at":"2026-06-27T23:49:05.871422Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2507.17563","last_updated":"2025-07-23T14:53:50Z","snapshot_observed_at":"2026-08-07T03:22:50.485332Z","submitted_at":"2025-07-23T14:53:50Z","title":"BoSS: Beyond-Semantic Speech","version":1},"cited_work":{"arxiv_id":"2507.17563","doi":"10.48550/arxiv.2507.17563","metadata_source":"arxiv_reference","pith_arxiv_id":"2507.17563","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Boss: Beyond-semantic speech,","venue":"ArXiv.org","work_id":"03af9241-116d-4c15-87e2-b24d3bd9bb3f","year":2025},"citing_paper":{"arxiv_id":"2606.05911","last_updated":"2026-06-04T09:16:26Z","snapshot_observed_at":"2026-08-06T13:48:13.385785Z","submitted_at":"2026-06-04T09:16:26Z","title":"DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-06-27T23:49:05.871422Z"},"links":{"cited_paper":"/paper/2507.17563","citing_paper":"/paper/2606.05911"},"observation_digest":"sha256:c8016748bb17b906bbdf0cbab29f4b38f8006560783b91dda56a4f19f0b827a5","observation_id":"0bfbeeef-bea8-4266-87cb-a94e28ad5ad6","resolution":{"observed_at":"2026-07-02T15:37:06.111137Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-06-27T23:49:05.871422Z","title":"Fullsubnet+: Channel attention fullsubnet with complex spectrograms for speech enhancement,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.05911","last_updated":"2026-06-04T09:16:26Z","snapshot_observed_at":"2026-08-06T13:48:13.385785Z","submitted_at":"2026-06-04T09:16:26Z","title":"DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-06-27T23:49:05.871422Z"},"links":{"citing_paper":"/paper/2606.05911"},"observation_digest":"sha256:da1b5f5e3c7eff7d6c6dbc02eb74ab9bd6ef9dcf95e1f90f799ae618d9c33768","observation_id":"d164540e-ee42-40a3-8df4-a4bbb0b45945","resolution":{"observed_at":"2026-06-27T23:49:05.871422Z","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":"2026-06-27T23:49:05.871422Z","title":"Taylor, can you hear me now? a taylor-unfolding framework for monaural speech enhancement,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.05911","last_updated":"2026-06-04T09:16:26Z","snapshot_observed_at":"2026-08-06T13:48:13.385785Z","submitted_at":"2026-06-04T09:16:26Z","title":"DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-06-27T23:49:05.871422Z"},"links":{"citing_paper":"/paper/2606.05911"},"observation_digest":"sha256:d9f557bc3b32c01eba1468d9e20cc321d79358f735627d1489bf97b66d9c9a17","observation_id":"87bb0623-a94f-4a7e-9ea9-2d2f66810dac","resolution":{"observed_at":"2026-06-27T23:49:05.871422Z","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":"2026-06-27T23:49:05.871422Z","title":"Comp- net: Complementary network for single-channel speech enhancement,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.05911","last_updated":"2026-06-04T09:16:26Z","snapshot_observed_at":"2026-08-06T13:48:13.385785Z","submitted_at":"2026-06-04T09:16:26Z","title":"DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-06-27T23:49:05.871422Z"},"links":{"citing_paper":"/paper/2606.05911"},"observation_digest":"sha256:ce637e186801066bb2ecfbe70269d6bd1d60e084a9b81e0361dafd91fda016bf","observation_id":"c38faa61-68ff-4381-a386-0f1c521e0e58","resolution":{"observed_at":"2026-06-27T23:49:05.871422Z","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":"2026-06-27T23:49:05.871422Z","title":"Learning a spiking neural network for efficient image deraining,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.05911","last_updated":"2026-06-04T09:16:26Z","snapshot_observed_at":"2026-08-06T13:48:13.385785Z","submitted_at":"2026-06-04T09:16:26Z","title":"DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-06-27T23:49:05.871422Z"},"links":{"citing_paper":"/paper/2606.05911"},"observation_digest":"sha256:693ee0b8f6fa3e7c0c558e9b91131fff78ee79650734afe8fa798dd32bf4ec3c","observation_id":"818b9622-2738-405c-99e0-fc4ff462f265","resolution":{"observed_at":"2026-06-27T23:49:05.871422Z","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":"2026-06-27T23:49:05.871422Z","title":"Adaptation and learning of spatio-temporal thresholds in spiking neural