{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:C52BDLP3DOOUYRSI3LHPIRL2EX","short_pith_number":"pith:C52BDLP3","schema_version":"1.0","canonical_sha256":"177411adfb1b9d4c4648dacef4457a25e5f0852d88877909385c24cae4a5b4ac","source":{"kind":"arxiv","id":"2106.08672","version":1},"attestation_state":"computed","paper":{"title":"DCCRN+: Channel-wise Subband DCCRN with SNR Estimation for Speech Enhancement","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SD"],"primary_cat":"eess.AS","authors_text":"Lei Xie, Shimin Zhang, Shubo Lv, Yanxin Hu","submitted_at":"2021-06-16T10:16:35Z","abstract_excerpt":"Deep complex convolution recurrent network (DCCRN), which extends CRN with complex structure, has achieved superior performance in MOS evaluation in Interspeech 2020 deep noise suppression challenge (DNS2020). This paper further extends DCCRN with the following significant revisions. We first extend the model to sub-band processing where the bands are split and merged by learnable neural network filters instead of engineered FIR filters, leading to a faster noise suppressor trained in an end-to-end manner. Then the LSTM is further substituted with a complex TF-LSTM to better model temporal dep"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2106.08672","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2021-06-16T10:16:35Z","cross_cats_sorted":["cs.SD"],"title_canon_sha256":"a9d806416d93aef4cb2b2c25c732f14a19e010a739c4c3d5878783f88735c5af","abstract_canon_sha256":"73683e3a8c4b6edb639352830018c84e42cc0e75176b6477f9c89d2473ce1dd3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:50:00.328396Z","signature_b64":"3qylYiK1KgQsmK4ZnurRPtH48mDDN45OG+gHMhAKKtRW9bH/jgk0barGiOjYENaGVW2/6df3pgsjcecrbLFwDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"177411adfb1b9d4c4648dacef4457a25e5f0852d88877909385c24cae4a5b4ac","last_reissued_at":"2026-07-05T02:50:00.327801Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:50:00.327801Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"DCCRN+: Channel-wise Subband DCCRN with SNR Estimation for Speech Enhancement","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SD"],"primary_cat":"eess.AS","authors_text":"Lei Xie, Shimin Zhang, Shubo Lv, Yanxin Hu","submitted_at":"2021-06-16T10:16:35Z","abstract_excerpt":"Deep complex convolution recurrent network (DCCRN), which extends CRN with complex structure, has achieved superior performance in MOS evaluation in Interspeech 2020 deep noise suppression challenge (DNS2020). This paper further extends DCCRN with the following significant revisions. We first extend the model to sub-band processing where the bands are split and merged by learnable neural network filters instead of engineered FIR filters, leading to a faster noise suppressor trained in an end-to-end manner. Then the LSTM is further substituted with a complex TF-LSTM to better model temporal dep"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2106.08672","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2106.08672/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2106.08672","created_at":"2026-07-05T02:50:00.327868+00:00"},{"alias_kind":"arxiv_version","alias_value":"2106.08672v1","created_at":"2026-07-05T02:50:00.327868+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2106.08672","created_at":"2026-07-05T02:50:00.327868+00:00"},{"alias_kind":"pith_short_12","alias_value":"C52BDLP3DOOU","created_at":"2026-07-05T02:50:00.327868+00:00"},{"alias_kind":"pith_short_16","alias_value":"C52BDLP3DOOUYRSI","created_at":"2026-07-05T02:50:00.327868+00:00"},{"alias_kind":"pith_short_8","alias_value":"C52BDLP3","created_at":"2026-07-05T02:50:00.327868+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2602.10666","citing_title":"From Diet to Free Lunch: Estimating Auxiliary Signal Properties using Dynamic Pruning Masks in Speech Enhancement Networks","ref_index":11,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/C52BDLP3DOOUYRSI3LHPIRL2EX","json":"https://pith.science/pith/C52BDLP3DOOUYRSI3LHPIRL2EX.json","graph_json":"https://pith.science/api/pith-number/C52BDLP3DOOUYRSI3LHPIRL2EX/graph.json","events_json":"https://pith.science/api/pith-number/C52BDLP3DOOUYRSI3LHPIRL2EX/events.json","paper":"https://pith.science/paper/C52BDLP3"},"agent_actions":{"view_html":"https://pith.science/pith/C52BDLP3DOOUYRSI3LHPIRL2EX","download_json":"https://pith.science/pith/C52BDLP3DOOUYRSI3LHPIRL2EX.json","view_paper":"https://pith.science/paper/C52BDLP3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2106.08672&json=true","fetch_graph":"https://pith.science/api/pith-number/C52BDLP3DOOUYRSI3LHPIRL2EX/graph.json","fetch_events":"https://pith.science/api/pith-number/C52BDLP3DOOUYRSI3LHPIRL2EX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/C52BDLP3DOOUYRSI3LHPIRL2EX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/C52BDLP3DOOUYRSI3LHPIRL2EX/action/storage_attestation","attest_author":"https://pith.science/pith/C52BDLP3DOOUYRSI3LHPIRL2EX/action/author_attestation","sign_citation":"https://pith.science/pith/C52BDLP3DOOUYRSI3LHPIRL2EX/action/citation_signature","submit_replication":"https://pith.science/pith/C52BDLP3DOOUYRSI3LHPIRL2EX/action/replication_record"}},"created_at":"2026-07-05T02:50:00.327868+00:00","updated_at":"2026-07-05T02:50:00.327868+00:00"}