{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:SI435TWOMVGL36NJ5OAC4ZALN5","short_pith_number":"pith:SI435TWO","canonical_record":{"source":{"id":"2310.08981","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2023-10-13T09:57:09Z","cross_cats_sorted":["cs.MM","eess.AS"],"title_canon_sha256":"d4b6cc54dae33a6876f512d02aaa25071f8e59c080b32b567e291dd5b10ab323","abstract_canon_sha256":"e76ac2c7d1a244af31b5f982445168ac92a102959e67f4b4f06b8781bbfd6634"},"schema_version":"1.0"},"canonical_sha256":"9239becece654cbdf9a9eb802e640b6f5b4ed9e353e6cd520d7097c5fc0d0f9f","source":{"kind":"arxiv","id":"2310.08981","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.08981","created_at":"2026-07-05T07:36:24Z"},{"alias_kind":"arxiv_version","alias_value":"2310.08981v3","created_at":"2026-07-05T07:36:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.08981","created_at":"2026-07-05T07:36:24Z"},{"alias_kind":"pith_short_12","alias_value":"SI435TWOMVGL","created_at":"2026-07-05T07:36:24Z"},{"alias_kind":"pith_short_16","alias_value":"SI435TWOMVGL36NJ","created_at":"2026-07-05T07:36:24Z"},{"alias_kind":"pith_short_8","alias_value":"SI435TWO","created_at":"2026-07-05T07:36:24Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:SI435TWOMVGL36NJ5OAC4ZALN5","target":"record","payload":{"canonical_record":{"source":{"id":"2310.08981","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2023-10-13T09:57:09Z","cross_cats_sorted":["cs.MM","eess.AS"],"title_canon_sha256":"d4b6cc54dae33a6876f512d02aaa25071f8e59c080b32b567e291dd5b10ab323","abstract_canon_sha256":"e76ac2c7d1a244af31b5f982445168ac92a102959e67f4b4f06b8781bbfd6634"},"schema_version":"1.0"},"canonical_sha256":"9239becece654cbdf9a9eb802e640b6f5b4ed9e353e6cd520d7097c5fc0d0f9f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:36:24.348969Z","signature_b64":"Rv/rLBrBefhkHf7arqBFYZUnyONWK7Lk+ouF5HwJxWk/7iEWdK0D+rU78kvBLtWQjz6halMSQp9ElkJdJrtsAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9239becece654cbdf9a9eb802e640b6f5b4ed9e353e6cd520d7097c5fc0d0f9f","last_reissued_at":"2026-07-05T07:36:24.348560Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:36:24.348560Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2310.08981","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:36:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CUrl+/YlXsc7tiKuKOJk4MpMuqq6PKG3T8K9I3jCI73TLue5M2IcH51XTBzT1fmv+fyP9lJSiY639PmdmMstBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T09:02:35.910647Z"},"content_sha256":"c984a2f80581c18246730c0016d2946298f7f084e19a5fa05cc2f24954ae3d4e","schema_version":"1.0","event_id":"sha256:c984a2f80581c18246730c0016d2946298f7f084e19a5fa05cc2f24954ae3d4e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:SI435TWOMVGL36NJ5OAC4ZALN5","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Low-latency Speech Enhancement via Speech Token Generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.MM","eess.AS"],"primary_cat":"cs.SD","authors_text":"Huaying Xue, Xiulian Peng, Yan Lu","submitted_at":"2023-10-13T09:57:09Z","abstract_excerpt":"Existing deep learning based speech enhancement mainly employ a data-driven approach, which leverage large amounts of data with a variety of noise types to achieve noise removal from noisy signal. However, the high dependence on the data limits its generalization on the unseen complex noises in real-life environment. In this paper, we focus on the low-latency scenario and regard speech enhancement as a speech generation problem conditioned on the noisy signal, where we generate clean speech instead of identifying and removing noises. Specifically, we propose a conditional generative framework "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.08981","kind":"arxiv","version":3},"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/2310.08981/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:36:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dL/4X/cXac3Wry34sSVKzu9lV0WExhZGHRQEl97a/ul4oU+us2LEsutRW/I4jG13qk1zUCiMuvN2XO14aq20Aw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T09:02:35.911014Z"},"content_sha256":"70f89af66204fb20ec64b3b8bd0340596c4dbe42f8eb56fa992165772425318a","schema_version":"1.0","event_id":"sha256:70f89af66204fb20ec64b3b8bd0340596c4dbe42f8eb56fa992165772425318a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/SI435TWOMVGL36NJ5OAC4ZALN5/bundle.json","state_url":"https://pith.science/pith/SI435TWOMVGL36NJ5OAC