networks,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.05911","last_updated":"2026-06-04T09:16:26Z","snapshot_observed_at":"2026-08-06T13:48:13.385785Z","submitted_at":"2026-06-04T09:16:26Z","title":"DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-06-27T23:49:05.871422Z"},"links":{"citing_paper":"/paper/2606.05911"},"observation_digest":"sha256:5b0661afef3db4829c615a60a374038d881b989e066e8d0e5ef558a6d379ca13","observation_id":"29a38930-b783-47cc-b22d-d78382adf665","resolution":{"observed_at":"2026-06-27T23:49:05.871422Z","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":"2026-06-27T23:49:05.871422Z","title":"Enhancing representation of spiking neural networks via similarity- sensitive contrastive learning,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.05911","last_updated":"2026-06-04T09:16:26Z","snapshot_observed_at":"2026-08-06T13:48:13.385785Z","submitted_at":"2026-06-04T09:16:26Z","title":"DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-06-27T23:49:05.871422Z"},"links":{"citing_paper":"/paper/2606.05911"},"observation_digest":"sha256:5def8fcc0060236be24371e377c334094d6a00910565f7c9c624e971ddab8969","observation_id":"d00f9c96-8fbf-4714-b7bd-013846c49a49","resolution":{"observed_at":"2026-06-27T23:49:05.871422Z","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":"2026-06-27T23:49:05.871422Z","title":"Spikingbert: Distilling bert to train spiking language models using implicit differentiation,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.05911","last_updated":"2026-06-04T09:16:26Z","snapshot_observed_at":"2026-08-06T13:48:13.385785Z","submitted_at":"2026-06-04T09:16:26Z","title":"DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-06-27T23:49:05.871422Z"},"links":{"citing_paper":"/paper/2606.05911"},"observation_digest":"sha256:a5f54ad147a1e8165555001017f3954a1b92a05b35bc9a916b43d272dcdb88ae","observation_id":"a009f631-620a-4107-94d2-b0806152280d","resolution":{"observed_at":"2026-06-27T23:49:05.871422Z","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":"2026-06-27T23:49:05.871422Z","title":"Tc-lif: A two- compartment spiking neuron model for long-term sequential modelling,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.05911","last_updated":"2026-06-04T09:16:26Z","snapshot_observed_at":"2026-08-06T13:48:13.385785Z","submitted_at":"2026-06-04T09:16:26Z","title":"DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-06-27T23:49:05.871422Z"},"links":{"citing_paper":"/paper/2606.05911"},"observation_digest":"sha256:e938aa6454f7a91bf82c617d8439fce07aa17dd327ab2aea82d97a1b32530bba","observation_id":"356e8e51-4b5d-4706-853f-28f41ec34001","resolution":{"observed_at":"2026-06-27T23:49:05.871422Z","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":"2026-06-27T23:49:05.871422Z","title":"Learning a spiking neural network for efficient image deraining,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.05911","last_updated":"2026-06-04T09:16:26Z","snapshot_observed_at":"2026-08-06T13:48:13.385785Z","submitted_at":"2026-06-04T09:16:26Z","title":"DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-06-27T23:49:05.871422Z"},"links":{"citing_paper":"/paper/2606.05911"},"observation_digest":"sha256:1672c133e9a33ae7c7a34887b7a6caa87b56f6cddacf586a5218156478d7b6b8","observation_id":"e500e48f-645f-4ea0-aae4-86c71071b37d","resolution":{"observed_at":"2026-06-27T23:49:05.871422Z","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":"2026-06-27T23:49:05.871422Z","title":"Spikelm: Towards general spike-driven language modeling via elastic bi-spiking mechanisms,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.05911","last_updated":"2026-06-04T09:16:26Z","snapshot_observed_at":"2026-08-06T13:48:13.385785Z","submitted_at":"2026-06-04T09:16:26Z","title":"DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-06-27T23:49:05.871422Z"},"links":{"citing_paper":"/paper/2606.05911"},"observation_digest":"sha256:da81ddd3d78be0c2d9696ab69e58b7f89c331b2c22fb976cfb62b669784cce48","observation_id":"dc07aed1-6b27-4622-9bbb-91de66462da7","resolution":{"observed_at":"2026-06-27T23:49:05.871422Z","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":"2026-06-27T23:49:05.871422Z","title":"Dpsnn: Spiking neural network for low- latency streaming speech enhancement,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.05911","last_updated":"2026-06-04T09:16:26Z","snapshot_observed_at":"2026-08-06T13:48:13.385785Z","submitted_at":"2026-06-04T09:16:26Z","title":"DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-06-27T23:49:05.871422Z"},"links":{"citing_paper":"/paper/2606.05911"},"observation_digest":"sha256:69c54e795e132791df2b904ae82f79e5db1412adcb5ef8565bfe6b6a36f4ba8d","observation_id":"0089ce88-a8ce-4694-8307-119d35b794fb","resolution":{"observed_at":"2026-06-27T23:49:05.871422Z","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":"2026-06-27T23:49:05.871422Z","title":"Temporally dynamic spiking transformer network for speech enhancement,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.05911","last_updated":"2026-06-04T09:16:26Z","snapshot_observed_at":"2026-08-06T13:48:13.385785Z","submitted_at":"2026-06-04T09:16:26Z","title":"DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-06-27T23:49:05.871422Z"},"links":{"citing_paper":"/paper/2606.05911"},"observation_digest":"sha256:6dac2992b87b0dec647a820693ae08ea66064975d3b471d94645724d79f67523","observation_id":"24e7c898-0edb-409b-804d-b1a638c553f9","resolution":{"observed_at":"2026-06-27T23:49:05.871422Z","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":"2026-06-27T23:49:05.871422Z","title":"Single channel speech enhancement using u-net spiking neural networks,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.05911","last_updated":"2026-06-04T09:16:26Z","snapshot_observed_at":"2026-08-06T13:48:13.385785Z","submitted_at":"2026-06-04T09:16:26Z","title":"DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-06-27T23:49:05.871422Z"},"links":{"citing_paper":"/paper/2606.05911"},"observation_digest":"sha256:3d90b43bcafffeb87a3d41d76892f329ccb19c7b2e19e89d4633e36eb948d345","observation_id":"4f801a00-ddbb-4190-b4f8-b635c88052dc","resolution":{"observed_at":"2026-06-27T23:49:05.871422Z","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":"2026-06-27T23:49:05.871422Z","title":"Gerstner and W","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2606.05911","last_updated":"2026-06-04T09:16:26Z","snapshot_observed_at":"2026-08-06T13:48:13.385785Z","submitted_at":"2026-06-04T09:16:26Z","title":"DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-06-27T23:49:05.871422Z"},"links":{"citing_paper":"/paper/2606.05911"},"observation_digest":"sha256:83b871f6d0c49829104ffa41193908b505d6ab24e1481f0a6c845e7b583f16cb","observation_id":"aa76be42-9f7f-4df0-8f19-95279c39207d","resolution":{"observed_at":"2026-06-27T23:49:05.871422Z","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":"2026-06-27T23:49:05.871422Z","title":"Rmp-loss: Regularizing membrane potential distribution for spiking neural networks,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.05911","last_updated":"2026-06-04T09:16:26Z","snapshot_observed_at":"2026-08-06T13:48:13.385785Z","submitted_at":"2026-06-04T09:16:26Z","title":"DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-06-27T23:49:05.871422Z"},"links":{"citing_paper":"/paper/2606.05911"},"observation_digest":"sha256:7e6327c3da30c531a3ba93e02a53c83a61d993717c3d730542fe04757b35049d","observation_id":"f87d911b-1a11-466f-8d7e-79cb3e4eff1b","resolution":{"observed_at":"2026-06-27T23:49:05.871422Z","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":"2026-06-27T23:49:05.871422Z","title":"The design for the wall street journal-based csr corpus,","venue":null,"work_id":null,"year":1992},"citing_paper":{"arxiv_id":"2606.05911","last_updated":"2026-06-04T09:16:26Z","snapshot_observed_at":"2026-08-06T13:48:13.385785Z","submitted_at":"2026-06-04T09:16:26Z","title":"DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-06-27T23:49:05.871422Z"},"links":{"citing_paper":"/paper/2606.05911"},"observation_digest":"sha256:89a46472c46ade9162bb259238c6fb322e96b16bb7bba61e1c9762965b4f66f2","observation_id":"441486f7-bdad-4c4c-aafe-5f8d807a2ad1","resolution":{"observed_at":"2026-06-27T23:49:05.871422Z","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":"2026-06-27T23:49:05.871422Z","title":"The