{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:MFBRF2D7OT4LU5J64ONF4WXPN4","short_pith_number":"pith:MFBRF2D7","canonical_record":{"source":{"id":"1805.01357","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2018-05-02T06:06:24Z","cross_cats_sorted":["cs.LG","eess.AS"],"title_canon_sha256":"43b41ca69fb81cbce1325cf56bed7260fd035d5cfc16212b07e484a45ea389fa","abstract_canon_sha256":"d1e77c968d5ed9ce63ff1b60b6b178405574784b8b4956d55bd2db9580f34d5d"},"schema_version":"1.0"},"canonical_sha256":"614312e87f74f8ba753ee39a5e5aef6f17c17d8374c66d96f25b64c6dc7b39f5","source":{"kind":"arxiv","id":"1805.01357","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1805.01357","created_at":"2026-05-18T00:16:51Z"},{"alias_kind":"arxiv_version","alias_value":"1805.01357v1","created_at":"2026-05-18T00:16:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.01357","created_at":"2026-05-18T00:16:51Z"},{"alias_kind":"pith_short_12","alias_value":"MFBRF2D7OT4L","created_at":"2026-05-18T12:32:37Z"},{"alias_kind":"pith_short_16","alias_value":"MFBRF2D7OT4LU5J6","created_at":"2026-05-18T12:32:37Z"},{"alias_kind":"pith_short_8","alias_value":"MFBRF2D7","created_at":"2026-05-18T12:32:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:MFBRF2D7OT4LU5J64ONF4WXPN4","target":"record","payload":{"canonical_record":{"source":{"id":"1805.01357","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2018-05-02T06:06:24Z","cross_cats_sorted":["cs.LG","eess.AS"],"title_canon_sha256":"43b41ca69fb81cbce1325cf56bed7260fd035d5cfc16212b07e484a45ea389fa","abstract_canon_sha256":"d1e77c968d5ed9ce63ff1b60b6b178405574784b8b4956d55bd2db9580f34d5d"},"schema_version":"1.0"},"canonical_sha256":"614312e87f74f8ba753ee39a5e5aef6f17c17d8374c66d96f25b64c6dc7b39f5","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:16:51.615971Z","signature_b64":"iIHYS5TrTqNNVGeKK+n78jVBbURsBekiHzhdb8taelPyI20ccTWNNguZUXAp9zeyIbTPuqZSgDTxXNdwG4/ABQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"614312e87f74f8ba753ee39a5e5aef6f17c17d8374c66d96f25b64c6dc7b39f5","last_reissued_at":"2026-05-18T00:16:51.615184Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:16:51.615184Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1805.01357","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":"2026-05-18T00:16:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+B1ll7ItIavqp8VNo9nVNXA3B48fTe+wj+v2xM6LWoDMnF0xrmz/QTtKUSViKDaINzeSCcZo8j0N9+lbuNjSCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T21:21:53.852068Z"},"content_sha256":"4a60c2f581306c85ecdb5826e0e83fc32b894cf2f3486eb3910451f1eabec0e6","schema_version":"1.0","event_id":"sha256:4a60c2f581306c85ecdb5826e0e83fc32b894cf2f3486eb3910451f1eabec0e6"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:MFBRF2D7OT4LU5J64ONF4WXPN4","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Boosting Noise Robustness of Acoustic Model via Deep Adversarial Training","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","eess.AS"],"primary_cat":"cs.SD","authors_text":"Bin Liu, Dengfeng Ke, Shan Liang, Shuai Nie, Wenju Liu1, Yaping Zhang","submitted_at":"2018-05-02T06:06:24Z","abstract_excerpt":"In realistic environments, speech is usually interfered by various noise and reverberation, which dramatically degrades the performance of automatic speech recognition (ASR) systems. To alleviate this issue, the commonest way is to use a well-designed speech enhancement approach as the front-end of ASR. However, more complex pipelines, more computations and even higher hardware costs (microphone array) are additionally consumed for this kind of methods. In addition, speech enhancement would result in speech distortions and mismatches to training. In this paper, we propose an adversarial traini"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.01357","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":""},"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":"2026-05-18T00:16:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"C57a8iN8avIiQvBjPb6orzXgN8M0YUuUa4ruExiQ5SZF8yeyDD2xF6lf8slt0d8Vm3uqvPg8HvZztwmeA2tWBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T21:21:53.852759Z"},"content_sha256":"adbb171add67484a4fae096932776adb6e15549aadab277a91d0d12f0415490c","schema_version":"1.0","event_id":"sha256:adbb171add67484a4fae096932776adb6e15549aadab277a91d0d12f0415490c"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/MFBRF2D7OT4LU5J64ONF4WXPN4/bundle.json","state_url":"https://pith.science/pith