{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:4FLJPMAL4LLHIOXRNSNPBXDKJC","short_pith_number":"pith:4FLJPMAL","canonical_record":{"source":{"id":"2107.05859","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2021-07-13T05:44:47Z","cross_cats_sorted":["cs.SD"],"title_canon_sha256":"76ebe2b5ef45bb316eb3bee649e10d74f16d9dc881496377b2f28b5529cc8387","abstract_canon_sha256":"30609c31ece6c732886161d105912e0e52f45b2a3800bcc621e1946c99fcc20e"},"schema_version":"1.0"},"canonical_sha256":"e15697b00be2d6743af16c9af0dc6a48a95563e05955a4e125a5c3e0eb308429","source":{"kind":"arxiv","id":"2107.05859","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2107.05859","created_at":"2026-07-05T02:57:29Z"},{"alias_kind":"arxiv_version","alias_value":"2107.05859v1","created_at":"2026-07-05T02:57:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2107.05859","created_at":"2026-07-05T02:57:29Z"},{"alias_kind":"pith_short_12","alias_value":"4FLJPMAL4LLH","created_at":"2026-07-05T02:57:29Z"},{"alias_kind":"pith_short_16","alias_value":"4FLJPMAL4LLHIOXR","created_at":"2026-07-05T02:57:29Z"},{"alias_kind":"pith_short_8","alias_value":"4FLJPMAL","created_at":"2026-07-05T02:57:29Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:4FLJPMAL4LLHIOXRNSNPBXDKJC","target":"record","payload":{"canonical_record":{"source":{"id":"2107.05859","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2021-07-13T05:44:47Z","cross_cats_sorted":["cs.SD"],"title_canon_sha256":"76ebe2b5ef45bb316eb3bee649e10d74f16d9dc881496377b2f28b5529cc8387","abstract_canon_sha256":"30609c31ece6c732886161d105912e0e52f45b2a3800bcc621e1946c99fcc20e"},"schema_version":"1.0"},"canonical_sha256":"e15697b00be2d6743af16c9af0dc6a48a95563e05955a4e125a5c3e0eb308429","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:57:29.049014Z","signature_b64":"W5TV8kAE6w8baJNSrw+poJlGB/v4+h55YuI6ZNs+42xTnoBtNIRaP80EJFjKZCDD+L+9FbCfTLP0A74L71AWBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e15697b00be2d6743af16c9af0dc6a48a95563e05955a4e125a5c3e0eb308429","last_reissued_at":"2026-07-05T02:57:29.048601Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:57:29.048601Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2107.05859","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":"2026-07-05T02:57:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FTxtrfUDaKVwJqH2bNWUL4ziGt7pFUTeqMLZAb/w0fXHT9kSubksuVYdh0VHz0E24NHl/gsFlrDBIRVm7eHMCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-19T10:30:07.257660Z"},"content_sha256":"8243e90c81b9af56c3ee1c410622b594a23b9fb24ed34b9b7b20c314b2d61586","schema_version":"1.0","event_id":"sha256:8243e90c81b9af56c3ee1c410622b594a23b9fb24ed34b9b7b20c314b2d61586"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:4FLJPMAL4LLHIOXRNSNPBXDKJC","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"AUC Optimization for Robust Small-footprint Keyword Spotting with Limited Training Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SD"],"primary_cat":"eess.AS","authors_text":"Chengdong Liang, Menglong Xu, Shengqiang Li, Xiao-Lei Zhang","submitted_at":"2021-07-13T05:44:47Z","abstract_excerpt":"Deep neural networks provide effective solutions to small-footprint keyword spotting (KWS). However, if training data is limited, it remains challenging to achieve robust and highly accurate KWS in real-world scenarios where unseen sounds that are out of the training data are frequently encountered. Most conventional methods aim to maximize the classification accuracy on the training set, without taking the unseen sounds into account. To enhance the robustness of the deep neural networks based KWS, in this paper, we introduce a new loss function, named the maximization of the area under the re"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2107.05859","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/2107.05859/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":"2026-07-05T02:57:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Jq55yUi0utATK//Ptm4kiWhczxzwzCVvDMu7tkQnapzh5HXaIG/jHvug29rxtO7gJijzWpCzDz3j9Ho3ffNZBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-19T10:30:07.258221Z"},"content_sha256":"546d17cf2e76ccb050b235e34479ac48e3a06e5afda3240856ef7b9ccf1636b6","schema_version":"1.0","event_id":"sha256:546d17cf2e76ccb050b235e34479ac48e3a06e5afda3240856ef7b9ccf1636b6"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/4FLJPMAL4LLHIOXRNSNPBXDKJC/bundle.json","state_url":"https://pith.science/pith/4FLJPMAL4LLHIOXRNSNPBXDKJC/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/4FLJPMAL4LLHIOXRNSNPBXDKJC/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":"2026-08-19T10:30:07Z","links":{"resolver":"https://pith.science/pith/4FLJPMAL4LLHIOXRNSNPBXDKJC","bundle":"https://pith.science/pith/4FLJPMAL4LLHIOXRNSNPBXDKJC/bundle.json","state":"https://pith.science/pith/4FLJPMAL4LLHIOXRNSNPBXDKJC/state.json","well_known_bundle":"https://pith.science/.well-known/pith/4FLJPMAL4LLHIOXRNSNPBXDKJC/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:4FLJPMAL4LLHIOXRNSNPBXDKJC","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":"30609c31ece6c732886161d105912e0e52f45b2a3800bcc621e1946c99fcc20e","cross_cats_sorted":["cs.SD"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2021-07-13T05:44:47Z","title_canon_sha256":"76ebe2b5ef45bb316eb3bee649e10d74f16d9dc881496377b2f28b5529cc8387"},"schema_version":"1.0","source":{"id":"2107.05859","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2107.05859","created_at":"2026-07-05T02:57:29Z"},{"alias_kind":"arxiv_version","alias_value":"2107.05859v1","created_at":"2026-07-05T02:57:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2107.05859","created_at":"2026-07-05T02:57:29Z"},{"alias_kind":"pith_short_12","alias_value":"4FLJPMAL4LLH","created_at":"2026-07-05T02:57:29Z"},{"alias_kind":"pith_short_16","alias_value":"4FLJPMAL4LLHIOXR","created_at":"2026-07-05T02:57:29Z"},{"alias_kind":"pith_short_8","alias_value":"4FLJPMAL","created_at":"2026-07-05T02:57:29Z"}],"graph_snapshots":[{"event_id":"sha256:546d17cf2e76ccb050b235e34479ac48e3a06e5afda3240856ef7b9ccf1636b6","target":"graph","created_at":"2026-07-05T02:57:29Z","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/2107.05859/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Deep neural networks provide effective solutions to small-footprint keyword spotting (KWS). However, if training data is limited, it remains challenging to achieve robust and highly accurate KWS in real-world scenarios where unseen sounds that are out of the training data are frequently encountered. Most conventional methods aim to maximize the classification accuracy on the training set, without taking the unseen sounds into account. To enhance the robustness of the deep neural networks based KWS, in this paper, we introduce a new loss function, named the maximization of the area under the re","authors_text":"Chengdong Liang, Menglong Xu, Shengqiang Li, Xiao-Lei Zhang","cross_cats":["cs.SD"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2021-07-13T05:44:47Z","title":"AUC Optimization for Robust Small-footprint Keyword Spotting with Limited Training Data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2107.05859","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":"sha256:8243e90c81b9af56c3ee1c410622b594a23b9fb24ed34b9b7b20c314b2d61586","target":"record","created_at":"2026-07-05T02:57:29Z","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":"30609c31ece6c732886161d105912e0e52f45b2a3800bcc621e1946c99fcc20e","cross_cats_sorted":["cs.SD"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2021-07-13T05:44:47Z","title_canon_sha256":"76ebe2b5ef45bb316eb3bee649e10d74f16d9dc881496377b2f28b5529cc8387"},"schema_version":"1.0","source":{"id":"2107.05859","kind":"arxiv","version":1}},"canonical_sha256":"e15697b00be2d6743af16c9af0dc6a48a95563e05955a4e125a5c3e0eb308429","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e15697b00be2d6743af16c9af0dc6a48a95563e05955a4e125a5c3e0eb308429","first_computed_at":"2026-07-05T02:57:29.048601Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:57:29.048601Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"W5TV8kAE6w8baJNSrw+poJlGB/v4+h55YuI6ZNs+42xTnoBtNIRaP80EJFjKZCDD+L+9FbCfTLP0A74L71AWBg==","signature_status":"signed_v1","signed_at":"2026-07-05T02:57:29.049014Z","signed_message":"canonical_sha256_bytes"},"source_id":"2107.05859","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8243e90c81b9af56c3ee1c410622b594a23b9fb24ed34b9b7b20c314b2d61586","sha256:546d17cf2e76ccb050b235e34479ac48e3a06e5afda3240856ef7b9ccf1636b6"],"state_sha256":"a41ce13000cfb7cb04e3a0ce2fdd19b1c926b0b4b5e265304a756d696132a0c6"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"YZMNL9Ak/fNuUzrPG1YrPuiAgFUl/0bodrp1s4q6+MKCV60lEt5oB/n2cya/d4z0nwKAdhUnKux0J6z1LDlBBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-19T10:30:07.262174Z","bundle_sha256":"192dd3c0a5828f215e977bf446d38e04b5bde6ccc99260a9d2d2fb51b7c88be5"}}