interspeech 2020 deep noise suppression challenge: Datasets, subjective testing framework, and challenge results,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.05911","last_updated":"2026-06-04T09:16:26Z","snapshot_observed_at":"2026-08-06T13:48:13.385785Z","submitted_at":"2026-06-04T09:16:26Z","title":"DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-06-27T23:49:05.871422Z"},"links":{"citing_paper":"/paper/2606.05911"},"observation_digest":"sha256:d86bc1de3320b86c62241133b8ce81840c77ec17b00419b80b5aa79d9d456709","observation_id":"e5396445-9817-4f5f-83f9-2e0f31eeaac0","resolution":{"observed_at":"2026-06-27T23:49:05.871422Z","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":"2026-06-27T23:49:05.871422Z","title":"Assessment for automatic speech recog- nition: Ii. noisex-92: A database and an experiment to study the effect of additive noise on speech recognition systems,","venue":null,"work_id":null,"year":1993},"citing_paper":{"arxiv_id":"2606.05911","last_updated":"2026-06-04T09:16:26Z","snapshot_observed_at":"2026-08-06T13:48:13.385785Z","submitted_at":"2026-06-04T09:16:26Z","title":"DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-06-27T23:49:05.871422Z"},"links":{"citing_paper":"/paper/2606.05911"},"observation_digest":"sha256:2ec4c3afbf937fd00afc06909f31bbd44f68068f19052d44ce4c0b3f042c378c","observation_id":"5e195b44-08eb-416a-96a0-4f0c2761c0f4","resolution":{"observed_at":"2026-06-27T23:49:05.871422Z","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":"2026-06-27T23:49:05.871422Z","title":"The voice bank corpus: Design, collection and data analysis of a large regional accent speech database,","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2606.05911","last_updated":"2026-06-04T09:16:26Z","snapshot_observed_at":"2026-08-06T13:48:13.385785Z","submitted_at":"2026-06-04T09:16:26Z","title":"DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-06-27T23:49:05.871422Z"},"links":{"citing_paper":"/paper/2606.05911"},"observation_digest":"sha256:c85f95949e85b979ce87d446371ac93f689f96b5b8cc2ffb12e320d641fe78c6","observation_id":"0583c682-0c80-4735-8705-317a45fcaea5","resolution":{"observed_at":"2026-06-27T23:49:05.871422Z","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":"2026-06-27T23:49:05.871422Z","title":"Investigating rnn-based speech enhancement methods for noise-robust text-to-speech,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2606.05911","last_updated":"2026-06-04T09:16:26Z","snapshot_observed_at":"2026-08-06T13:48:13.385785Z","submitted_at":"2026-06-04T09:16:26Z","title":"DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-06-27T23:49:05.871422Z"},"links":{"citing_paper":"/paper/2606.05911"},"observation_digest":"sha256:8dd825e2ae372eaa44515358a2da0fda1b2564a095c3dc4ecc9e8465c22dcc8b","observation_id":"8070a8eb-3a49-4bb3-a568-f6cb6eb44639","resolution":{"observed_at":"2026-06-27T23:49:05.871422Z","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":"2026-06-27T23:49:05.871422Z","title":"On the importance of power compression and phase estimation in monaural speech dereverberation,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.05911","last_updated":"2026-06-04T09:16:26Z","snapshot_observed_at":"2026-08-06T13:48:13.385785Z","submitted_at":"2026-06-04T09:16:26Z","title":"DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-06-27T23:49:05.871422Z"},"links":{"citing_paper":"/paper/2606.05911"},"observation_digest":"sha256:f91d58ab26a5e66248e53f160cd333c7828c279ba7854250139542cad79a21e6","observation_id":"b649d9a7-f917-4b33-aa1d-a82f51891977","resolution":{"observed_at":"2026-06-27T23:49:05.871422Z","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":"2026-06-27T23:49:05.871422Z","title":"Conv-tasnet: Surpassing ideal time– frequency magnitude masking for speech