{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:4EIZVKOKKUHJD2QE7RNPPEMOY6","short_pith_number":"pith:4EIZVKOK","canonical_record":{"source":{"id":"2309.02432","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2023-09-05T17:58:58Z","cross_cats_sorted":["cs.SD"],"title_canon_sha256":"caa0c2187ccefc3f65e44ef6ada8de75cac2b17b138acb07ce7b94efbb595b46","abstract_canon_sha256":"8e503ddf0cfa0bce983da3302b2bc6e3540caaab1dd09bb1118adf9177bbba1e"},"schema_version":"1.0"},"canonical_sha256":"e1119aa9ca550e91ea04fc5af7918ec79b20da0b09fbe2dbcd3165fb94c83f4f","source":{"kind":"arxiv","id":"2309.02432","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2309.02432","created_at":"2026-07-05T06:48:00Z"},{"alias_kind":"arxiv_version","alias_value":"2309.02432v1","created_at":"2026-07-05T06:48:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2309.02432","created_at":"2026-07-05T06:48:00Z"},{"alias_kind":"pith_short_12","alias_value":"4EIZVKOKKUHJ","created_at":"2026-07-05T06:48:00Z"},{"alias_kind":"pith_short_16","alias_value":"4EIZVKOKKUHJD2QE","created_at":"2026-07-05T06:48:00Z"},{"alias_kind":"pith_short_8","alias_value":"4EIZVKOK","created_at":"2026-07-05T06:48:00Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:4EIZVKOKKUHJD2QE7RNPPEMOY6","target":"record","payload":{"canonical_record":{"source":{"id":"2309.02432","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2023-09-05T17:58:58Z","cross_cats_sorted":["cs.SD"],"title_canon_sha256":"caa0c2187ccefc3f65e44ef6ada8de75cac2b17b138acb07ce7b94efbb595b46","abstract_canon_sha256":"8e503ddf0cfa0bce983da3302b2bc6e3540caaab1dd09bb1118adf9177bbba1e"},"schema_version":"1.0"},"canonical_sha256":"e1119aa9ca550e91ea04fc5af7918ec79b20da0b09fbe2dbcd3165fb94c83f4f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:48:00.118367Z","signature_b64":"E8N585Kf3qSa13ohj1RTtcFLiiIU3hXvGIDogKMg3AcgBzZhMRciDCAZjGMp6ha/6rnIn29LtsLWGag/Z4cLBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e1119aa9ca550e91ea04fc5af7918ec79b20da0b09fbe2dbcd3165fb94c83f4f","last_reissued_at":"2026-07-05T06:48:00.117989Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:48:00.117989Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2309.02432","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T06:48:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"969cTXnWQ/0vWjxvPunegvhs1vIxOy4kb9IY+mOyx4TmdHeImgibar5LeQEWEu45Tqtyk0Eiifwpf/rpzfcjBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T00:50:27.933818Z"},"content_sha256":"6aff9e81bf82082c4fb58e3877fbcc4628f00c0f73eace70a1cefba7ec63b9c0","schema_version":"1.0","event_id":"sha256:6aff9e81bf82082c4fb58e3877fbcc4628f00c0f73eace70a1cefba7ec63b9c0"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:4EIZVKOKKUHJD2QE7RNPPEMOY6","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Employing Real Training Data for Deep Noise Suppression","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SD"],"primary_cat":"eess.AS","authors_text":"Jan Pirklbauer, Marvin Sach, Tim Fingscheidt, Ziyi Xu","submitted_at":"2023-09-05T17:58:58Z","abstract_excerpt":"Most deep noise suppression (DNS) models are trained with reference-based losses requiring access to clean speech. However, sometimes an additive microphone model is insufficient for real-world applications. Accordingly, ways to use real training data in supervised learning for DNS models promise to reduce a potential training/inference mismatch. Employing real data for DNS training requires either generative approaches or a reference-free loss without access to the corresponding clean speech. In this work, we propose to employ an end-to-end non-intrusive deep neural network (DNN), named PESQ-"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2309.02432","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/2309.02432/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":"2026-07-05T06:48:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"E7/1hzpDm4IYjDGI7Lg2fLJDnrkv4LyrOYPlURwxioPEiFHUFEyPFlmvdpbDLrysq+Plmhg3kqLigwYjX3LUDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T00:50:27.934327Z"},"content_sha256":"ee8bf9936fef31f0d604511a2c1d2ae381fc65f5b5fa76dce9a924ea6b831081","schema_version":"1.0","event_id":"sha256:ee8bf9936fef31f0d604511a2c1d2ae381fc65f5b5fa76dce9a924ea6b831081"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/4EIZVKOKKUHJD2QE7RNPPEMOY6/bundle.json","state_url":"https://pith.science/pith/4EIZVKOKKUHJD2QE7RNPPEMOY6/