4ZALN5/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/SI435TWOMVGL36NJ5OAC4ZALN5/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-07-07T09:02:35Z","links":{"resolver":"https://pith.science/pith/SI435TWOMVGL36NJ5OAC4ZALN5","bundle":"https://pith.science/pith/SI435TWOMVGL36NJ5OAC4ZALN5/bundle.json","state":"https://pith.science/pith/SI435TWOMVGL36NJ5OAC4ZALN5/state.json","well_known_bundle":"https://pith.science/.well-known/pith/SI435TWOMVGL36NJ5OAC4ZALN5/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:SI435TWOMVGL36NJ5OAC4ZALN5","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"e76ac2c7d1a244af31b5f982445168ac92a102959e67f4b4f06b8781bbfd6634","cross_cats_sorted":["cs.MM","eess.AS"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2023-10-13T09:57:09Z","title_canon_sha256":"d4b6cc54dae33a6876f512d02aaa25071f8e59c080b32b567e291dd5b10ab323"},"schema_version":"1.0","source":{"id":"2310.08981","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.08981","created_at":"2026-07-05T07:36:24Z"},{"alias_kind":"arxiv_version","alias_value":"2310.08981v3","created_at":"2026-07-05T07:36:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.08981","created_at":"2026-07-05T07:36:24Z"},{"alias_kind":"pith_short_12","alias_value":"SI435TWOMVGL","created_at":"2026-07-05T07:36:24Z"},{"alias_kind":"pith_short_16","alias_value":"SI435TWOMVGL36NJ","created_at":"2026-07-05T07:36:24Z"},{"alias_kind":"pith_short_8","alias_value":"SI435TWO","created_at":"2026-07-05T07:36:24Z"}],"graph_snapshots":[{"event_id":"sha256:70f89af66204fb20ec64b3b8bd0340596c4dbe42f8eb56fa992165772425318a","target":"graph","created_at":"2026-07-05T07:36:24Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2310.08981/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Existing deep learning based speech enhancement mainly employ a data-driven approach, which leverage large amounts of data with a variety of noise types to achieve noise removal from noisy signal. However, the high dependence on the data limits its generalization on the unseen complex noises in real-life environment. In this paper, we focus on the low-latency scenario and regard speech enhancement as a speech generation problem conditioned on the noisy signal, where we generate clean speech instead of identifying and removing noises. Specifically, we propose a conditional generative framework ","authors_text":"Huaying Xue, Xiulian Peng, Yan Lu","cross_cats":["cs.MM","eess.AS"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2023-10-13T09:57:09Z","title":"Low-latency Speech Enhancement via Speech Token Generation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.08981","kind":"arxiv","version":3},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:c984a2f80581c18246730c0016d2946298f7f084e19a5fa05cc2f24954ae3d4e","target":"record","created_at":"2026-07-05T07:36:24Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"e76ac2c7d1a244af31b5f982445168ac92a102959e67f4b4f06b8781bbfd6634","cross_cats_sorted":["cs.MM","eess.AS"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2023-10-13T09:57:09Z","title_canon_sha256":"d4b6cc54dae33a6876f512d02aaa25071f8e59c080b32b567e291dd5b10ab323"},"schema_version":"1.0","source":{"id":"2310.08981","kind":"arxiv","version":3}},"canonical_sha256":"9239becece654cbdf9a9eb802e640b6f5b4ed9e353e6cd520d7097c5fc0d0f9f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9239becece654cbdf9a9eb802e640b6f5b4ed9e353e6cd520d7097c5fc0d0f9f","first_computed_at":"2026-07-05T07:36:24.348560Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:36:24.348560Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Rv/rLBrBefhkHf7arqBFYZUnyONWK7Lk+ouF5HwJxWk/7iEWdK0D+rU78kvBLtWQjz6halMSQp9ElkJdJrtsAA==","signature_status":"signed_v1","signed_at":"2026-07-05T07:36:24.348969Z","signed_message":"canonical_sha256_bytes"},"source_id":"2310.08981","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c984a2f80581c18246730c0016d2946298f7f084e19a5fa05cc2f24954ae3d4e","sha256:70f89af66204fb20ec64b3b8bd0340596c4dbe42f8eb56fa992165772425318a"],"state_sha256":"52f8314ba17175530835d08e6fbb157d947c95fa7296c535ed4ce42496b84cdd"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Es9J4vz9DCIQygVs69u9UyFGB9mHpCKCfrQt5HBahyJRAH+qdkZ8vGyROf1i9AcofsDgBKmsxRfFVJBCIPeuAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T09:02:35.913056Z","bundle_sha256":"ecb5c92465b93932ff5002a1cd03f30f55e1e08b104cd17ab01b783465df313f"}}