separation,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2606.05911","last_updated":"2026-06-04T09:16:26Z","snapshot_observed_at":"2026-08-06T13:48:13.385785Z","submitted_at":"2026-06-04T09:16:26Z","title":"DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-06-27T23:49:05.871422Z"},"links":{"citing_paper":"/paper/2606.05911"},"observation_digest":"sha256:000e523fe895ef383507c9f44af94632195e366bc263e05fa11bbab7e18c9181","observation_id":"b42f9f18-7454-4d52-92bc-c413c2f3da24","resolution":{"observed_at":"2026-06-27T23:49:05.871422Z","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":"2026-06-27T23:49:05.871422Z","title":"Segan: Speech enhancement generative adversarial network,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2606.05911","last_updated":"2026-06-04T09:16:26Z","snapshot_observed_at":"2026-08-06T13:48:13.385785Z","submitted_at":"2026-06-04T09:16:26Z","title":"DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-06-27T23:49:05.871422Z"},"links":{"citing_paper":"/paper/2606.05911"},"observation_digest":"sha256:939c10c98b9868a242303a7383c26a9345f5585c6bbc765dc82d1dba3b37391b","observation_id":"5549e1fd-c473-4cd0-88d8-a1d80993a241","resolution":{"observed_at":"2026-06-27T23:49:05.871422Z","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":"2026-06-27T23:49:05.871422Z","title":"Time-frequency masking-based speech enhancement using generative adversarial network,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2606.05911","last_updated":"2026-06-04T09:16:26Z","snapshot_observed_at":"2026-08-06T13:48:13.385785Z","submitted_at":"2026-06-04T09:16:26Z","title":"DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-06-27T23:49:05.871422Z"},"links":{"citing_paper":"/paper/2606.05911"},"observation_digest":"sha256:ed9f3e37ec697e9323c96e12f264b8f00a83170a387f2057f4d582f2915cef43","observation_id":"41caa21c-be19-47cc-bb54-5f56702a7802","resolution":{"observed_at":"2026-06-27T23:49:05.871422Z","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":"2026-06-27T23:49:05.871422Z","title":"Metricgan: Generative adversarial networks based black-box metric scores optimization for speech enhancement,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2606.05911","last_updated":"2026-06-04T09:16:26Z","snapshot_observed_at":"2026-08-06T13:48:13.385785Z","submitted_at":"2026-06-04T09:16:26Z","title":"DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-06-27T23:49:05.871422Z"},"links":{"citing_paper":"/paper/2606.05911"},"observation_digest":"sha256:6d2c50aae222bde8da521506e071a4828168a75f07ca298c57b20284b5664403","observation_id":"5f7bd2da-295e-47bb-b934-ca0371b3c0bb","resolution":{"observed_at":"2026-06-27T23:49:05.871422Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1609.03499","last_updated":"2016-09-19T18:04:35Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2016-09-12T17:29:40Z","title":"WaveNet: A Generative Model for Raw Audio","version":2},"cited_work":{"arxiv_id":"1609.03499","doi":"10.48550/arxiv.1609.03499","metadata_source":"pith","pith_arxiv_id":"1609.03499","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"WaveNet: A Generative Model for Raw Audio","venue":"cs.SD","work_id":"05682736-8137-4a5f-a5b6-1127e99b0041","year":2016},"citing_paper":{"arxiv_id":"2606.05911","last_updated":"2026-06-04T09:16:26Z","snapshot_observed_at":"2026-08-06T13:48:13.385785Z","submitted_at":"2026-06-04T09:16:26Z","title":"DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-06-27T23:49:05.871422Z"},"links":{"cited_paper":"/paper/1609.03499","citing_paper":"/paper/2606.05911"},"observation_digest":"sha256:538dcd65fe979e7c94209aad79f8a5feec317ae07542f300e747410ca7656816","observation_id":"7c1da7bf-af2b-4878-8b46-4bb0212a6e74","resolution":{"observed_at":"2026-07-02T15:37:06.105506Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-06-27T23:49:05.871422Z","title":"Srtnet: Time domain speech enhancement via stochastic refinement,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.05911","last_updated":"2026-06-04T09:16:26Z","snapshot_observed_at":"2026-08-06T13:48:13.385785Z","submitted_at":"2026-06-04T09:16:26Z","title":"DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-06-27T23:49:05.871422Z"},"links":{"citing_paper":"/paper/2606.05911"},"observation_digest":"sha256:402bdc8e74862bbb5d8c2262b839155cb9eee42c9597d9184d439cd015ca5d78","observation_id":"fb5eeaf6-c0bb-4a87-98b6-67fd6df1d973","resolution":{"observed_at":"2026-06-27T23:49:05.871422Z","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":"2026-06-27T23:49:05.871422Z","title":"Phasen: A phase-and- harmonics-aware speech enhancement network,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.05911","last_updated":"2026-06-04T09:16:26Z","snapshot_observed_at":"2026-08-06T13:48:13.385785Z","submitted_at":"2026-06-04T09:16:26Z","title":"DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-06-27T23:49:05.871422Z"},"links":{"citing_paper":"/paper/2606.05911"},"observation_digest":"sha256:3731feeecd2f638002ca6e718ad10ce3ad30761324df122dba56a613f79b907c","observation_id":"c76b881e-6196-4f10-8224-53b3f19dcccc","resolution":{"observed_at":"2026-06-27T23:49:05.871422Z","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":"2026-06-27T23:49:05.871422Z","title":"Speech enhancement using self-adaptation and multi-head self- attention,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.05911","last_updated":"2026-06-04T09:16:26Z","snapshot_observed_at":"2026-08-06T13:48:13.385785Z","submitted_at":"2026-06-04T09:16:26Z","title":"DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-06-27T23:49:05.871422Z"},"links":{"citing_paper":"/paper/2606.05911"},"observation_digest":"sha256:8e1ab9e0bb9992b886464aeeac872ff47d6eadab4941f20c7891d77927d1d68d","observation_id":"4e726dcc-9178-4821-a44c-86650ee6968e","resolution":{"observed_at":"2026-06-27T23:49:05.871422Z","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":"2026-06-27T23:49:05.871422Z","title":"Tstnn: Two-stage transformer based neural network for speech enhancement in the time domain,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.05911","last_updated":"2026-06-04T09:16:26Z","snapshot_observed_at":"2026-08-06T13:48:13.385785Z","submitted_at":"2026-06-04T09:16:26Z","title":"DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-06-27T23:49:05.871422Z"},"links":{"citing_paper":"/paper/2606.05911"},"observation_digest":"sha256:0846c54d0f0f90865468f6a690b658ec5d6ff16ba500e855c1830875ccc1b010","observation_id":"622c42ad-37d2-4d49-9155-254f4fdb4977","resolution":{"observed_at":"2026-06-27T23:49:05.871422Z","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":"2026-06-27T23:49:05.871422Z","title":"A multi-dimensional deep structured state space approach to speech enhancement using small- footprint models,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.05911","last_updated":"2026-06-04T09:16:26Z","snapshot_observed_at":"2026-08-06T13:48:13.385785Z","submitted_at":"2026-06-04T09:16:26Z","title":"DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-06-27T23:49:05.871422Z"},"links":{"citing_paper":"/paper/2606.05911"},"observation_digest":"sha256:a2a52bd5cb0a9cb40cd1233f7afd9ff8211e03f8d729e0c368f0472957311592","observation_id":"581301c0-241f-42ab-b64a-86515171514e","resolution":{"observed_at":"2026-06-27T23:49:05.871422Z","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":"2026-06-27T23:49:05.871422Z","title":"A two-stage framework in cross-spectrum domain for real-time speech enhancement,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.05911","last_updated":"2026-06-04T09:16:26Z","snapshot_observed_at":"2026-08-06T13:48:13.385785Z","submitted_at":"2026-06-04T09:16:26Z","title":"DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-06-27T23:49:05.871422Z"},"links":{"citing_paper":"/paper/2606.05911"},"observation_digest":"sha256:4a4e93771686ff30bd3a7268faf56d06fb7f7ba719d0195a83b05b5b607e9700","observation_id":"9be1ffcb-fa57-4522-a358-3e48f0fa7cca","resolution":{"observed_at":"2026-06-27T23:49:05.871422Z","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":"2026-06-27T23:49:05.871422Z","title":"Dual-signal transformation lstm network for real-time noise suppression,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.05911","last_updated":"2026-06-04T09:16:26Z","snapshot_observed_at":"2026-08-06T13:48:13.385785Z","submitted_at":"2026-06-04T09:16:26Z","title":"DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-06-27T23:49:05.871422Z"},"links":{"citing_paper":"/paper/2606.05911"},"observation_digest":"sha256:b926f6f2e7ba5bccb307fc1d39f23f732f454f230b044c9b0f2b4b78aa0c83c5","observation_id":"6f9f9755-9e33-4e62-a5f9-bb973960bad2","resolution":{"observed_at":"2026-06-27T23:49:05.871422Z","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":"2026-06-27T23:49:05.871422Z","title":"Iifc-net: A monaural speech enhancement network with high-order information interaction and fea- ture calibration,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.05911","last_updated":"2026-06-04T09:16:26Z","snapshot_observed_at":"2026-08-06T13:48:13.385785Z","submitted_at":"2026-06-04T09:16:26Z","title":"DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-06-27T23:49:05.871422Z"},"links":{"citing_paper":"/paper/2606.05911"},"observation_digest":"sha256:c1e3661618a64f272955cbcd495f042afcff379dc55e24f159df8524b3ca9056","observation_id":"6e67fa91-34a2-4f11-b333-ff3f879fd63a","resolution":{"observed_at":"2026-06-27T23:49:05.871422Z","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":"2026-06-27T23:49:05.871422Z","title":"A mask free neural network for monaural speech enhancement,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.05911","last_updated":"2026-06-04T09:16:26Z","snapshot_observed_at":"2026-08-06T13:48:13.385785Z","submitted_at":"2026-06-04T09:16:26Z","title":"DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-06-27T23:49:05.871422Z"},"links":{"citing_paper":"/paper/2606.05911"},"observation_digest":"sha256:10fe6e981fb109f4ef0406c77d5a95d7a4598b4d9d523ae9130f923d06a1c1c4","observation_id":"6d869753-4b41-432c-9bb3-b4d149151603","resolution":{"observed_at":"2026-06-27T23:49:05.871422Z","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":"2026-06-27T23:49:05.871422Z","title":"Sicrn: Advancing speech enhancement through state space model and inplace convolution techniques,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.05911","last_updated":"2026-06-04T09:16:26Z","snapshot_observed_at":"2026-08-06T13:48:13.385785Z","submitted_at":"2026-06-04T09:16:26Z","title":"DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-06-27T23:49:05.871422Z"},"links":{"citing_paper":"/paper/2606.05911"},"observation_digest":"sha256:5313b1fe999add1af693e6508078805f200a0feba2c2f00f040ea893b9f0d476","observation_id":"accfa4a0-d5e5-4949-834d-c434ecb6ac24","resolution":{"observed_at":"2026-06-27T23:49:05.871422Z","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":"2026-06-27T23:49:05.871422Z","title":"Exploiting bispectral features for single-channel speech enhancement,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.05911","last_updated":"2026-06-04T09:16:26Z","snapshot_observed_at":"2026-08-06T13:48:13.385785Z","submitted_at":"2026-06-04T09:16:26Z","title":"DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-06-27T23:49:05.871422Z"},"links":{"citing_paper":"/paper/2606.05911"},"observation_digest":"sha256:c538bcb5db13091e971fa3ac423c3f025ff18e39590e62cf34cb99583d1c2573","observation_id":"e7ffd3cc-bf51-486b-9163-7a58fb06716a","resolution":{"observed_at":"2026-06-27T23:49:05.871422Z","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":"2026-06-27T23:49:05.871422Z","title":"Tsdt-net: Ultra- low-complexity two-stage model combining dual-path-transformer and transform-average-concatenate network for speech enhancement,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.05911","last_updated":"2026-06-04T09:16:26Z","snapshot_observed_at":"2026-08-06T13:48:13.385785Z","submitted_at":"2026-06-04T09:16:26Z","title":"DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-06-27T23:49:05.871422Z"},"links":{"citing_paper":"/paper/2606.05911"},"observation_digest":"sha256:1bef1d45e9b92b2efcbf9712fcf5dc7cf35ab4cbcb46a245110b9710a8027e60","observation_id":"c07df0b2-1fa6-44cb-b251-dbcae51eae44","resolution":{"observed_at":"2026-06-27T23:49:05.871422Z","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":"2026-06-27T23:49:05.871422Z","title":"Per- ceptual evaluation of speech quality (pesq)-a new method for speech quality assessment of telephone networks and