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/4EIZVKOKKUHJD2QE7RNPPEMOY6/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":"2026-08-11T00:50:27Z","links":{"resolver":"https://pith.science/pith/4EIZVKOKKUHJD2QE7RNPPEMOY6","bundle":"https://pith.science/pith/4EIZVKOKKUHJD2QE7RNPPEMOY6/bundle.json","state":"https://pith.science/pith/4EIZVKOKKUHJD2QE7RNPPEMOY6/state.json","well_known_bundle":"https://pith.science/.well-known/pith/4EIZVKOKKUHJD2QE7RNPPEMOY6/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:4EIZVKOKKUHJD2QE7RNPPEMOY6","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":"8e503ddf0cfa0bce983da3302b2bc6e3540caaab1dd09bb1118adf9177bbba1e","cross_cats_sorted":["cs.SD"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2023-09-05T17:58:58Z","title_canon_sha256":"caa0c2187ccefc3f65e44ef6ada8de75cac2b17b138acb07ce7b94efbb595b46"},"schema_version":"1.0","source":{"id":"2309.02432","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2309.02432","created_at":"2026-07-05T06:48:00Z"},{"alias_kind":"arxiv_version","alias_value":"2309.02432v1","created_at":"2026-07-05T06:48:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2309.02432","created_at":"2026-07-05T06:48:00Z"},{"alias_kind":"pith_short_12","alias_value":"4EIZVKOKKUHJ","created_at":"2026-07-05T06:48:00Z"},{"alias_kind":"pith_short_16","alias_value":"4EIZVKOKKUHJD2QE","created_at":"2026-07-05T06:48:00Z"},{"alias_kind":"pith_short_8","alias_value":"4EIZVKOK","created_at":"2026-07-05T06:48:00Z"}],"graph_snapshots":[{"event_id":"sha256:ee8bf9936fef31f0d604511a2c1d2ae381fc65f5b5fa76dce9a924ea6b831081","target":"graph","created_at":"2026-07-05T06:48:00Z","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/2309.02432/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Most deep noise suppression (DNS) models are trained with reference-based losses requiring access to clean speech. However, sometimes an additive microphone model is insufficient for real-world applications. Accordingly, ways to use real training data in supervised learning for DNS models promise to reduce a potential training/inference mismatch. Employing real data for DNS training requires either generative approaches or a reference-free loss without access to the corresponding clean speech. In this work, we propose to employ an end-to-end non-intrusive deep neural network (DNN), named PESQ-","authors_text":"Jan Pirklbauer, Marvin Sach, Tim Fingscheidt, Ziyi Xu","cross_cats":["cs.SD"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2023-09-05T17:58:58Z","title":"Employing Real Training Data for Deep Noise Suppression"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2309.02432","kind":"arxiv","version":1},"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":"sha256:6aff9e81bf82082c4fb58e3877fbcc4628f00c0f73eace70a1cefba7ec63b9c0","target":"record","created_at":"2026-07-05T06:48:00Z","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":"8e503ddf0cfa0bce983da3302b2bc6e3540caaab1dd09bb1118adf9177bbba1e","cross_cats_sorted":["cs.SD"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2023-09-05T17:58:58Z","title_canon_sha256":"caa0c2187ccefc3f65e44ef6ada8de75cac2b17b138acb07ce7b94efbb595b46"},"schema_version":"1.0","source":{"id":"2309.02432","kind":"arxiv","version":1}},"canonical_sha256":"e1119aa9ca550e91ea04fc5af7918ec79b20da0b09fbe2dbcd3165fb94c83f4f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e1119aa9ca550e91ea04fc5af7918ec79b20da0b09fbe2dbcd3165fb94c83f4f","first_computed_at":"2026-07-05T06:48:00.117989Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:48:00.117989Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"E8N585Kf3qSa13ohj1RTtcFLiiIU3hXvGIDogKMg3AcgBzZhMRciDCAZjGMp6ha/6rnIn29LtsLWGag/Z4cLBA==","signature_status":"signed_v1","signed_at":"2026-07-05T06:48:00.118367Z","signed_message":"canonical_sha256_bytes"},"source_id":"2309.02432","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6aff9e81bf82082c4fb58e3877fbcc4628f00c0f73eace70a1cefba7ec63b9c0","sha256:ee8bf9936fef31f0d604511a2c1d2ae381fc65f5b5fa76dce9a924ea6b831081"],"state_sha256":"bcc5d88a32052be5603cc398c2f1b639e002f522ce7add9c8401f71da86b4af6"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"EOyTJ+nTi8CQUZx4+snpubA1GvF1NI6S5cP0QTbxrixcKAMclJyvAex3avvf0zvFSN1xE2vdwoJH6CyaDvcrAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-11T00:50:27.939327Z","bundle_sha256":"e5859879ac42db1a3e4084aac653a1e82472f12e9787172819904e0505b5ac43"}}