/MFBRF2D7OT4LU5J64ONF4WXPN4/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/MFBRF2D7OT4LU5J64ONF4WXPN4/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":"2026-05-25T21:21:53Z","links":{"resolver":"https://pith.science/pith/MFBRF2D7OT4LU5J64ONF4WXPN4","bundle":"https://pith.science/pith/MFBRF2D7OT4LU5J64ONF4WXPN4/bundle.json","state":"https://pith.science/pith/MFBRF2D7OT4LU5J64ONF4WXPN4/state.json","well_known_bundle":"https://pith.science/.well-known/pith/MFBRF2D7OT4LU5J64ONF4WXPN4/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:MFBRF2D7OT4LU5J64ONF4WXPN4","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":"d1e77c968d5ed9ce63ff1b60b6b178405574784b8b4956d55bd2db9580f34d5d","cross_cats_sorted":["cs.LG","eess.AS"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2018-05-02T06:06:24Z","title_canon_sha256":"43b41ca69fb81cbce1325cf56bed7260fd035d5cfc16212b07e484a45ea389fa"},"schema_version":"1.0","source":{"id":"1805.01357","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1805.01357","created_at":"2026-05-18T00:16:51Z"},{"alias_kind":"arxiv_version","alias_value":"1805.01357v1","created_at":"2026-05-18T00:16:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.01357","created_at":"2026-05-18T00:16:51Z"},{"alias_kind":"pith_short_12","alias_value":"MFBRF2D7OT4L","created_at":"2026-05-18T12:32:37Z"},{"alias_kind":"pith_short_16","alias_value":"MFBRF2D7OT4LU5J6","created_at":"2026-05-18T12:32:37Z"},{"alias_kind":"pith_short_8","alias_value":"MFBRF2D7","created_at":"2026-05-18T12:32:37Z"}],"graph_snapshots":[{"event_id":"sha256:adbb171add67484a4fae096932776adb6e15549aadab277a91d0d12f0415490c","target":"graph","created_at":"2026-05-18T00:16:51Z","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"},"paper":{"abstract_excerpt":"In realistic environments, speech is usually interfered by various noise and reverberation, which dramatically degrades the performance of automatic speech recognition (ASR) systems. To alleviate this issue, the commonest way is to use a well-designed speech enhancement approach as the front-end of ASR. However, more complex pipelines, more computations and even higher hardware costs (microphone array) are additionally consumed for this kind of methods. In addition, speech enhancement would result in speech distortions and mismatches to training. In this paper, we propose an adversarial traini","authors_text":"Bin Liu, Dengfeng Ke, Shan Liang, Shuai Nie, Wenju Liu1, Yaping Zhang","cross_cats":["cs.LG","eess.AS"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2018-05-02T06:06:24Z","title":"Boosting Noise Robustness of Acoustic Model via Deep Adversarial Training"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.01357","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":"sha256:4a60c2f581306c85ecdb5826e0e83fc32b894cf2f3486eb3910451f1eabec0e6","target":"record","created_at":"2026-05-18T00:16:51Z","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":"d1e77c968d5ed9ce63ff1b60b6b178405574784b8b4956d55bd2db9580f34d5d","cross_cats_sorted":["cs.LG","eess.AS"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2018-05-02T06:06:24Z","title_canon_sha256":"43b41ca69fb81cbce1325cf56bed7260fd035d5cfc16212b07e484a45ea389fa"},"schema_version":"1.0","source":{"id":"1805.01357","kind":"arxiv","version":1}},"canonical_sha256":"614312e87f74f8ba753ee39a5e5aef6f17c17d8374c66d96f25b64c6dc7b39f5","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"614312e87f74f8ba753ee39a5e5aef6f17c17d8374c66d96f25b64c6dc7b39f5","first_computed_at":"2026-05-18T00:16:51.615184Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:16:51.615184Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"iIHYS5TrTqNNVGeKK+n78jVBbURsBekiHzhdb8taelPyI20ccTWNNguZUXAp9zeyIbTPuqZSgDTxXNdwG4/ABQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:16:51.615971Z","signed_message":"canonical_sha256_bytes"},"source_id":"1805.01357","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4a60c2f581306c85ecdb5826e0e83fc32b894cf2f3486eb3910451f1eabec0e6","sha256:adbb171add67484a4fae096932776adb6e15549aadab277a91d0d12f0415490c"],"state_sha256":"c0858d319221d53e7caf4d437662e9175c3989365e3a388f8ae8bd485694f3f8"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CQlY+o0RMXkxPKDBj7FHtl5AeTLqKCh36r9jvQmUiAtl7Dg7Qsb3fdtWG5pqtNugddIqaHldvO6IAqEzarUrAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T21:21:53.856492Z","bundle_sha256":"262bc70415939374daaaddde583a47d9874a263941fdddf5359a110d8230f1c6"}}