codecs,","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2606.05911","last_updated":"2026-06-04T09:16:26Z","snapshot_observed_at":"2026-08-06T13:48:13.385785Z","submitted_at":"2026-06-04T09:16:26Z","title":"DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-06-27T23:49:05.871422Z"},"links":{"citing_paper":"/paper/2606.05911"},"observation_digest":"sha256:de7faf3e6ac801407d83170bd02db0456e5ea9ddfda04c6f3e1ade7498e05292","observation_id":"ca4f4f64-17da-48cc-9942-74411d617796","resolution":{"observed_at":"2026-06-27T23:49:05.871422Z","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":"2026-06-27T23:49:05.871422Z","title":"An algorithm for predicting the intelligibility of speech masked by modulated noise maskers,","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2606.05911","last_updated":"2026-06-04T09:16:26Z","snapshot_observed_at":"2026-08-06T13:48:13.385785Z","submitted_at":"2026-06-04T09:16:26Z","title":"DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-06-27T23:49:05.871422Z"},"links":{"citing_paper":"/paper/2606.05911"},"observation_digest":"sha256:933d4c75d13acf9d6e4e2f5aaa966d6667e1f6f57dd252dc17f54ad3a7fa58ab","observation_id":"d3cbeca0-06d4-4069-a4b1-f7eba7f232f6","resolution":{"observed_at":"2026-06-27T23:49:05.871422Z","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":"2026-06-27T23:49:05.871422Z","title":"Evaluation of objective quality measures for speech enhancement,","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2606.05911","last_updated":"2026-06-04T09:16:26Z","snapshot_observed_at":"2026-08-06T13:48:13.385785Z","submitted_at":"2026-06-04T09:16:26Z","title":"DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement","version":1},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-06-27T23:49:05.871422Z"},"links":{"citing_paper":"/paper/2606.05911"},"observation_digest":"sha256:feca6a0e68d7a5fd6fe40f9c9e2a2b7cfc31c1d3f12e7307c6f5776ce5c49a97","observation_id":"1f90533c-00dc-42ad-8d80-3784326b271b","resolution":{"observed_at":"2026-06-27T23:49:05.871422Z","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":"2026-06-27T23:49:05.871422Z","title":"Dccrn+: Channel-wise subband dccrn with snr estimation for speech enhancement,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.05911","last_updated":"2026-06-04T09:16:26Z","snapshot_observed_at":"2026-08-06T13:48:13.385785Z","submitted_at":"2026-06-04T09:16:26Z","title":"DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement","version":1},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-06-27T23:49:05.871422Z"},"links":{"citing_paper":"/paper/2606.05911"},"observation_digest":"sha256:3e68a4b4f03f86eb1dfe4dc1e9e7b7d75a6b6c020a3792bdf06394787b2b50d2","observation_id":"b1f960ef-d0b6-4a5a-aef2-6f3d73cb5231","resolution":{"observed_at":"2026-06-27T23:49:05.871422Z","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":"2026-06-27T23:49:05.871422Z","title":"Densely connected neural network with dilated convolutions for real-time speech enhancement in the time domain,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.05911","last_updated":"2026-06-04T09:16:26Z","snapshot_observed_at":"2026-08-06T13:48:13.385785Z","submitted_at":"2026-06-04T09:16:26Z","title":"DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement","version":1},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-06-27T23:49:05.871422Z"},"links":{"citing_paper":"/paper/2606.05911"},"observation_digest":"sha256:3cb2dc707cc37338bb91b908b6710ef757b6f94d94919ee2eb6230d38c9b7da4","observation_id":"33f5702c-582e-4f25-9843-c1df2806d55b","resolution":{"observed_at":"2026-06-27T23:49:05.871422Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2606.05911","last_updated":"2026-06-04T09:16:26Z","latest_version":1,"primary_category":"cs.SD","snapshot_observed_at":"2026-08-06T13:48:13.385785Z","submitted_at":"2026-06-04T09:16:26Z","title":"DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement"},"reference_resolution":{"displayed":78,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":75,"verified_exact":2,"verified_fuzzy":0},"total_outbound_references":78},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 78 of 78 outbound references and 0 inbound Pith citation observations for arXiv:2606.05911."}